Detection of self-paced reaching movement intention from EEG signals
Identifieur interne : 001E24 ( Pmc/Curation ); précédent : 001E23; suivant : 001E25Detection of self-paced reaching movement intention from EEG signals
Auteurs : Eileen Lew [Suisse] ; Ricardo Chavarriaga [Suisse] ; Stefano Silvoni [Italie] ; José Del R. Millán [Suisse]Source :
- Frontiers in Neuroengineering [ 1662-6443 ] ; 2012.
Abstract
Future neuroprosthetic devices, in particular upper limb, will require decoding and
executing not only the user's intended movement type, but also
Url:
DOI: 10.3389/fneng.2012.00013
PubMed: 23055968
PubMed Central: 3458432
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Detection of self-paced reaching movement intention from EEG
signals</title><author><name sortKey="Lew, Eileen" sort="Lew, Eileen" uniqKey="Lew E" first="Eileen" last="Lew">Eileen Lew</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering</institution><country>Ecole Polytechnique Fédérale de Lausanne, Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Chavarriaga, Ricardo" sort="Chavarriaga, Ricardo" uniqKey="Chavarriaga R" first="Ricardo" last="Chavarriaga">Ricardo Chavarriaga</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering</institution><country>Ecole Polytechnique Fédérale de Lausanne, Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Silvoni, Stefano" sort="Silvoni, Stefano" uniqKey="Silvoni S" first="Stefano" last="Silvoni">Stefano Silvoni</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Laboratory of Robotics and Kinematics, IRRCSS Camillo Hospital Foundation</institution><country>Venice, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Millan, Jose Del R" sort="Millan, Jose Del R" uniqKey="Millan J" first="José Del R." last="Millán">José Del R. Millán</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering</institution><country>Ecole Polytechnique Fédérale de Lausanne, Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23055968</idno><idno type="pmc">3458432</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3458432</idno><idno type="RBID">PMC:3458432</idno><idno type="doi">10.3389/fneng.2012.00013</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">001E24</idno><idno type="wicri:Area/Pmc/Curation">001E24</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Detection of self-paced reaching movement intention from EEG
signals</title><author><name sortKey="Lew, Eileen" sort="Lew, Eileen" uniqKey="Lew E" first="Eileen" last="Lew">Eileen Lew</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering</institution><country>Ecole Polytechnique Fédérale de Lausanne, Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Chavarriaga, Ricardo" sort="Chavarriaga, Ricardo" uniqKey="Chavarriaga R" first="Ricardo" last="Chavarriaga">Ricardo Chavarriaga</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering</institution><country>Ecole Polytechnique Fédérale de Lausanne, Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Silvoni, Stefano" sort="Silvoni, Stefano" uniqKey="Silvoni S" first="Stefano" last="Silvoni">Stefano Silvoni</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Laboratory of Robotics and Kinematics, IRRCSS Camillo Hospital Foundation</institution><country>Venice, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Millan, Jose Del R" sort="Millan, Jose Del R" uniqKey="Millan J" first="José Del R." last="Millán">José Del R. Millán</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering</institution><country>Ecole Polytechnique Fédérale de Lausanne, Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Neuroengineering</title><idno type="eISSN">1662-6443</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Future neuroprosthetic devices, in particular upper limb, will require decoding and
executing not only the user's intended movement type, but also
<italic>when</italic> the user intends to execute the movement. This work investigates
the potential use of brain signals recorded non-invasively for detecting the time before a
self-paced reaching movement is initiated which could contribute to the design of
practical upper limb neuroprosthetics. In particular, we show the detection of self-paced
reaching movement intention in single trials using the readiness potential, an
electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency
range (0.1–1 Hz). Our experiments with 12 human volunteers, two of them stroke
subjects, yield high detection rates prior to the movement onset and low detection rates
during the non-movement intention period. With the proposed approach, movement intention
was detected around 500 ms before actual onset, which clearly matches previous literature
on readiness potentials. Interestingly, the result obtained with one of the stroke
subjects is coherent with those achieved in healthy subjects, with single-trial
performance of up to 92% for the paretic arm. These results suggest that, apart from
contributing to our understanding of voluntary motor control for designing more advanced
neuroprostheses, our work could also have a direct impact on advancing robot-assisted
neurorehabilitation.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Abbink, J H" uniqKey="Abbink J">J. H. Abbink</name></author><author><name sortKey="Van Der Bilt, A" uniqKey="Van Der Bilt A">A. van der Bilt</name></author><author><name sortKey="Van Der Glas, H W" uniqKey="Van Der Glas H">H. W. van der Glas</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Andersen, R A" uniqKey="Andersen R">R. A. Andersen</name></author><author><name sortKey="Cui, H" uniqKey="Cui H">H. Cui</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Awwad Shiekh Hasan, B" uniqKey="Awwad Shiekh Hasan B">B. Awwad Shiekh Hasan</name></author><author><name sortKey="Gan, J Q" uniqKey="Gan J">J. Q. Gan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Awwad Shiekh Hasan, B" uniqKey="Awwad Shiekh Hasan B">B. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Neuroeng</journal-id><journal-id journal-id-type="iso-abbrev">Front Neuroeng</journal-id><journal-id journal-id-type="publisher-id">Front. Neuroeng.</journal-id><journal-title-group><journal-title>Frontiers in Neuroengineering</journal-title></journal-title-group><issn pub-type="epub">1662-6443</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23055968</article-id><article-id pub-id-type="pmc">3458432</article-id><article-id pub-id-type="doi">10.3389/fneng.2012.00013</article-id><article-categories><subj-group subj-group-type="heading"><subject>Neuroscience</subject><subj-group><subject>Original Research Article</subject></subj-group></subj-group></article-categories><title-group><article-title>Detection of self-paced reaching movement intention from EEG
signals</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Lew</surname><given-names>Eileen</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Chavarriaga</surname><given-names>Ricardo</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Silvoni</surname><given-names>Stefano</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Millán</surname><given-names>José del R.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><sup>1</sup><institution>Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering</institution><country>Ecole Polytechnique Fédérale de Lausanne, Switzerland</country></aff><aff id="aff2"><sup>2</sup><institution>Laboratory of Robotics and Kinematics, IRRCSS Camillo Hospital Foundation</institution><country>Venice, Italy</country></aff><author-notes><fn fn-type="edited-by"><p>Edited by: Giovanni Mirabella, University of La Sapienza, Italy</p></fn><fn fn-type="edited-by"><p>Reviewed by: Hari S. Sharma, Uppsala University, Sweden; Vassiliy Tsytsarev, University
of Maryland School of Medicine, USA; Peter J. Ifft, Duke University, USA</p></fn><corresp id="fn001">*Correspondence: José del R. Millán, Defitech Chair in
Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of
Engineering, Ecole Polytechnique Fédérale de Lausanne, Switzerland.
e-mail: <email xlink:type="simple">jose.millan@epfl.ch</email></corresp></author-notes><pub-date pub-type="epreprint"><day>23</day><month>4</month><year>2012</year></pub-date><pub-date pub-type="epub"><day>12</day><month>7</month><year>2012</year></pub-date><pub-date pub-type="collection"><year>2012</year></pub-date><volume>5</volume><elocation-id>13</elocation-id><history><date date-type="received"><day>05</day><month>3</month><year>2012</year></date><date date-type="accepted"><day>20</day><month>6</month><year>2012</year></date></history><permissions><copyright-statement>Copyright © 2012 Lew, Chavarriaga, Silvoni and
Millán.</copyright-statement><copyright-year>2012</copyright-year><license license-type="open-access" xlink:href="http://www.frontiersin.org/licenseagreement"><license-p>This is an open-access article distributed under the terms of the <uri xlink:type="simple" xlink:href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution
License</uri>, which permits use, distribution and reproduction in other forums,
provided the original authors and source are credited and subject to any copyright
notices concerning any third-party graphics etc.</license-p></license></permissions><abstract><p>Future neuroprosthetic devices, in particular upper limb, will require decoding and
executing not only the user's intended movement type, but also
<italic>when</italic> the user intends to execute the movement. This work investigates
the potential use of brain signals recorded non-invasively for detecting the time before a
self-paced reaching movement is initiated which could contribute to the design of
practical upper limb neuroprosthetics. In particular, we show the detection of self-paced
reaching movement intention in single trials using the readiness potential, an
electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency
range (0.1–1 Hz). Our experiments with 12 human volunteers, two of them stroke
subjects, yield high detection rates prior to the movement onset and low detection rates
during the non-movement intention period. With the proposed approach, movement intention
was detected around 500 ms before actual onset, which clearly matches previous literature
on readiness potentials. Interestingly, the result obtained with one of the stroke
subjects is coherent with those achieved in healthy subjects, with single-trial
performance of up to 92% for the paretic arm. These results suggest that, apart from
contributing to our understanding of voluntary motor control for designing more advanced
neuroprostheses, our work could also have a direct impact on advancing robot-assisted
neurorehabilitation.</p></abstract><kwd-group><kwd>BCI</kwd><kwd>EEG</kwd><kwd>rehabilitation</kwd><kwd>self-paced protocol</kwd><kwd>stroke</kwd><kwd>voluntary movements</kwd></kwd-group><counts><fig-count count="18"></fig-count><table-count count="3"></table-count><equation-count count="0"></equation-count><ref-count count="66"></ref-count><page-count count="17"></page-count><word-count count="10249"></word-count></counts></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Human movements are usually volitional, where we spontaneously decide when to initiate it
and commit to a particular course of action to accomplish a daily task (Haggard, <xref ref-type="bibr" rid="B27">2008</xref>). This is the reason why uncovering the neural
correlates of voluntary movement is important for implementing practical Brain Computer
Interface (BCI) technology that people can use over long periods of time in a natural way.
Current non-invasive BCI allows its user to deliver mental commands to a robot controller
that transforms them into appropriate motor actions—e.g., left, right, and forward
decoded from electroencephalography (EEG) signals while the user imagines different limb
movements (Galán et al., <xref ref-type="bibr" rid="B22">2008</xref>; Millán
et al., <xref ref-type="bibr" rid="B46">2009</xref>). However, most brain-actuated robots
assume that the user wants to operate the neuroprosthesis in well-defined periods of time,
in contrast to daily experiences of motor control, where movements are executed sporadically
in a self-paced manner.</p><p>In this paper, we investigate the feasibility of detecting the intention to perform a
reaching movement in single trials before actual execution from human EEG. Intention has
been described as doing something purposefully (Schall, <xref ref-type="bibr" rid="B58">2004</xref>). In this paper, we defined intention as the time of awareness of wanting to
perform a reaching task. This definition is not to be confused with the work of Congedo et
al. (<xref ref-type="bibr" rid="B17">2006</xref>), Gonzalez et al. (<xref ref-type="bibr" rid="B25">2006</xref>), and Bai et al. (<xref ref-type="bibr" rid="B5">2007</xref>) where
movement intention was defined as the problem of classifying the intention to move the left
hand or right hand. To study movement intention, we follow a self-paced paradigm where
subjects can execute a reaching movement at any time they wish. This is a more natural and
ecological experimental setup than the classical reaction task paradigm, where subjects
perform movements in response to a cue.</p><p>A number of recent studies have found neural correlates of <italic>when</italic> subjects
decide to initiate a movement. Through invasive methods, Fried et al. (<xref ref-type="bibr" rid="B20">2011</xref>) have reported progressive neuronal recruitment in the supplementary
motor area (SMA) over 1500 ms before subjects made the decision to move. In another study
with human electrocorticography (ECoG), Ball et al. (<xref ref-type="bibr" rid="B7">2009</xref>) reported the existence peri-movement activity as early as 200 ms before
movement onset. With regard to non-invasive EEG studies, the earliest evidence of the neural
correlates of voluntary movement intention was discovered by Kornhuber and Deecke (<xref ref-type="bibr" rid="B35">1965</xref>), who identified a slow, negative potential as early
as 1.5 s before the execution of movement. This slow cortical potential (SCP) was initially
named as Bereitschaftspotential. This readiness potential has two main components. The first
one is a slow negative potential starting 1.5 s before voluntary movement. This negativity
is more prominent over the central-medial scalp. The late component occurs 400 ms before
movement, with a steeper slope over the contralateral primary motor area (Shibasaki and
Hallett, <xref ref-type="bibr" rid="B60">2006</xref>). The slow potentials originate in
depolarizations of the apical dendritic tree in the upper cortical layers that are caused by
synchronous firing, mainly from thalamocortical afferents, showing local excitatory
mobilization for negative slow potentials (Birbaumer, <xref ref-type="bibr" rid="B9">1999</xref>). The presence of this readiness potential was further analyzed in a series
of famous studies by Libet et al. (<xref ref-type="bibr" rid="B41">1982</xref>, <xref ref-type="bibr" rid="B40">1983</xref>) who showed that there is an unconscious preparatory
brain activity that begins 1 s or more before movement, preceding the conscious awareness to
act. Similar negativity components have been observed in patients with brain lesions (Deecke
et al., <xref ref-type="bibr" rid="B18">1987</xref>).</p><p>Nevertheless, being a SCP close to DC, the presence of the readiness potential in single
trials seems to be elusive. Another EEG correlate of movement preparation and execution is
the event-related desynchronization (ERD; Pfurtscheller and Lopes da Silva, <xref ref-type="bibr" rid="B54">1999</xref>), a decrease in mu and beta power (8–30 Hz)
over the contralateral primary motor cortex. Bai et al. (<xref ref-type="bibr" rid="B6">2011</xref>) showed that self-paced wrist extension movement onset can be detected on
average 0.62± 0.25 s before actual movement from the analysis of ERD. Finally, Awwad
Shiekh Hasan and Gan (<xref ref-type="bibr" rid="B3">2010</xref>, <xref ref-type="bibr" rid="B4">2011</xref>) studied the EEG activity in the mu, beta and lower gamma bands
(8–45 Hz) to detect movement onset also during self-paced wrist extension movements.
They achieved good results, but with a poor temporal resolution (from 2 s before to 2 s
after the movement).</p><p>Here we show for the first time the detection of self-paced reaching movement intention in
single trials from the analysis of the readiness potential in 12 human volunteers, two of
them stroke subjects. In this study, we used EEG signals filtered in a narrow frequency
range of [0.1–1] Hz, which is reported to better capture anticipatory-related SCPs
(Garipelli et al., <xref ref-type="bibr" rid="B24">2011</xref>). We explicitly focus on the
readiness potential for two reasons. Firstly, as mentioned above, it is a well-known
correlate of voluntary movement intention. Secondly, it is a promising non-invasive method
for localization of motor control after hemispheric lesions (Green, <xref ref-type="bibr" rid="B26">2003</xref>), which could be useful for understanding motor functional
improvements following rehabilitation.</p><p>In this respect, apart from contributing to our understanding of voluntary motor control
and to the design of more advanced neuroprostheses, our work could also have a direct impact
on advancing robot-assisted neurorehabilitation (Riener et al., <xref ref-type="bibr" rid="B56">2005</xref>; Johnson, <xref ref-type="bibr" rid="B33">2006</xref>). Indeed,
robot-assisted therapy for stroke patients with moderate-to-severe upper-limb deficits has
shown promising results in terms of improving motor functional recovery compared to
traditional therapy (Kwakkel et al., <xref ref-type="bibr" rid="B36">2008</xref>; Masiero et
al., <xref ref-type="bibr" rid="B44">2009</xref>; Staubli et al., <xref ref-type="bibr" rid="B63">2009</xref>; Lo et al., <xref ref-type="bibr" rid="B42">2010</xref>; Hogan and
Krebs, <xref ref-type="bibr" rid="B28">2011</xref>). Still this kind of neurorehabilitation
therapy could be improved, as earlier detection of movement intention can minimize the
delays in device activation and, thus, allow tighter coupling between the initial formation
of the motor plan in the cortex and its execution at the periphery through movement-assisted
devices, thus better promoting brain plasticity after stroke (Muralidharan et al., <xref ref-type="bibr" rid="B50">2011</xref>). It is for this reason that, in one of the
experiments, we have involved stroke patients in order to carry out a first feasibility
study.</p><p>Single-trial classification of SCP has already been used in BCI, most notably by Birbaumer
et al. (<xref ref-type="bibr" rid="B10">1999</xref>). Recently, Bradberry et al. (<xref ref-type="bibr" rid="B12">2010</xref>) showed the possibility of decoding arm trajectories
from SCPs. Garipelli et al. (<xref ref-type="bibr" rid="B23">2009</xref>) has also analyzed
the SCP for studying and classifying anticipatory behavior. Bai et al. (<xref ref-type="bibr" rid="B5">2007</xref>) explored the use of SCP, computed with a low-pass
filter at 10 Hz, for classifying a right vs. left hand movement. In this work, the focus is
on identifying the intention to execute a self-paced reaching action before the movement
starts, irrespective of the movement direction or laterality. It is also worth noting that
the readiness potentials have a similar shape to SCP associated to anticipatory behavior, in
particular the contingent negative variation (Walter et al., <xref ref-type="bibr" rid="B66">1964</xref>). However, as discussed in Rektor (<xref ref-type="bibr" rid="B55">2003</xref>), while both kinds of SCP are readily confounded in scalp recordings, more
invasive techniques (Ikeda et al., <xref ref-type="bibr" rid="B30">1994</xref>; Lamarche et
al., <xref ref-type="bibr" rid="B37">1995</xref>) or clever experimental designs (Ruchkin et
al., <xref ref-type="bibr" rid="B57">1986</xref>; Brunia and Damen, <xref ref-type="bibr" rid="B13">1988</xref>) demonstrate differences.</p><p>The experiments and the proposed methods are detailed in section 2. In section 3, we report
the experimental results where, in particular, we compare the effect of using manually and
automatically selected channels. We also report on the classification of movement onset
using electromyograph (EMG) signals as well as on the classification of non-movement
intention period. Finally, we discuss the implications of our results in section 4.</p></sec><sec id="s2"><title>2. Materials and methods</title><sec><title>2.1. Experimental protocols</title><p>We have designed two experiments: (1) EEG recordings of free arm reaching movements to a
target button from healthy subjects using only their dominant arms, and (2) EEG
measurements of a high-precision arm reaching task from stroke patients and healthy
subjects as a control group. The reason why, after the promising results achieved in the
first experiment, we have run a second experiment with a small stroke cohort is to make a
preliminary study on the feasibility to detect movement intention in single trials as a
potential tool for rehabilitation. This experiment was done in a clinical setting. In this
later experiment, subjects performed the task with both arms in order to analyze possible
differences in performance between the paretic and healthy arms of patients.</p><sec><title>2.1.1. Experiment 1</title><p>Eight subjects (three female, age 29.33 ± 2.06) participated in the experiment.
They were informed about the experimental procedures and gave their consent. All
subjects were healthy with no known history of neurological abnormalities or
musculo-skeletical disorders. Seven out of the eight subjects in this experiment were
right-handed.</p><p>The experimental workspace consisted of four targets (up, down, left, and right), which
correspond to buttons on the horizontal plane located with respect to the mid-sagittal
plane of the subjects as shown in Figure <xref ref-type="fig" rid="F1">1</xref> (left).
Despite the center-out reaching task, it is important to highlight that the decoding of
reaching directions is not within the scope of this paper as, here, we are interested in
studying the common initiation of movement, irrespective of the movement type. The
dimension of the horizontal plane was 47.0 × 48.5 cm (length × width).
The distance from the home position to each target positions was approximately 20 cm.
The target and home position buttons were a disc with a diameter of 27 mm. The design of
the targets and home position consisted of microswitch buttons with direct connection to
the input trigger of the ActiveTwo (EEG recording device) USB2 receiver. The buttons act
as the event marker for the movement onset. This design provides a high temporal
resolution in marking the movement onset events (releasing the buttons from the center
rest position). The recordings were conducted in a normal office environment, with
people working and speaking around, to mimic as close as possible a realistic
scenario.</p><fig id="F1" position="float"><label>Figure 1</label><caption><p><bold>Experimental setup for Experiment 1(left) and Experiment 2
(right)</bold>.</p></caption><graphic xlink:href="fneng-05-00013-g0001"></graphic></fig><p>Subjects were instructed to perform natural self-paced center-out and center-in arm
reaching tasks with their dominant arm. They were asked to fixate their eyes on a cross
in the middle of the vertical plane as shown in Figure <xref ref-type="fig" rid="F1">1</xref> (left), thus minimizing eye movement-related artifacts in the recording.
Each trial began with the subject placing their dominant hand on the center position.
While at this position, subjects were asked to relax their hand, forearm, elbow, and arm
in order not to induce any muscular tension which could possibly effect the outcome of
the analysis. After 500 ms, an auditory cue informed the subject which target direction
to reach. However, subjects were not supposed to react immediately (i.e., reaction task)
or wait a fixed period of time (i.e., memory task) after the presentation of the cue. In
contrast, they initiated the movement whenever they wish, but not before 2 s after the
presentation of the auditory cue.</p><p>The role of the auditory cue was to ensure equal distribution of targets to be reached.
There were a total of 200 trials recorded for each subject. Nevertheless, not all trials
were kept for analysis. We discarded trials where the subject moved earlier than 2 s. We
also removed trials if subjects reached to the wrong target. Finally, we discarded
trials contaminated with strong artifacts or noise. After that, it remained an average
of 188 trials across all subjects, where the average preparation time
(<italic>T</italic><sub><italic>onset</italic></sub>) is 5.03± 1.77 s as shown
in Figure <xref ref-type="fig" rid="F2">2</xref>.</p><fig id="F2" position="float"><label>Figure 2</label><caption><p><bold>The timeline of the experimental protocol.</bold> Each trial starts when the
subject places their hand on the center button. Next, the auditory cue informs the
subject which direction to reach. After a delayed period of more than 2 s, he
releases his hands from the home position and reaches towards the target. In order
to complete the movement, the subject returns back to the home position before
starting the next trial. Only center-out reaching periods are considered. The
average <italic>T</italic><sub><italic>onset</italic></sub> across all subjects in
Experiment 1 is 5.03 ± 1.77 s.</p></caption><graphic xlink:href="fneng-05-00013-g0002"></graphic></fig><p>The design of this experiment allows voluntary initiation of movement by the subjects,
in contrast with most cue-based reaction time task experimental protocols where there is
a <italic>go</italic> cue that instructs the subject when to start the movement. It has
been shown that the brain areas involved in a spontaneous task differ from those of an
instructed task (Thut et al., <xref ref-type="bibr" rid="B64">2000</xref>). In
particular, they found longer lasting activity in the SMA during the spontaneous task
and in the premotor area (PMA) during the instructed condition. Lu et al. (<xref ref-type="bibr" rid="B43">2011</xref>) also reported different brain areas responsible
for cued and self-initiated movements.</p><p>The reaching task in this study is a form of unconstrained, multi-degree of freedom
movement. Therefore, besides EEG, we also recorded EMG signals from the <italic>musculus
biceps branchii</italic> (selection of location through trial and error before
experiment) to monitor that there is no muscular activity during the preparation period.
This signal was also used to determine the time onset of muscular activation with
respect to the movement onset given by the experimental apparatus (i.e., microswitch at
center position).</p><p>Before the experiment starts, subjects were asked to perform a calibration session
where they have to move their eyes toward the targets, and to perform a 1 min natural
eye blinking (Schlögl et al., <xref ref-type="bibr" rid="B59">2007</xref>). This
session was used to measure the effects of eye movements on the EEG signals (see section
2.2.2).</p></sec><sec><title>2.1.2. Experiment 2</title><p>Four subjects, recorded at the San Camillo Hospital, Venice, Italy, participated in
this experiment. There were two stroke patients and two healthy control subjects. All
procedures were approved by the Ethics Committee of the San Camillo Hospital before
experimentations. All subjects were informed about the experimental procedures and gave
their consent.</p><p>Table <xref ref-type="table" rid="T1">1</xref> shows a summary of the subjects'
particulars. All subjects were right-handed. Stroke subject <bold>dpm</bold> suffers
from a left cerebellar hemorrhagic stroke, also commonly known as intracerebral bleed,
where the ipsilateral body part is affected. Stroke subject <bold>lg</bold> suffers from
a left nucleo-capsular stroke caused by lesion in a deeper brain structure, thus
affecting the contralateral limb. Table <xref ref-type="table" rid="T1">1</xref> also
reports the Fugl-Meyer Motor Assessment score for upper extremity
(FMA-UE)—maximum score of 66—for both stroke subjects. Both patients had
preserved tactile and proprioceptive sensibility of the arm with normal cognitive
abilities at the time of admission to the hospital.</p><table-wrap id="T1" position="float"><label>Table 1</label><caption><p><bold>Details of subjects who participated in the Experiment 2</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1"><bold>Subject</bold></th><th align="left" rowspan="1" colspan="1"><bold>Age</bold></th><th align="left" rowspan="1" colspan="1"><bold>Medical condition</bold></th><th align="left" rowspan="1" colspan="1"><bold>Paretic arm</bold></th><th align="left" rowspan="1" colspan="1"><bold>Time since stroke</bold></th><th align="left" rowspan="1" colspan="1"><bold>FMA-UE</bold></th><th align="left" rowspan="1" colspan="1"><bold>Left hand MT</bold></th><th align="left" rowspan="1" colspan="1"><bold>Right hand MT</bold></th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">cg</td><td align="left" rowspan="1" colspan="1">25</td><td align="left" rowspan="1" colspan="1">Healthy</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">0.61 ± 0.19</td><td align="left" rowspan="1" colspan="1">0.58 ± 0.15</td></tr><tr><td align="left" rowspan="1" colspan="1">gc</td><td align="left" rowspan="1" colspan="1">26</td><td align="left" rowspan="1" colspan="1">Healthy</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">0.70 ± 0.17</td><td align="left" rowspan="1" colspan="1">0.66 ± 0.16</td></tr><tr><td align="left" rowspan="1" colspan="1">dpm</td><td align="left" rowspan="1" colspan="1">50</td><td align="left" rowspan="1" colspan="1">Stroke</td><td align="left" rowspan="1" colspan="1">left</td><td align="left" rowspan="1" colspan="1">55 days</td><td align="left" rowspan="1" colspan="1">56/66</td><td align="left" rowspan="1" colspan="1"><bold>3.53 ± 1.63</bold></td><td align="left" rowspan="1" colspan="1">1.67 ± 0.73</td></tr><tr><td align="left" rowspan="1" colspan="1">lg</td><td align="left" rowspan="1" colspan="1">61</td><td align="left" rowspan="1" colspan="1">Stroke</td><td align="left" rowspan="1" colspan="1">right</td><td align="left" rowspan="1" colspan="1">658 days</td><td align="left" rowspan="1" colspan="1">43/66</td><td align="left" rowspan="1" colspan="1">1.59 ± 0.35</td><td align="left" rowspan="1" colspan="1"><bold>2.34 ± 0.36</bold></td></tr></tbody></table><table-wrap-foot><p>MT refers to the time needed to complete a reaching movement. Values in
bold show the performance with the paretic arm.</p></table-wrap-foot></table-wrap><p>The subject was seated in front of a computer screen holding on to a haptic
manipulandum (PHANTOM Premium 3.0/6DOF, Sensable Technologies) with her arm resting on
the table as shown in Figure <xref ref-type="fig" rid="F1">1</xref> (right). This
experiment used a similar paradigm to the previous experiment. In contrast to the
previous experiment, the reaching task was performed with both arms. The subjects were
instructed to move a manipulandum that controls the position of a cursor (a green
circle) on a computer screen. The rest position is the condition when the green circle
remains inside the white box located in the middle of the screen. The task was to bring
the cursor to one of the center-out target box. When the target was cued, the subject
was asked to wait at least 2 s before initiating the movement. If he failed to do so,
the subject had to move the cursor back to the rest position and wait for another 2 s
before initiating the movement. The trial was discarded from analysis and repeated until
the subject successfully fulfilled the requirement of 2 s delay period.</p><p>Subjects were asked to minimize their eye movements, in particular, before starting the
arm movement. In this experiment, there was also a calibration session to record the
baseline eye movement activity as in Experiment 1. The subject was asked to blink for 5
s, then, he had to look back and forth between the home position and the different
targets as they appear on the screen where each target appeared five times. The
recordings from the calibration session have been used for studying the effects of eye
movements on the EEG channels (see section 2.2.2).</p><p>For each subject, we performed 3 recordings of 80 trials (targets are randomly cued),
thus resulting in a total of 240 trials for each arm movement. After discarding early
starts and artifacts, it remains an average of 229 trials for the left hand and 230
trials for the right hand across all subjects. For the stroke patients, the unaffected
arm was tested first. The whole experiment lasted from 3 to 4 h, including the
electrodes placement time. Each recording lasted from 6 to 15 min. Both stroke subjects
were able to achieve the reaching task without much difficulty, but with longer average
reaching time (as shown in Table <xref ref-type="table" rid="T1">1</xref>) in comparison
with the control subjects. Previous analysis with stroke subjects has reported that
goal-directed arm movements are slower and more variable than healthy subjects'
(Levin, <xref ref-type="bibr" rid="B39">1996</xref>; Cirstea and Levin, <xref ref-type="bibr" rid="B16">2000</xref>).</p></sec></sec><sec><title>2.2. Methods</title><sec><title>2.2.1. EEG and EMG recordings</title><p>We acquired EEG potentials with a portable ActiveTwo measurement system from BioSemi
(<ext-link ext-link-type="uri" xlink:href="http://www.biosemi.com">http://www.biosemi.com</ext-link>) using 64 electrodes arranged in the modified 10/20
International System. This system was also used to record the electrooculograph (EOG)
signal. In Experiment 1, the Biosemi ActiveTwo measurement system was also used to
record the EMG signals from the arm. As for the second experiment, the EMG signals were
recorded with a Biopac System (<ext-link ext-link-type="uri" xlink:href="http://www.biopac.com">http://www.biopac.com</ext-link>).</p><p>The signals were recorded at a sampling rate of 2048 Hz and downsampled to 256 Hz. To
analyze EEG, we first applied the Common Average Referencing (CAR) procedure (Offner,
<xref ref-type="bibr" rid="B51">1950</xref>; Osselton, <xref ref-type="bibr" rid="B52">1965</xref>), where, at each time step, the average potential over all the channels
is subtracted from each channel. The re-referencing procedure removes the global
background activity, keeping activity from local sources beneath the electrodes. The
most intuitive implementation of a CAR is to use all the recorded channels (Bertrand et
al., <xref ref-type="bibr" rid="B8">1985</xref>). However, the EEG channels could be
contaminated by noise, in particular by EOG and muscular artifacts, that may propagate
to all other unaffected channels. In the next section we identify the EEG channels that
are affected by EOG artifacts, which are the most prominent potential source of noise
given the nature of the task and the frequency band to be analyzed. These channels are
then removed from the analysis and, in particular, for computation of the CAR.</p></sec><sec><title>2.2.2. Ocular artifacts</title><p>EOG signals were acquired from three electrodes positioned above the nasion, and below
the outer canthi of the eyes (Schlögl et al., <xref ref-type="bibr" rid="B59">2007</xref>). The bipolar EOG channels in the <italic>left-central</italic> and
<italic>central-right</italic> positions were able to capture both the horizontal and
the vertical EOG components.</p><p>Besides manual removal of noisy trials, we used a regression analysis method to assess
the influence of EOG artifacts on each EEG channel (Schlögl et al., <xref ref-type="bibr" rid="B59">2007</xref>). Briefly, channels having a large correlation
with the EOG components are discarded from the montage before performing the CAR.</p><p>Figure <xref ref-type="fig" rid="F3">3</xref> illustrates the regression coefficients
of the horizontal and vertical EOG components with the EEG channels computed in the
calibration session for one of the subjects. The EEG signals are re-referenced using CAR
with all 64 channels recorded from the experiment. This figure illustrates high
contributions of eye movement artifacts in the frontal and temporal electrodes.</p><fig id="F3" position="float"><label>Figure 3</label><caption><p><bold>Regression coefficients of EOG components plotted on a topographical map,
showing the effect of eye movement on scalp electrodes using signal re-referenced
with all 64-channels recorded from one of the subjects.</bold> The rightmost
figure shows the sum of the contributions of both vertical EOG and horizontal
EOG.</p></caption><graphic xlink:href="fneng-05-00013-g0003"></graphic></fig><p>We first remove the peripheral electrodes, filter the signals with CAR using the
remaining 41 electrodes, and recomputed the coefficients. As shown in Figure <xref ref-type="fig" rid="F4">4</xref> (top panel), the effects of vertical EOG is still
high on the frontal electrodes. We further remove electrodes for which the coefficients
were above 0.3. Thus, re-referencing the signals with a total of 34 channels can
minimize the effects of eye movements on the scalp EEG as shown in Figure <xref ref-type="fig" rid="F4">4</xref> (bottom).</p><fig id="F4" position="float"><label>Figure 4</label><caption><p><bold>(Top) The weights of EOG artifacts by re-referencing the signals with
41-channels and (bottom) 34-channels.</bold> This figure shows the EOG
coefficients from the calibration session of one of the subjects participating in
the experiments.</p></caption><graphic xlink:href="fneng-05-00013-g0004"></graphic></fig><p>Similar results were obtained for all other subjects in both experiments. Therefore, in
this paper, we performed the analysis with only 34 channels as shown in Figure <xref ref-type="fig" rid="F5">5</xref>.</p><fig id="F5" position="float"><label>Figure 5</label><caption><p><bold>Grand averages of SCPs for all the right-handed subjects participating in
Experiment 1.</bold> EEG signals are filtered between 0.1 and 1 Hz.
<italic>t</italic> = 0 corresponds to the movement onset.</p></caption><graphic xlink:href="fneng-05-00013-g0005"></graphic></fig></sec><sec><title>2.2.3. Pre-processing</title><p>The EEG signals were processed with a narrow band zero-phase non-causal IIR filter with
cutoff frequencies of 0.1 and 1 Hz which has been reported to better capture
anticipatory-related SCPs (Garipelli et al., <xref ref-type="bibr" rid="B24">2011</xref>). The EOG signals were also preprocessed with the same method as the EEG
signals.</p><p>EMG signals were acquired bipolarly over the <italic>musculus biceps brachii</italic>of the subject's arm, and high-pass-filtered with a Butterworth filter (8th
order, cutoff of 50 Hz) to remove motion artifacts. The signals were then rectified,
low-pass-filtered (8th order, cutoff of 20 Hz) and integrated over 25 ms to obtain
envelopes of EMG activity (Cheung et al., <xref ref-type="bibr" rid="B15">2009</xref>).
The purpose of recording the EMG is to monitor that there is no muscular activity during
the reaching preparation phase and to ensure that the movement intention detected is not
due to the muscular activity of the arm through classification of the EMG activity (see
section 3.3).</p></sec><sec><title>2.2.4. Channel selection</title><p>We compare the classification performance using manually and automatically selected
channels. In the first case, channels were selected on the basis of the grand-average
SCP. In the case of automatic selection, the channels are ranked according to their
discriminant power (DP; see below).</p><p>For computing the grand averages of SCPs, each epoch was baseline corrected with the
average activity between [2 2.25] s before the movement onset. Figure <xref ref-type="fig" rid="F5">5</xref> shows the grand averages of SCPs over all
right-handed subjects participating in Experiment 1 for each of the 34 channels. The
SCPs in stroke subjects obtained from Experiment 2 exhibit a similar trend in the
development of the negativity prior to the movement onset, as shown in Figure <xref ref-type="fig" rid="F6">6</xref>. However, the negativity peaked after 1 s of movement
onset, when for control subjects the peak was roughly at movement onset (see also Figure
<xref ref-type="fig" rid="F5">5</xref>). This is in agreement with Jankelowitz and
Colebatch (<xref ref-type="bibr" rid="B32">2005</xref>), who recorded a larger and
longer readiness potential when the stroke subjects moved the affected limb. The
channels chosen manually for classification were C1, Cz, C2, CP1, CPz, CP2 as they
exhibit prominent negative slopes in the grand average and are also consistent with
previous literature (Kornhuber and Deecke, <xref ref-type="bibr" rid="B35">1965</xref>;
Libet et al., <xref ref-type="bibr" rid="B41">1982</xref>).</p><fig id="F6" position="float"><label>Figure 6</label><caption><p><bold>Grand averages of SCPs, filtered between 0.1 and 1 Hz, for the paretic arm of
stroke patient <italic>lg</italic> (right arm) from Experiment 2.</bold><italic>t</italic> = 0 corresponds to the movement onset.</p></caption><graphic xlink:href="fneng-05-00013-g0006"></graphic></fig><p>Alternatively, we performed automatic channel selection using the Canonical Variant
Analysis (CVA) (also commonly known as Multivariate Discriminant Analysis; Galán
et al., <xref ref-type="bibr" rid="B21">2007</xref>). This technique estimates the DP of
each channel by comparing the movement preparation period to the non-movement related
period. Figure <xref ref-type="fig" rid="F7">7</xref> shows the DP value of each channel
in the form of a topographic map for EEG signals in the frequency range [0.1–1]
Hz for all eight subjects in Experiment 1. It is observed that the channels with high DP
for movement preparation are different for each subject, suggesting subject-specific
brain patterns in preparing reaching movement. With the exception of subject
<bold>a5</bold> (where four out of six of the predefined channels match the most
discriminant channels selected by CVA), the topoplots for the rest of the subjects
showed high DP index in the frontal and parietal regions. According to Andersen and Cui
(<xref ref-type="bibr" rid="B2">2009</xref>), the posterior parietal and frontal
cortical areas are responsible for planning and decision making of movement intent. In
this respect, it has also been reported that the frontal and parietal cortex region of
the human brain carried considerable information to predict the outcome of a motor
decision the subject had not yet consciously made (Soon et al., <xref ref-type="bibr" rid="B62">2008</xref>). For subjects <bold>f1</bold>, <bold>b5</bold>,
<bold>e7,</bold> and <bold>e8</bold>, CVA did not show any similarity with the
pre-defined channels. Finally, we can observe that the topographic map for subject
<bold>d6</bold>, the only left-handed subject in this study, showed high DP on the
contralateral channels (right hemisphere).</p><fig id="F7" position="float"><label>Figure 7</label><caption><p><bold>Each topoplot shows the normalized discriminant power index of each channel
for a single healthy subject in Experiment 1</bold>.</p></caption><graphic xlink:href="fneng-05-00013-g0007"></graphic></fig><p>As for Experiment 2, Figure <xref ref-type="fig" rid="F8">8</xref> shows the DP value
for each channel using the amplitudes of EEG signals filtered between [0.1–1] Hz
for control subjects and stroke subjects (both left and right hand data). The regions
showing the highest DP for healthy controls were similar to the observations in
Experiment 1. Comparison between the pre-defined channels and the six most discriminant
channels selected using the CVA method showed that four out of six channels were similar
for stroke subject <bold>lg</bold> with data from both hands and for control subject
<bold>cg</bold> only for the right hand. The topographic maps for stroke subject
<bold>lg</bold> shows very consistent DP between left and right hand, and most
importantly, the focus area is similar to the pre-defined channels set. As in Experiment
1, this central region is where the SCPs show high negativity prior to movement
onset.</p><fig id="F8" position="float"><label>Figure 8</label><caption><p><bold>The topographic maps show the normalized discriminant power index of each
channel for the left and right hand for the control subjects (<italic>cg</italic>and <italic>gc</italic>) and the stroke subjects (<italic>dpm</italic> and
<italic>lg</italic>).</bold> Plots highlighted with a blue frame refers to the
paretic arm of the patients.</p></caption><graphic xlink:href="fneng-05-00013-g0008"></graphic></fig><p>In section 3, we will show the performance differences between using pre-defined and
CVA-selected channels.</p></sec><sec><title>2.2.5. Classification</title><p>To detect the movement intention, we categorized the signals into two different time
periods, namely the baseline period (idle period) and the movement preparation (active
period). During the <italic>idle period</italic>, we assume that there is no on-going
movement preparation activity. This period was taken 500 ms before the auditory target
cue at each trial. The second part is the movement preparation period, which we termed
as the <italic>active period</italic>, defined at [–750 –250] ms before
the movement onset. Figure <xref ref-type="fig" rid="F9">9</xref> depicts the selection
of the EEG samples to train the movement intention classifier. Training data for the
classifier consists of the baseline period (yellow box) as class <italic>idle</italic>and the movement preparation period (green box) as class <italic>active</italic>. As a
classifier, we relied on Linear Discriminant Analysis (LDA), whose input was a vector
with the EEG amplitude of the selected channels in a 500 ms window to capture the
negative slope occurring 400 ms before start of movement. The EEG signals were
subsampled from 256 to 8 Hz (four data points per window) before classification,
resulting in a total of 24 features. To test the performance of our classifier, we used
500 ms windows shifted every 10 ms starting from 2500 ms before movement onset until
1000 ms after movement onset. We report below the results of a fivefold
cross-validation. It is worth noting that the data used for feature selection is only
based on the training data. Furthermore, we employed a cross-validation method which
maintain the chronological order of the data (Millán, <xref ref-type="bibr" rid="B45">2004</xref>; Bourdaud et al., <xref ref-type="bibr" rid="B11">2008</xref>)
which yields a better, and less optimistic, estimation of accuracy in comparison with
the traditional method.</p><fig id="F9" position="float"><label>Figure 9</label><caption><p><bold>Selected EEG samples to build the training set of the movement intention
classifier</bold>.</p></caption><graphic xlink:href="fneng-05-00013-g0009"></graphic></fig></sec></sec></sec><sec id="s3"><title>3. Results</title><sec><title>3.1. Experiment 1</title><p>Figure <xref ref-type="fig" rid="F10">10</xref> shows the results of movement intention
detection where each plot represents the performance of a single subject in Experiment 1.
Each plot reports the average <italic>sensitivity</italic> rate, or True Positive Rate
(TPR), across the five test folds in the time window [−2, 1] s with respect to the
actual movement onset. This can also be interpreted as the percentage of trials being
detected as movement intention at time <italic>t</italic>. This time in the plot (X-axis)
corresponds to the last sample of the analysis window analyzed by the classifier. The
shaded region bounding the average TPR illustrates the standard deviation at each point.
The magenta line refers to the onset of <italic>biceps branchii</italic> muscular
activation. This is defined as the time when the EMG activity exceeds a threshold equal to
μ + 3σ, where μ and σ are the mean and standard
deviation of EMG signals of a one-second window after the target cue (Abbink et al., <xref ref-type="bibr" rid="B1">1998</xref>). On average, all subjects exhibit an early arm
muscular activity at 263 ± 40 ms. Similar EMG timing has been observed by Flanders
(<xref ref-type="bibr" rid="B19">1991</xref>) and Hong et al. (<xref ref-type="bibr" rid="B29">1994</xref>) who studied the temporal patterns of muscles activation for
unconstrained arm reaching movement in three dimensional space. The chance level line (in
red) was calculated by shuffling the labels of the training data and performing 10 times
fivefold cross validation. To test whether the sensitivity rate is significantly above the
chance level with 95% confidence interval, we used the Wilcoxon rank sum test. The line in
green depicts the first time a group of five consecutive samples has a true positive rate
significantly above chance level (<italic>p</italic> < 0.05).</p><fig id="F10" position="float"><label>Figure 10</label><caption><p><bold>Single trial performances of movement intention detection for all subjects in
Experiment 1 using SCPs in the frequency range [0.1–1] Hz during the time
interval (−2, 1) s with respect to the actual movement onset.</bold> Y-axis
of the plots represents the movement intention detection rate. The magenta line
depicts the onset from arm muscular activation (−263 ± 40 ms on
average across all subjects). The green line depicts the first time a group of five
consecutive samples has a TPR significantly above chance level (<italic>p</italic>< 0.05), which is shown as a red line. The gray and red shaded regions
bounding the performance curves indicates their standard deviation at each point. Note
that the variance of the random performance is so small that the red shaded area is
barely visible.</p></caption><graphic xlink:href="fneng-05-00013-g0010"></graphic></fig><p>Movement intention can be detected above chance level across healthy subjects on average
at 460 ± 85 ms before actual onset. The detection of movement intention is before
arm muscular activation with the exception of subject <bold>b5</bold>. As reported in
Table <xref ref-type="table" rid="T2">2</xref>, the average maximum TPR was 0.76 ±
0.07, peaking on average 167 ms before movement onset. As we will see in section 3.3,
although this peak performance is achieved slightly after EMG onset, using only EMG is a
less reliable predictor for movement intention.</p><table-wrap id="T2" position="float"><label>Table 2</label><caption><p><bold>Maximum TPR for each subject and the time point (in ms) when this value is
reached</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1"><bold>Subject ID</bold></th><th align="left" rowspan="1" colspan="1"><bold>TPR</bold></th><th align="left" rowspan="1" colspan="1"><bold>Time (ms)</bold></th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">a5</td><td align="left" rowspan="1" colspan="1">0.85</td><td align="left" rowspan="1" colspan="1">−110</td></tr><tr><td align="left" rowspan="1" colspan="1">a8</td><td align="left" rowspan="1" colspan="1">0.70</td><td align="left" rowspan="1" colspan="1">−240</td></tr><tr><td align="left" rowspan="1" colspan="1">b5</td><td align="left" rowspan="1" colspan="1">0.83</td><td align="left" rowspan="1" colspan="1">−150</td></tr><tr><td align="left" rowspan="1" colspan="1">c2</td><td align="left" rowspan="1" colspan="1">0.84</td><td align="left" rowspan="1" colspan="1">−100</td></tr><tr><td align="left" rowspan="1" colspan="1">d6</td><td align="left" rowspan="1" colspan="1">0.72</td><td align="left" rowspan="1" colspan="1">−120</td></tr><tr><td align="left" rowspan="1" colspan="1">e7</td><td align="left" rowspan="1" colspan="1">0.80</td><td align="left" rowspan="1" colspan="1">−140</td></tr><tr><td align="left" rowspan="1" colspan="1">e8</td><td align="left" rowspan="1" colspan="1">0.71</td><td align="left" rowspan="1" colspan="1">−190</td></tr><tr><td align="left" rowspan="1" colspan="1">f1</td><td align="left" rowspan="1" colspan="1">0.69</td><td align="left" rowspan="1" colspan="1">−290</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Average ± std</bold></td><td align="left" rowspan="1" colspan="1"><bold>0.76 ± 0.07</bold></td><td align="left" rowspan="1" colspan="1"><bold>−167 ± 68</bold></td></tr></tbody></table></table-wrap><p>As described in section 2.2.4, we performed another analysis using the six best selected
channels yielded by the CVA method. Figure <xref ref-type="fig" rid="F11">11</xref> shows
an earlier detection of movement intention with the features selected automatically,
except for subject <bold>a5</bold>, <bold>b5</bold>, and <bold>e7</bold>. Unsurprisingly,
for subject <bold>a5</bold> time differences are only 50 ms, since the CVA-selected
channels highly overlap with the pre-selected set. The left-handed subject,
<bold>d6</bold>, with high discriminability on the right lateral brain area, showed an
earlier detection time of 360 ms using channels selected with CVA feature selection
method. All subjects showed TPR above 70%, except for subject <bold>e8</bold> whose DP
topographic map did not show a strong discriminative area.</p><fig id="F11" position="float"><label>Figure 11</label><caption><p><bold>Time of movement intention detection comparison between pre-selected channels
set and best selected channel using CVA techniques</bold>.</p></caption><graphic xlink:href="fneng-05-00013-g0011"></graphic></fig></sec><sec><title>3.2. Experiment 2</title><p>Figure <xref ref-type="fig" rid="F12">12</xref> shows the results of movement intention
detection for both the left and right hand reaching movement of all subjects (the upper
graphs correspond to the healthy control subjects, followed by the two stroke patients).
For all subjects, movement intention can be detected more than 400 ms before the recorded
onset with their left hand and right hand. Movement intention can also be detected before
the onset of EMG activity (magenta line, see previous section for details) for all
subjects and conditions, except the non-paretic arm (right) of stroke subject
<bold>dpm</bold>. The false positive rate prior to the detection of movement intention
was also low (between 0.1 and 0.2 for both hands) for all subjects except stroke subject
<bold>dpm</bold>. It is worthy to note that stroke subject <bold>dpm</bold> was a recent
stroke patient (see Table <xref ref-type="table" rid="T1">1</xref>) and, probably, the
neural reorganization processes were still ongoing at the time of the experiment, which
took place only 1.5 month after the stroke.</p><fig id="F12" position="float"><label>Figure 12</label><caption><p><bold>Single trial performances of movement intention detection for all subjects in
Experiment 2 (both left and right arm reaching movement) using SCPs in the frequency
range [0.1–1] Hz during the time interval (−2, 1) s with respect to
the actual movement onset.</bold> This figure has a similar format to Figure <xref ref-type="fig" rid="F10">10</xref>. Plots highlighted with a blue frame refers to
the paretic arm of the stroke subjects.</p></caption><graphic xlink:href="fneng-05-00013-g0012"></graphic></fig><p>The result for stroke subject <bold>lg</bold> in Figure <xref ref-type="fig" rid="F12">12</xref> (last row) exhibits a different performance curve as compared with the
results of healthy controls. In particular, a high detection rate sustained up to 1 s
after onset of movement. This difference could be due to the slower speed of reaching
(c.f. Table <xref ref-type="table" rid="T1">1</xref>) of stroke subject <bold>lg</bold> is
2.34 ± 0.36 s using his paretic arm compared to a faster speed of 0.5–0.6
s for healthy controls. The longer sustained high performance could also be due to the
fact that the readiness potential of the affected limb in stroke subjects has higher
amplitude over a longer period of time (Jankelowitz and Colebatch, <xref ref-type="bibr" rid="B32">2005</xref>).</p><p>As reported in Table <xref ref-type="table" rid="T3">3</xref>, the average maximum TPR
obtained in this experiment was 0.81 for the left hand, peaking on average 140 ms before
movement onset, while for the right hand the average maximum TPR is 0.79 at 162 ms before
movement onset. It is interesting to note that for the two conditions where the peak
performance is closer to movement onset (left hand of healthy subject <bold>gc</bold> and
right hand of stroke subject <bold>dpm</bold>), performance stabilizes soon after EMG
onset (in between −300 and −200 ms before movement onset) and slowly
reaches its maximum value, which is 0.9 or higher. Altogether, these results are in
agreement with those of Experiment 1 carried out with a larger set of subjects.</p><table-wrap id="T3" position="float"><label>Table 3</label><caption><p><bold>Maximum TPR for each subject and hand, and the time point (in ms) when the TPR
reaches maximum value</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1"><bold>Subject ID</bold></th><th align="center" colspan="2" rowspan="1"><bold>Left hand</bold></th><th align="center" colspan="2" rowspan="1"><bold>Right hand</bold></th></tr><tr><th rowspan="1" colspan="1"></th><th align="left" rowspan="1" colspan="1"><bold>TPR</bold></th><th align="left" rowspan="1" colspan="1"><bold>Time (ms)</bold></th><th align="left" rowspan="1" colspan="1"><bold>TPR</bold></th><th align="left" rowspan="1" colspan="1"><bold>Time (ms)</bold></th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">cg</td><td align="left" rowspan="1" colspan="1">0.84</td><td align="left" rowspan="1" colspan="1">−200</td><td align="left" rowspan="1" colspan="1">0.79</td><td align="left" rowspan="1" colspan="1">−310</td></tr><tr><td align="left" rowspan="1" colspan="1">gc</td><td align="left" rowspan="1" colspan="1">0.90</td><td align="left" rowspan="1" colspan="1">−30</td><td align="left" rowspan="1" colspan="1">0.80</td><td align="left" rowspan="1" colspan="1">−140</td></tr><tr><td align="left" rowspan="1" colspan="1">dpm</td><td align="left" rowspan="1" colspan="1">0.66</td><td align="left" rowspan="1" colspan="1">−100</td><td align="left" rowspan="1" colspan="1">0.63</td><td align="left" rowspan="1" colspan="1">−140</td></tr><tr><td align="left" rowspan="1" colspan="1">lg</td><td align="left" rowspan="1" colspan="1">0.85</td><td align="left" rowspan="1" colspan="1">−230</td><td align="left" rowspan="1" colspan="1">0.92</td><td align="left" rowspan="1" colspan="1">−60</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Average ± std</bold></td><td align="left" rowspan="1" colspan="1"><bold>0.81 ± 0.11</bold></td><td align="left" rowspan="1" colspan="1"><bold>−140 ± 92</bold></td><td align="left" rowspan="1" colspan="1"><bold>0.79 ± 0.12</bold></td><td align="left" rowspan="1" colspan="1"><bold>−162 ± 105</bold></td></tr></tbody></table></table-wrap><p>We also compared the earliest time of onset detection using either the pre-selected
channels or the channels chosen with the CVA data driven approach. Stroke subject
<bold>dpm</bold> was excluded from the comparison because of the random results, to
avoid misleading conclusion from the earlier detected intention. Figure <xref ref-type="fig" rid="F13">13</xref> shows the earliest time when movement intention was
detected for the two control subjects and stroke subject <bold>lg</bold>. Differences in
time and performance between the two approaches are not significant.</p><fig id="F13" position="float"><label>Figure 13</label><caption><p><bold>Comparison of detected movement intention when TPR is above chance level
(<italic>p</italic> < 0.05) between using pre-selected channel set and
best selected channel from the data using CVA technique</bold>.</p></caption><graphic xlink:href="fneng-05-00013-g0013"></graphic></fig></sec><sec><title>3.3. Effects of muscular artifacts</title><p>In this section, we are interested in studying how and when movement intention can be
detected from the arm muscular activity. To model the movement class, we take the window
ended at 0 s (between −500 and 0 ms) because the grand averages of EMG activity
showed no movement on average 250 ms before the movement onset.</p><p>Figure <xref ref-type="fig" rid="F14">14</xref> shows the EMG classification results
using the same technique as in the case of EEG for the subjects in Experiment 1. The
results show that movement intention can be detected from EMG activity at a time point
close to the actual onset derived from the button release. Interestingly, the EMG
classifier detects movement intention <italic>after</italic> the thresholding method
(magenta line in Figure <xref ref-type="fig" rid="F14">14</xref>) and
<italic>significantly later</italic> than the EEG classifier. We can thus conclude that
detection of movement intention from EEG signals is not due to muscular artifacts.</p><fig id="F14" position="float"><label>Figure 14</label><caption><p><bold>Single trial detection of movement intention from EMG activity for all subjects
in Experiment 1.</bold> Y-axis of the plots represents movement intention detection
rate.</p></caption><graphic xlink:href="fneng-05-00013-g0014"></graphic></fig><p>Similarly, in Experiment 2 (Figure <xref ref-type="fig" rid="F15">15</xref>) movement
intention can be detected significantly above chance level <italic>only after</italic> the
time obtained from the thresholding method (magenta line in Figure <xref ref-type="fig" rid="F15">15</xref>) for the control subjects, which is in agreement with the results
from Experiment 1. The EMG signal classification for both stroke patients, however,
yielded random level classification, showing that these signals cannot be used to detect
reliably movement intention or onset. Further analysis of other muscles, such as triceps
and deltoid, yielded similar results. The reason for this is that, given the precision and
spatial accuracy required in this task, agonist and antagonist muscles are activated
synergistically to achieve a fine control of the forearm.</p><fig id="F15" position="float"><label>Figure 15</label><caption><p><bold>Single trial detection of movement intention from EMG activity for all subjects
and hands in Experiment 2.</bold> Plots highlighted with a blue frame refers to the
paretic arm of the patients. Y-axis of the plots represents movement intention
detection rate.</p></caption><graphic xlink:href="fneng-05-00013-g0015"></graphic></fig><p>Altogether, these results show that detection of movement intention from EEG signals
occurs before the muscular activation, showing high probability that preparation for
movement happens before the peripheral system reacts and this information could be exploit
for detecting the intent to move. This result is also in line with the behavioral study of
Libet et al. (<xref ref-type="bibr" rid="B40">1983</xref>), where participants in the
experiment reported the conscious intention to act 206 ms before the onset of muscle
activity.</p></sec><sec><title>3.4. Analysis of the non-movement intention period</title><p>Up to now, we have studied the performance of the EEG classifier to detect movement
intention during the preparation period. The results show a quite high
<italic>sensitivity</italic> rate of the EEG decoder. In this section, we analyze the
<italic>specificity</italic> of such an EEG classifier. To do so, we examine the
performance of the proposed method during the non-movement intention period—i.e.,
the time where subjects should not engage in preparing the movement. Figure <xref ref-type="fig" rid="F16">16</xref> shows the rate of trials detected as movement
intention during such a period lasting from –1000 ms before the auditory target
cue until 2000 ms afterwards for all subjects in Experiment 1. Since windows for
classification are 500 ms long, the first decision point is at −500 ms before the
target cue. Interestingly, the detection of movement intention remains significantly below
random level over the whole period preceding the target cue. And, remarkably, this is also
the case during the first 2 s after the target cue (when the subjects should not move) for
five out of eight subjects. The remaining three subjects (<bold>b5</bold>,
<bold>d6</bold>, and <bold>f1</bold>) reached detection rates significantly above random,
but only for a short period of time (less than 250 ms for all three subjects, starting at
750 ms after the target cue for <bold>b5</bold> and <bold>d6</bold>, and at 1000 ms for
<bold>f1</bold>) before they decreased rapidly below chance level again. This may
reflect some form of movement preparation after the subjects were informed of the target
that they suppressed afterwards.</p><fig id="F16" position="float"><label>Figure 16</label><caption><p><bold>Detection of movement intention during the non-movement intention period for
all subjects in Experiment 1.</bold> Time 0 s refers to the delivery of the auditory
target cue. Y-axis of the plots represents movement intention detection rate.</p></caption><graphic xlink:href="fneng-05-00013-g0016"></graphic></fig><p>Regarding Experiment 2, Figure <xref ref-type="fig" rid="F17">17</xref> shows lower
(false) detection rates, at approximately 10%, for both control subjects and one of the
stroke subject, <bold>lg</bold>. Detection rates started rising approximately 1.5 s after
the target cue, with the exception of stroke subject <bold>dpm</bold>, who showed constant
random level performance throughout the entire period. A plausible explanation for this
increase is that, in this experiment, subjects had a large number of trials where the
movement onset was between 2 and 3 s after the target cue, in particular stroke subject
<bold>lg</bold>.</p><fig id="F17" position="float"><label>Figure 17</label><caption><p><bold>Detection of movement intention during the non-movement intention period for
all subjects and hands in Experiment 2.</bold> Time 0 s refers to the delivery of
the visual target cue. Plots highlighted with a blue frame refers to the paretic arm
of the patients. Y-axis of the plots represents movement intention detection rate.</p></caption><graphic xlink:href="fneng-05-00013-g0017"></graphic></fig><p>As a conclusion, our approach demonstrates to have a high sensitivity and a reasonably
good specificity (below random detection level during the non-movement period) to allow
robust single trial detection of movement intention from human EEG.</p></sec></sec><sec id="s4"><title>4. Discussion</title><p>Our experiments, involving healthy subjects and stroke subjects, demonstrate successful
single-trial detection of movement intention from EEG prior to the actual movement in a
self-paced reaching protocol. In particular, we show the detection of self-paced reaching
movement intention in single trials from the analysis of the readiness potential, an EEG
slow potentials that we compute in a narrow frequency range between 0.1 and 1 Hz. In these
experiments, SCPs seem to carry most of the relevant information for the detection of
movement intention as performance is higher than other frequency bands, as shown in Figure
<xref ref-type="fig" rid="F18">18</xref>. In future work we will explore whether coherence
among different EEG frequencies and channels, reflecting the rather complex brain network
involved in this task, could offer further insight into the underlying mechanism of
self-paced movement preparation and improve the performance of the detection.</p><fig id="F18" position="float"><label>Figure 18</label><caption><p><bold>Each pixel refers to the single trial performance of movement intention detection
for all subjects in Experiment 1 using signals filtered in various frequency ranges
(Y-axis).</bold> The dotted line in magenta refers to the EMG activation for each
subject.</p></caption><graphic xlink:href="fneng-05-00013-g0018"></graphic></fig><p>Our SCP approach yields high detection rates close to the movement onset (sensitivity) and
below random detection level during the non-movement period (specificity). Also, movement
intention was detected around 500 ms before actual onset, in agreement with previous studies
on readiness potentials using grand average activity (Kornhuber and Deecke, <xref ref-type="bibr" rid="B35">1965</xref>; Libet et al., <xref ref-type="bibr" rid="B41">1982</xref>). To further increase the performance of our method, in particular its
specificity, we could improve our experimental protocol in order to better model the
non-movement intention period. It would suffice to incorporate null trials (i.e., no
movement trials). We will also explore the use of an evidence accumulation framework
(Perdikis et al., <xref ref-type="bibr" rid="B53">2011</xref>) that have proven beneficial
in BCI as it only issues commands with high probability of confidence levels.</p><p>Previous works on movement onset have focused on hand/wrist flexion only (Awwad Shiekh
Hasan and Gan, <xref ref-type="bibr" rid="B3">2010</xref>, <xref ref-type="bibr" rid="B4">2011</xref>; Bai et al., <xref ref-type="bibr" rid="B6">2011</xref>), without reaching to
a definite goal. Cortical activity is different in both cases. Readiness potentials, and
their associated topography, have been found to be modulated by the consequence of movement,
complexity of the movement, level of skill, sequence of hand movements; as well as the part
of the body performing the movement, force, speed, and precision of a movement (Lang, <xref ref-type="bibr" rid="B38">2003</xref>). In particular, Simonetta et al. (<xref ref-type="bibr" rid="B61">1991</xref>) reported larger amplitudes of the readiness
potential in sequential motor tasks than in simple movements. There is also a larger late BP
in self-paced movement of the proximal than the distal part of the upper extremities
(Jankelowitz and Colebatch, <xref ref-type="bibr" rid="B31">2002</xref>). Finally, different
studies have reported that the attentional level has an influence on the neural correlates
of movement onset. Libet et al. (<xref ref-type="bibr" rid="B41">1982</xref>) showed
differences in the shape of the readiness potential depending on subjects'
strategies, either involvement of general preplanning to act in the near future or direct
movement when subjects were aware of the need to move. The former showed earlier onset
(about 1 s). Keller and Heckhausen (<xref ref-type="bibr" rid="B34">1990</xref>) compared
the readiness potentials between consciously and unconsciously performed motor actions, and
found larger amplitudes in Cz, FCz, and Fz with consciously performed movements.</p><p>A previous study with four stroke subjects (Muralidharan et al., <xref ref-type="bibr" rid="B50">2011</xref>) reported that attempted finger extension could be detected in
stroke subjects with accuracy rates varying across subjects with a maximum true positive
rate of 70% through combinations of PSD in the range of [2–30] Hz. These results,
however, were obtained in a reaction task paradigm where subjects performed the movement or
relaxed in response to a cue. In our study, the average maximum true positive rate was
0.81± 0.11 across both groups, controls and stroke subjects. The performance for one
of the stroke subjects, <bold>dpm</bold>, was slightly above random, with maximum TPR of
0.66, while for another stroke subject, <bold>lg</bold>, the maximum TPR was 0.92 for
reaching trials executed with his paretic hand. Although promising, the results achieved
with stroke patients can only be taken as a preliminary feasibility study because of the
limited number of subjects involved in the study. Nevertheless, it is worth noting that one
of the patients achieved similar performance to the healthy subjects with the paretic
arm.</p><p>In this work we have explored the use of EEG readiness potentials to decode a key aspect of
voluntary movement behavior, namely self-paced onset. But it could also be related to
another critical aspects of voluntary behavior, in particular volitional
inhibition—stopping or changing a planned motor action that is not any more
appropriate to the current context. In fact, Chen et al. (<xref ref-type="bibr" rid="B14">2010</xref>) have found that SMA is involved in both, movement preparation and movement
inhibition. It would be interesting to detect the onset of an inhibitory process in a
reaching version of the countermanding paradigm proposed by Mirabella et al. (<xref ref-type="bibr" rid="B49">2006</xref>, <xref ref-type="bibr" rid="B47">2008</xref>, <xref ref-type="bibr" rid="B48">2011</xref>). In particular, Mirabella et al. (<xref ref-type="bibr" rid="B48">2011</xref>) shows the existence of neurons in the dorsal
premotor cortex exhibiting a pattern of activity compatible with the control of reaching arm
movement initiation and suppression, thus suggesting that motor cortices are the final
target of the inhibitory command elaborated elsewhere. The identification of the inhibitory
process onset, in conjunction with detection of voluntary self-paced movement onset, may
lead to more efficient and natural neuroprosthetics as well as more effective post-stroke
motor rehabilitation training.</p><p>Detection of voluntary movement intention prior to its actual execution is a new capability
that may advance the current state of the art in BCI and neurorehabilitation. For motor
recovery, triggering the robotic-assistive device before EMG activation can largely improve
the outcome of therapy (Muralidharan et al., <xref ref-type="bibr" rid="B50">2011</xref>).
In this case, decoding readiness potentials suits naturally in the design of goal-directed
protocols where patients need to execute purposeful actions, which have been shown to
produce significantly smoother, faster, and more forceful movement than repetitive routine
movement (Trombly and Wu, <xref ref-type="bibr" rid="B65">1999</xref>). In the case of motor
substitution, it will provide a natural signal to enable usual brain control of wheelchairs
and upper limb neuroprostheses while blocking their operation until the subject wishes to do
so. The results reported here are certainly encouraging and can be extended in a couple of
ways for its practical application in a neuroprosthesis. Future work will be devoted to test
the proposed method in an online implementation and perform the analysis with more disabled
users. In particular, subjects could learn to control a robotic arm. It will be interesting
to analyze the learning effects and the stability of the signals during such closed-loop
real-time control applications. Regarding neurorehabilitation, as discussed in the
Introduction, it would be extremely exciting to try our approach in combination with
rehabilitation robotics for motor recovery of spinal cord injury and stroke patients.</p><sec><title>Conflict of interest statement</title><p>The authors declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest.</p></sec></sec></body><back><ack><p>Authors warmly thank G. Garipelli for useful discussions and insights on the analysis of
slow cortical potentials, as well as S. Degallier, L. Tonin, G. Cisotto, and C. Genna for
their precious help with recordings. This work is supported by the European ICT Programme
Project FP7-224631, Swiss NCCR “Robotics”, S. Camillo Hospital Foundation,
and Italian Ministry of Health. This paper only reflects the authors' views and
funding agencies are not liable for any use that may be made of the information contained
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