A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography
Identifieur interne : 003156 ( Ncbi/Curation ); précédent : 003155; suivant : 003157A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography
Auteurs : Hong Gi Yeom [Corée du Sud] ; Wonjun Hong [Corée du Sud] ; Da-Yoon Kang [Corée du Sud] ; Chun Kee Chung [Corée du Sud] ; June Sic Kim [Corée du Sud] ; Sung-Phil Kim [Corée du Sud]Source :
- BioMed Research International [ 2314-6133 ] ; 2014.
Abstract
Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.
Url:
DOI: 10.1155/2014/176857
PubMed: 25050324
PubMed Central: 4090526
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University, Seoul 151-742</wicri:regionArea><placeName><settlement type="city">Séoul</settlement></placeName><orgName type="university">Université nationale de Séoul</orgName><placeName><settlement type="city">Séoul</settlement></placeName></affiliation></author><author><name sortKey="Kim, Sung Phil" sort="Kim, Sung Phil" uniqKey="Kim S" first="Sung-Phil" last="Kim">Sung-Phil Kim</name><affiliation wicri:level="1"><nlm:aff id="I4">School of Design and Human Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea</nlm:aff><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>School of Design and Human Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798</wicri:regionArea><wicri:noRegion>Ulsan 689-798</wicri:noRegion></affiliation></author></analytic><series><title level="j">BioMed Research International</title><idno type="ISSN">2314-6133</idno><idno type="eISSN">2314-6141</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Wolpaw, Jr" uniqKey="Wolpaw J">JR Wolpaw</name></author><author><name sortKey="Birbaumer, N" uniqKey="Birbaumer N">N Birbaumer</name></author><author><name sortKey="Mcfarland, Dj" uniqKey="Mcfarland D">DJ McFarland</name></author><author><name sortKey="Pfurtscheller, G" uniqKey="Pfurtscheller G">G Pfurtscheller</name></author><author><name sortKey="Vaughan, Tm" uniqKey="Vaughan T">TM Vaughan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Donoghue, Jp" uniqKey="Donoghue J">JP Donoghue</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Schwartz, Ab" uniqKey="Schwartz A">AB 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