Berlin Brain-Computer Interface : The HCl communication channel for discovery
Identifieur interne : 000937 ( PascalFrancis/Curation ); précédent : 000936; suivant : 000938Berlin Brain-Computer Interface : The HCl communication channel for discovery
Auteurs : Roman Krepki [Allemagne] ; Gabriel Curio [Allemagne] ; Benjamin Blankertz [Allemagne] ; Klaus-Robert Müller [Allemagne]Source :
- International journal of human-computer studies [ 1071-5819 ] ; 2007.
Descripteurs français
- Pascal (Inist)
- Interface utilisateur, Application médicale, Multimédia, Décodage, Jeu vidéo, Intelligence artificielle, Système complexe, Système réparti, Traitement donnée, Contrôle information, Vie artificielle, Charge travail, Cerveau, Moelle épinière, Encéphale, Homme, Système nerveux central, Electroencéphalographie, Modélisation, Jeu ordinateur, Algorithme réparti, Sensibilité tactile, Equipement entrée sortie.
- Wicri :
- topic : Multimédia, Intelligence artificielle, Homme.
English descriptors
- KwdEn :
- Artificial intelligence, Artificial life, Brain, Central nervous system, Complex system, Computer games, Data processing, Decoding, Distributed algorithm, Distributed system, Electroencephalography, Encephalon, Human, Information control, Input output equipment, Medical application, Modeling, Multimedia, Spinal cord, Tactile sensitivity, User interface, Video game, Workload.
Abstract
The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain-Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies.
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Pascal:07-0221497Le document en format XML
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<front><div type="abstract" xml:lang="en">The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain-Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies.</div>
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<fC01 i1="01" l="ENG"><s0>The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain-Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies.</s0>
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</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Sistema complejo</s0>
<s5>12</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Système réparti</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Distributed system</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Sistema repartido</s0>
<s5>13</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Traitement donnée</s0>
<s5>14</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Data processing</s0>
<s5>14</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Tratamiento datos</s0>
<s5>14</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Contrôle information</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Information control</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Control información</s0>
<s5>15</s5>
</fC03>
<fC03 i1="11" i2="3" l="FRE"><s0>Vie artificielle</s0>
<s5>16</s5>
</fC03>
<fC03 i1="11" i2="3" l="ENG"><s0>Artificial life</s0>
<s5>16</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Charge travail</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Workload</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Carga trabajo</s0>
<s5>17</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Cerveau</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Brain</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Cerebro</s0>
<s5>18</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Moelle épinière</s0>
<s5>19</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Spinal cord</s0>
<s5>19</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Médula espinal</s0>
<s5>19</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Encéphale</s0>
<s5>20</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Encephalon</s0>
<s5>20</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Encéfalo</s0>
<s5>20</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Homme</s0>
<s5>21</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Human</s0>
<s5>21</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Hombre</s0>
<s5>21</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Système nerveux central</s0>
<s5>22</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Central nervous system</s0>
<s5>22</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Sistema nervioso central</s0>
<s5>22</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Electroencéphalographie</s0>
<s5>23</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Electroencephalography</s0>
<s5>23</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Electroencefalografía</s0>
<s5>23</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>24</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Modeling</s0>
<s5>24</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Modelización</s0>
<s5>24</s5>
</fC03>
<fC03 i1="20" i2="3" l="FRE"><s0>Jeu ordinateur</s0>
<s5>25</s5>
</fC03>
<fC03 i1="20" i2="3" l="ENG"><s0>Computer games</s0>
<s5>25</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE"><s0>Algorithme réparti</s0>
<s5>26</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG"><s0>Distributed algorithm</s0>
<s5>26</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA"><s0>Algoritmo repartido</s0>
<s5>26</s5>
</fC03>
<fC03 i1="22" i2="X" l="FRE"><s0>Sensibilité tactile</s0>
<s5>27</s5>
</fC03>
<fC03 i1="22" i2="X" l="ENG"><s0>Tactile sensitivity</s0>
<s5>27</s5>
</fC03>
<fC03 i1="22" i2="X" l="SPA"><s0>Sensibilidad tactil</s0>
<s5>27</s5>
</fC03>
<fC03 i1="23" i2="X" l="FRE"><s0>Equipement entrée sortie</s0>
<s5>41</s5>
</fC03>
<fC03 i1="23" i2="X" l="ENG"><s0>Input output equipment</s0>
<s5>41</s5>
</fC03>
<fC03 i1="23" i2="X" l="SPA"><s0>Equipo entrada salida</s0>
<s5>41</s5>
</fC03>
<fN21><s1>155</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
<pR><fA30 i1="01" i2="1" l="ENG"><s1>Workshop on AmI for Scientific Discovery</s1>
<s3>Vienna AUT</s3>
<s4>2004-04-25</s4>
</fA30>
</pR>
</standard>
</inist>
</record>
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