Brain-computer interface ({BCI}) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for {EEG}-based {BCIs}. Classic P300-based {BCIs} work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient {BCI} system should be able to understand the user's intentions from the ongoing {EEG} instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous {BCI} and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this {BCI} system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.

Improving Asynchronous Control for P300-based BCI: towards a completely autoadaptative system / Schettini, F.; Aloise, F.; Aricò, P.; Salinari, S.; Mattia, D.; Babiloni, F.; Cincotti, F.. - ELETTRONICO. - 13:(2011), pp. 150-151.

Improving Asynchronous Control for P300-based BCI: towards a completely autoadaptative system

F. Schettini
;
F. Aloise;P. Aricò;S. Salinari;D. Mattia;F. Babiloni;F. Cincotti
2011

Abstract

Brain-computer interface ({BCI}) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for {EEG}-based {BCIs}. Classic P300-based {BCIs} work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient {BCI} system should be able to understand the user's intentions from the ongoing {EEG} instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous {BCI} and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this {BCI} system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.
2011
Brain-Computer Interface, EEG, P300
01 Pubblicazione su rivista::01a Articolo in rivista
Improving Asynchronous Control for P300-based BCI: towards a completely autoadaptative system / Schettini, F.; Aloise, F.; Aricò, P.; Salinari, S.; Mattia, D.; Babiloni, F.; Cincotti, F.. - ELETTRONICO. - 13:(2011), pp. 150-151.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/655452
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