Abstract
New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject’s will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance, combining trials across subjects can significantly improve performance compared with when only a single user is involved. Yet, cooperative BCIs may only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. The possible benefits of using several subjects are addressed, and compared with current single-subject BCI paradigms. To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP) based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0.95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications.
Highlights
The field of non-invasive Brain-Computer Interface (BCI) has been active during the last two decades thanks to pioneering works such as the P300 speller [? ], which opened the path to a new research field combining neuroscience, signal processing, machine learning and human-computer interaction
The P300 speller is not the typical cooperative BCI application, the P300 experiment represents the most common paradigm in BCI based on event-related potentials (ERP) detection, and it has become a type of benchmark experiment for evaluating new BCI approaches
The results presented in Figure ??a confirm that aggregating subjects can improve the classification accuracy estimated by Area Under the ROC curve (AUC)
Summary
The field of non-invasive Brain-Computer Interface (BCI) has been active during the last two decades thanks to pioneering works such as the P300 speller [? ], which opened the path to a new research field combining neuroscience, signal processing, machine learning and human-computer interaction. ], which opened the path to a new research field combining neuroscience, signal processing, machine learning and human-computer interaction. Over these years, BCIs have mainly been used as a way to control a computer and to use different type of devices. BCIs have mainly been used as a way to control a computer and to use different type of devices In all these contexts, the interaction between the subject and the devices has always been in relation to the subject’s will. ]. Due to the low signal-to-noise ratio (SNR) of the electroencephalogram (EEG) signal, many strategies have been proposed to increase the SNR, e.g., the combination of several trials over time thanks to the repetition of different events [? Several trials can be recorded for the same event, i.e., from several subjects; several trials can be recorded for different events that have the same meaning, e.g., the presentation at different times of the same item in a P300 speller
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call