Error-related EEG potentials in brain-computer interfaces

People with severe motor disabilities (spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), etc.) but with intact brain functions are somehow prisoners of their own body. They need alternative ways of communication and control to interact with their environment in their everyday life. These new tools are supposed to increase their quality of life by giving these people the opportunity to recover part of their independence. Therefore, these alternative ways have to be reliable and ergonomic to be successfully used by disabled people. Over the past two decades, numerous studies proposed electroencephalogram (EEG) activity for direct brain-computer interaction. EEG-based brain-computer interfaces (BCIs) provide disabled people with new tools for control and communication and are promising alternatives to invasive methods. However, as any other interaction modality based on physiological signals and body channels (muscular activity, speech and gestures, etc.), BCIs are prone to errors in the recognition of subject's intent, and those errors can be frequent. Indeed, even well-trained subjects rarely reach 100% of success. In contrast to other interaction modalities, a unique feature of the brain channel is that it conveys both information from which we can derive mental control commands to operate a brain-actuated device as well as information about cognitive states that are crucial for a purposeful interaction, all this on the millisecond range. One of these states is the awareness of erroneous responses, which a number of groups have recently proposed as a way to improve the performance of BCIs. However, most of these studies propose the use of error-related potentials (ErrP) following an error made by the subject himself. This thesis first describes a new kind of ErrP, the so-called interaction ErrP, that are present in the ongoing EEG following an error of the interface and no longer errors of the subject himself. More importantly, these ErrP are satisfactorily detected no more in grand averages but at the level of single trials. Indeed, the classification rates of both error and correct single trials based on error-potentials detection are on average 80%. At this level it becomes possible to introduce a kind of automatic verification procedure in the BCI: after translating the subject's intention into a control command, the BCI provides a feedback of that command, but will not transfer it to the device if ErrP follow the feedback. Experimental results presented in this thesis confirm that this new protocol greatly increases the reliability of the BCI. Furthermore, this tool turns out to be of great benefit especially for beginners who normally reach moderate performances. Indeed, filtering out wrong responses increases the user's confidence in the interface and thus accelerates mastering the control of the brain-actuated device. The second issue explored in this thesis is the practical integration of ErrP detection in a BCI. Indeed, providing a first feedback of the subject's intent, as recognized by the BCI, before eventually sending the command to the controlled device, induces additional information to process by the subject and may considerably slow down the interaction since the introduction of an automatic response rejection strongly interferes with the BCI. However, this study shows the feasibility of simultaneously and satisfactorily detecting erroneous responses of the interface and classifying motor imagination for device control at the level of single trials. The integration of an automatic error detection procedure leads to great improvements of the BCI performance. Another aspect of this thesis is to investigate the potential benefit of using neurocognitive knowledge to increase the classification rate of ErrP, and more generally the performance of the BCI. Recent findings have uncovered that ErrP are most probably generated in a deep fronto-central brain area called anterior cingulate cortex (ACC). This hypothesis is verified using a well-known inverse model called sLORETA. Indeed, the localization provided for ErrP shows clear foci of activity both in the ACC and the pre-supplementary motor area (pre-SMA). The localization results using the cortical current density (CCD) model are very similar and more importantly, this model outperforms EEG for ErrP classification. Thanks to its stability, this model is likely to be successfully used in a BCI framework. The ELECTRA model for estimating local field potentials is also tested, but classification and localization results using this method are not so encouraging. More generally, the work described here suggests that it could be possible to recognize in real time high-level cognitive and emotional states from EEG (as opposed, and in addition, to motor commands) such as alarm, fatigue, frustration, confusion, or attention that are crucial for an effective and purposeful interaction. Indeed, the rapid recognition of these states will lead to truly adaptive interfaces that customize dynamically in response to changes of the cognitive and emotional/affective states of the user.