You Are Wrong! - Automatic Detection of Interaction Errors from Brain Waves

Brain-computer interfaces, as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. In this paper we exploit a unique feature of the "brain channel", namely that it carries information about cognitive states that are crucial for a purposeful interaction. One of these states is the awareness of erroneous responses. Different physiological studies have shown the presence of error-related potentials (ErrP) in the EEG recorded right after people get aware they have made an error. However, for human-computer interaction, the central question is whether ErrP are also elicited when the error is made by the interface during the recognition of the subject's intent and no longer by errors of the subject himself. In this paper we report experimental results with three volunteer subjects during a simple human-robot interaction (i.e., bringing the robot to either the left or right side of a room) that seem to reveal a new kind of ErrP, which is satisfactorily recognized in single trials. These recognition rates significantly improve the performance of the brain interface.

[1]  P. Sajda,et al.  Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  José del R. Millán,et al.  Brain-Computer Interfaces , 2020, Handbook of Clinical Neurology.

[3]  José del R. Millán,et al.  Non-invasive estimation of local field potentials for neuroprosthesis control , 2005, Cognitive Processing.

[4]  J. Hohnsbein,et al.  ERP components on reaction errors and their functional significance: a tutorial , 2000, Biological Psychology.

[5]  José del R. Millán,et al.  Brain-actuated interaction , 2004, Artif. Intell..

[6]  Christoph M. Michel,et al.  Electrical neuroimaging based on biophysical constraints , 2004, NeuroImage.

[7]  M. Botvinick,et al.  Anterior cingulate cortex, error detection, and the online monitoring of performance. , 1998, Science.

[8]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[9]  José del R. Millán,et al.  Non-Invasive Brain-Actuated Control of a Mobile Robot , 2003, IJCAI.

[10]  G Pfurtscheller,et al.  EEG-based communication: improved accuracy by response verification. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[12]  K.-R. Muller,et al.  Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[14]  Clay B. Holroyd,et al.  The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. , 2002, Psychological review.

[15]  J. Davenport Editor , 1960 .

[16]  D.J. McFarland,et al.  The Wadsworth Center brain-computer interface (BCI) research and development program , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[18]  G. Pfurtscheller,et al.  EEG-based communication: presence of an error potential , 2000, Clinical Neurophysiology.

[19]  N. Yeung,et al.  Anterior Cingulate Cortex , 2002 .

[20]  J. Allman,et al.  The Anterior Cingulate Cortex , 2001, Annals of the New York Academy of Sciences.