A Collaborative Representation Approach to Detecting Error-Related Potentials in SSVEP-BCIs

This study takes advantage of Error Related Potentials, a certain type of neurophysiological event associated with humans' ability to observe and recognize erroneous actions, in order to improve SSVEP-based Brain Computer Interfaces (BCIs). The Error Related Potentials serve as a passive correction mechanism that originates directly from the user's brain. In this paper we propose a novel approach to spatial filtering, based on a supervised variant of Collaborative Representation Projections (CRP) offering a more discriminant representation of electroencephalography signals for detecting Error Related Potentials. This new approach enhances the detectability of Error Related Potentials by projecting the spatial information of signals into a new space where samples of the same class tend to form local neighborhoods. Moreover, the limitations under which the Error Related Potentials positively contribute to the performance of a SSVEP-based BCI are explored. For this reason we also provide a new methodology, namely Inverse Correct Response Time (ICRT), that reliably captures the trade-off, between the gain of the automated error detection and the induced time delay of a BCI system that potentially incorporates Error Related Potentials.

[1]  Yijun Wang,et al.  Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[2]  Rajesh P. N. Rao Brain-Computer Interfacing: Major Types of BCIs , 2013 .

[3]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[4]  N. Birbaumer Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[5]  José del R. Millán,et al.  Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy , 2008 .

[6]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  J. Hohnsbein,et al.  Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks. , 1991, Electroencephalography and clinical neurophysiology.

[8]  Christian Kothe,et al.  Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.

[9]  Guillaume Gibert,et al.  xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.

[10]  Wolfgang Rosenstiel,et al.  Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning , 2012, PloS one.

[11]  T. Lagerlund,et al.  Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[12]  Donald A. Wilson,et al.  Habituation mechanisms and their importance for cognitive function , 2015, Front. Integr. Neurosci..

[13]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[14]  Ivan Volosyak,et al.  Impact of Frequency Selection on LCD Screens for SSVEP Based Brain-Computer Interfaces , 2009, IWANN.

[15]  D. Regan Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine , 1989 .

[16]  Martin Spüler,et al.  Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity , 2015, Front. Hum. Neurosci..

[17]  Guillaume Gibert,et al.  OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.

[18]  Francesco Piccione,et al.  User adaptive BCIs: SSVEP and P300 based interfaces , 2003, PsychNology J..

[19]  Anton Nijholt,et al.  BCI for Games: A 'State of the Art' Survey , 2008, ICEC.

[20]  Adrian R. Willoughby,et al.  The Medial Frontal Cortex and the Rapid Processing of Monetary Gains and Losses , 2002, Science.

[21]  Ricardo Chavarriaga,et al.  Errare machinale est: the use of error-related potentials in brain-machine interfaces , 2014, Front. Neurosci..

[22]  José del R. Millán,et al.  Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction , 2008, IEEE Transactions on Biomedical Engineering.

[23]  Christa Neuper,et al.  Implementation of Error Detection into the Graz-Brain-Computer Interface, the Interaction Error Potential , 2009 .

[24]  Fabien Lotte,et al.  Brain-Computer Interfaces: Beyond Medical Applications , 2012, Computer.

[25]  Takayuki Suyama,et al.  Fixed low-rank EEG spatial filter estimation for emotion recognition induced by movies , 2016, 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

[26]  P. Suffczynski,et al.  On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions , 2013, PloS one.

[27]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[28]  Zhenyu Wang,et al.  A collaborative representation based projections method for feature extraction , 2015, Pattern Recognit..

[29]  Clay B. Holroyd,et al.  Error-related scalp potentials elicited by hand and foot movements: evidence for an output-independent error-processing system in humans , 1998, Neuroscience Letters.

[30]  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.

[31]  Anthony J. Ries,et al.  Best practice for single-trial detection of event-related potentials: Application to brain-computer interfaces. , 2017, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[32]  M. Matteucci,et al.  The Utility Metric: A Novel Method to Assess the Overall Performance of Discrete Brain–Computer Interfaces , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  Yijun Wang,et al.  Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.

[34]  Tiago H. Falk,et al.  Recent advances and open challenges in hybrid brain-computer interfacing: a technological review of non-invasive human research , 2016 .

[35]  Wolfgang Rosenstiel,et al.  Online use of error-related potentials in healthy users and people with severe motor impairment increases performance of a P300-BCI , 2012, Clinical Neurophysiology.

[36]  Sadasivan Puthusserypady,et al.  Spatial filter feature extraction methods for P300 BCI speller: A comparison , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[37]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..

[38]  Rajesh P. N. Rao Brain-Computer Interfacing: An Introduction , 2010 .