Low channel count montages using sensor tying for VEP-based BCI

OBJECTIVE Brain Computer Interfaces (BCIs) are slowly making their appearance on the consumer market, accompanied by a higher popularity among the general public. This new group of users requires easy-to-use headsets with robustness to non-precise placement. In this paper, an optimized fixed montage EEG headset for VEP BCIs is proposed. APPROACH The proposed layout covers only the most relevant area with large sensors to account for slight misplacement. To obtain large sensors, without having them physically available, we tie multiple sensors together and simulate the effect by averaging the signal of multiple sensors. MAIN RESULTS In simulations based on recorded 256-channel EEG data, it is shown that a circular center-surround configuration with sensor tying, leading to only 8 channels covering a large part of the occipital lobe, can provide high performance and good robustness to misplacement. Automatically optimized layouts were unable to achieve better performance, demonstrating the utility of this manual design. Finally, the performance and benefits of sensor tying in the manual design are then validated in a physical experiment. SIGNIFICANCE The resulting proposed layout fulfills most requirements of an easy to use consumer EEG headset.

[1]  Ronald Phlypo,et al.  EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[2]  N. Birbaumer,et al.  A brain–computer interface tool to assess cognitive functions in completely paralyzed patients with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[3]  Ashverya Laxmi,et al.  DUF581 Is Plant Specific FCS-Like Zinc Finger Involved in Protein-Protein Interaction , 2014, PloS one.

[4]  Gabriel Curio,et al.  Brain-computer communication and slow cortical potentials , 2004, IEEE Transactions on Biomedical Engineering.

[5]  F. Perrin,et al.  Spherical splines for scalp potential and current density mapping. , 1989, Electroencephalography and clinical neurophysiology.

[6]  A. van Oosterom,et al.  The surface Laplacian of the potential: theory and application , 1996, IEEE Transactions on Biomedical Engineering.

[7]  Fabio Babiloni,et al.  The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability , 2019, Sensors.

[8]  Sundeep Prabhakar Chepuri,et al.  Sparsity-Promoting Sensor Selection for Non-Linear Measurement Models , 2013, IEEE Transactions on Signal Processing.

[9]  D. Creel,et al.  Visually evoked potentials. , 2019, Handbook of clinical neurology.

[10]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  Peter Desain,et al.  Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing , 2015, PloS one.

[12]  Daniel Sánchez Morillo,et al.  Dry EEG Electrodes , 2014, Sensors.

[13]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[14]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[15]  Tzyy-Ping Jung,et al.  Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review , 2010, IEEE Reviews in Biomedical Engineering.

[16]  Michael Bensch,et al.  Design and Implementation of a P300-Based Brain-Computer Interface for Controlling an Internet Browser , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  Stephen P. Boyd,et al.  Sensor Selection via Convex Optimization , 2009, IEEE Transactions on Signal Processing.

[18]  J. Cohen,et al.  P300, stimulus intensity, modality, and probability. , 1996, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[19]  Yijun Wang,et al.  VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier] , 2009, IEEE Computational Intelligence Magazine.

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

[21]  R. Gold,et al.  Optimal binary sequences for spread spectrum multiplexing (Corresp.) , 1967, IEEE Trans. Inf. Theory.

[22]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[23]  D. Tucker,et al.  Scalp electrode impedance, infection risk, and EEG data quality , 2001, Clinical Neurophysiology.

[24]  A. Kübler,et al.  The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications , 2014, PloS one.