How Many Electrodes are Needed for Multi-target SSVEP-BCI control: Exploring the Minimum number of signal Electrodes for CCA and MEC

As the SSVEP paradigm (based on steady state visual evoked potentials) requires EEG-measurement, high number of EEG electrodes might be impractical in daily life scenarios because of the time consuming electrode montage. Reducing the number of signal electrodes can shorten preparation time but might compromise signal quality. This paper explores the number of signal electrodes required to achieve sufficient control over multitarget SSVEP-based BCI systems. In this respect, two of the most commonly used multi-channel classification methods, the minimum energy combination method (MEC) and the canonical correlation analysis (CCA), are investigated. Data from six healthy subjects recorded during a copy spelling experiment using eight signal electrodes were analyzed off-line. A spelling interface with 30 flickering targets was used. Results for all possible channel combinations were evaluated, revealing that already three electrode channels are sufficient for reliable BCI control.

[1]  Chun-Yen Chang,et al.  Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Xingyu Wang,et al.  Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Piotr Stawicki,et al.  Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI , 2016, Comput. Intell. Neurosci..

[4]  Piotr Stawicki,et al.  Evaluation of Suitable Frequency Differences in SSVEP-Based BCIs , 2015, Symbiotic.

[5]  Ivan Volosyak,et al.  SSVEP-based Bremen–BCI interface—boosting information transfer rates , 2011, Journal of neural engineering.

[6]  T. Jung,et al.  Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Piotr Stawicki,et al.  Exploring the possibilities and limitations of multitarget SSVEP-based BCI applications , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Damien Coyle,et al.  Calibration-less detection of steady-state visual evoked potentials-comparisons and combinations of methods , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[9]  Seungjin Choi,et al.  SSVEP response on Oculus Rift , 2015, The 3rd International Winter Conference on Brain-Computer Interface.

[10]  Ivan Volosyak,et al.  Brain–computer interface using water-based electrodes , 2010, Journal of neural engineering.

[11]  Ivan Volosyak,et al.  Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[12]  Tzyy-Ping Jung,et al.  Measuring Steady-State Visual Evoked Potentials from non-hair-bearing areas , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Wei Wu,et al.  Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs , 2007, IEEE Transactions on Biomedical Engineering.

[14]  Gernot R. Müller-Putz,et al.  Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI , 2008, Journal of Neuroscience Methods.

[15]  Piotr Stawicki,et al.  Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard , 2015, Front. Neurosci..

[16]  Vojkan Mihajlovic,et al.  Dry and Water-Based EEG Electrodes in SSVEP-Based BCI Applications , 2012, BIOSTEC.

[17]  Tzyy-Ping Jung,et al.  Hybrid frequency and phase coding for a high-speed SSVEP-based BCI speller , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Yan Wang,et al.  Visual stimulus design for high-rate SSVEP BCI , 2010 .