Steady state visual evoked potential detection using Subclass Marginal Fisher Analysis

Recently, SSVEP detection from EEG signals has attracted the interest of the research community, leading to a number of well-tailored methods, such as Canonical Correlation Analysis (CCA) and a number of variants. Despite their effectiveness, due to their strong dependence on the correct calculation of correlations, these methods may prove to be inadequate in front of potential deficiency in the number of channels used, the number of available trials or the duration of the acquired signals. In this paper, we propose the use of Subclass Marginal Fisher Analysis (SMFA) in order to overcome such problems. SMFA has the power to effectively learn discriminative features of poor signals, and this advantage is expected to offer the appropriate robustness needed in order to handle such deficiencies. In this context, we pinpoint the qualitative advantages of SMFA, and through a series of experiments we prove its superiority over the state-of-the-art in detecting SSVEPs from EEG signals acquired with limited resources.

[1]  Xiaorong Gao,et al.  An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.

[2]  Anastasios Tefas,et al.  Subclass Marginal Fisher Analysis , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  Anastasios Tefas,et al.  Graph Embedding Exploiting Subclasses , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[5]  Anastasios Tefas,et al.  Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets , 2011, Neural Networks.

[6]  Yijun Wang,et al.  A high-speed BCI based on code modulation VEP , 2011, Journal of neural engineering.

[7]  L. Angelis,et al.  Exploiting the temporal patterning of transient VEP signals: A statistical single-trial methodology with implications to brain–computer interfaces (BCIs) , 2014, Journal of Neuroscience Methods.

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

[9]  Yijun Wang,et al.  Enhancing detection of steady-state visual evoked potentials using individual training data , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Xiaorong Gao,et al.  Design and implementation of a brain-computer interface with high transfer rates , 2002, IEEE Transactions on Biomedical Engineering.

[11]  Jie Li,et al.  Design of assistive Wheelchair System directly Steered by Human Thoughts , 2013, Int. J. Neural Syst..

[12]  Anastasios Maronidis,et al.  Subclass Graph Embedding and a Marginal Fisher Analysis paradigm , 2015, Pattern Recognit..

[13]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[14]  Yu-Te Wang,et al.  A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials , 2015, PloS one.

[15]  Anastasios Tefas,et al.  Frontal View Recognition Using Spectral Clustering and Subspace Learning Methods , 2010, ICANN.

[16]  Yiannis Kompatsiaris,et al.  Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs , 2016, ArXiv.