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Classification of steady state visual evoked potentials by Multi-Class T-Weight Method

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Abstract

In this paper, Multi-Class T-Weight Method (MCTW) is presented for classification in brain-computer interface (BCI) systems. Proposed method is an extension of the existing Improved T-Weight method for multi-class problems. The method was tested on the frequency and correlation based features obtained from electroencephalogram data of 20 Subjects in a steady state visual evoked potential (SSVEP) based offline BCI classification task. Obtained classification performances with different classifiers show that the MCTW method compete with the other well-known classifiers like linear discriminant analysis (LDA) and support vector machines (SVMs). Therefore, it can be used in classifying SSVEP based electroencephalogram data with proper features.

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Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, Stony Brook University, Stony Brook, NY, 11794, USA

    Z. Iscan & Z. Dokur

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  1. Z. Iscan
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  2. Z. Dokur
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Correspondence to Z. Iscan.

Additional information

This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, Russia, September 23–28, 2013.

The article is published in the original.

Zafer Işcan was born in 1981. He received his B.Sc. degree in Electronics and Communication Eng. in 2002, M. Sc. degree in Biomedical Eng. in 2005 and Ph.D. degree in 2012, all from Istanbul Technical University, Turkey. He worked as a research assistant at Department of Electronics and Communication Engineering at the same university from 2005 to 2012. Currently, he is a postdoctoral associate at the Department of Psychiatry at Stony Brook University, New York, USA. His main research areas include signal processing, pattern recognition and brain computer interfaces. He is the owner of the 2nd prize in IEEE MLSP 2010 Competition for Data classification. He has 8 journals, 4 international and 6 national conference papers.

Zümray Dokur was born in 1970. She received her B.Sc. degree in 1992, M.Sc. degree in 1995, and Ph.D. degree in 2000, all in Electronics and Communication Eng. from Istanbul Technical University. Since 1992, she has been with the Department of Electronics and Communication Engineering at the same university where at present she is a professor. Her research interests include applications of neural networks and genetic algorithms to pattern recognition, biomedical signal processing, image processing and computer vision. She has 28 sci., 3 non-sci. journal papers. Besides, 22 international and 20 national conference papers.

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Iscan, Z., Dokur, Z. Classification of steady state visual evoked potentials by Multi-Class T-Weight Method. Pattern Recognit. Image Anal. 25, 321–326 (2015). https://doi.org/10.1134/S1054661815020121

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