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ABSTRACT
P300 speller-based Brain–Computer Interface (BCI) is a direct communication from human brain to computer machine that is based on the decoding of brain responses generated by stimulation of a subject using P300 speller paradigm. This communication does not require any muscular movements. A novel Devanagari script (DS) input-based P300 speller system is proposed in this article. A novel 8 × 8 matrix consisting of Devanagari characters, digits, and special symbols has also been proposed as a DS paradigm. The character set associated with the DS paradigm is comparatively larger than the standard 6 × 6 English Row/Column (RC) paradigm. The problems related to crowding effect, fatigue, and task difficulty increase while using the DS paradigm for the P300 speller. This leads to either exhaustive or false detection of characters. In order to overcome these problems, a novel Weighted Ensemble of Support Vector Machines (WESVM)-based method has been proposed for classification. Further improvement in the system performance in terms of accuracy and reduced computational cost has been achieved by employing a binary Differential Evolution (DE) method for optimal channel selection. The proposed method has been tested on the EEG data collected from nine subjects using the DS paradigm. An average accuracy of 94.2% was achieved when the WESVM method was applied with the binary DE-based channel selection method.
Acknowledgments
The authors are sincerely thankful to the editor and the anonymous reviewers for their valuable comments and suggestions that helped improve the quality of the article. We would also like to thank the people associated with MILE Lab and Primates Research Lab of IISc, Bangalore, India, for providing us the necessary support and facilities for recording the dataset.
Funding
Authors acknowledge Department of Science and Technology, Government of India, for financial support vide Reference No. SR/CSRI/38/2015 (G) under Cognitive Science Research Initiative (CSRI) to carry out this work.
Additional information
Funding
Notes on contributors
Rahul Kumar Chaurasiya
Rahul Kumar Chaurasiya received his BTech degree from the MANIT, Bhopal, India, in 2009. He received his ME degree from the department of Electrical Engineering, IISc, Bangalore, India, in 2011. After receiving his ME degree, he joined Brocade communications as senior software engineer in Bangalore. In 2013, he joined NIT, Raipur, as assistant professor. Currently, he is also working toward his PhD degree from the same institute. His current research area includes pattern recognition, signal processing, optimization, and brain–computer interfacing.
Narendra D. Londhe
Narendra D. Londhe received his MTech and PhD degrees from the departments of EE, IIT, Roorkee, in 2004 and 2011, respectively. He is currently an assistant professor at the department of EE, NIT, Raipur. His research area includes medical signal & image processing, machine learning, pattern recognition, and medical ultrasound. He has published more than 40 research articles in the aforementioned areas. He is a senior member of IEEE and life member of several other technical societies.
Subhojit Ghosh
Subhojit Ghosh received his ME degree from BIT, Mesra, in 2003 and his PhD degree from the department of EE, IIT Kharagpur, in 2010. He has been working as assistant professor at department of EE, NIT Raipur, from 2013. He has also worked as assistant professor in BIT Mesra and NIT Rourkela for 8 and 2 years, respectively. His research area includes control system, optimization, soft computing, and biomedical engineering. He has published more than 40 research papers in the aforementioned areas.