Narendra LondheAssociate Professor, Department of Electrical Engineering, National Institute of Technology Raipur, India
Speech Title: Weighted ensemble of deep convolution neural networks for single-trial character detection in Devanagari-script-based P300 speller
Abstract: The existing Devanagari-script-input-based P300 speller (DS-P3S) performs better mostly with 3-15 trials. This leads to poor information transfer rate (ITR) and a major concern in its real-time adaptation. In DS-P3S, the display paradigm is a matrix of 8×8 size which has 28 more characters than the 6×6 English paradigm. The increased number of characters leads to user-related issues such as a crowding effect, double flashing, adjacency distraction, task difficulty, and fatigue which increases the false detection rate. To tackle this, we propose an efficient single-trial character detection approach for DS-P3S using weighted ensemble of deep convolution neural networks (WE-DCNNs). The weighted strategy is constructed based on measured ensemble diversity to counter the instability by the individual classifier. Additionally, to reduce the false detection rate arising from a single trial, a new channel dropout-based character detection approach is introduced first time in this article. The ITR of 55.45 b/min and an average P300 classification accuracy of 92.64% achieved are comparatively higher than existing methods of DS-P3S. The significant reduction in tradeoff between bias and variance for the different subjects affirms the ease of applicability of the proposed model with just a single trial.
Biography: Narendra D. Londhe is presently working as Associate Professor in the Department of Electrical Engineering of National Institute of Technology Raipur, Chhattisgarh INDIA. He completed his B.E. from Amravati University in 2000 followed by M.Tech and Ph.D. from Indian Institute of Technology Roorkee in the year 2006 and 2011 respectively. He has 14 years of rich experience in academics and research. He has published more than 150 articles in recognized journals, conferences, and books. His main areas of research include Medical Signal and Image Processing, Biomedical Instrumentation, Speech Signal Processing, Biometrics, Intelligent Healthcare, Brain Computer Interface, Artificial Intelligence and Pattern Recognition. He has been awarded by organizations like Taiwan Society of Ultrasound in Medicine, Ultrasonics Society of India, and NIT Raipur. He is an active member of different recognized societies from his areas of research including senior membership of IEEE.