Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning

The performance of an existing Devanagari script (DS) input-based P300 speller with conventional machine learning techniques suffers from low information transfer rate (ITR). This occurs due to its required large size of display, i.e., 8 × 8 row-column (RC) paradigm which exhibits issues like crowdi...

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Vydané v:	IEEE transactions on biomedical engineering Ročník 66; číslo 11; s. 2992 - 3005
Hlavní autori:	Kshirsagar, G. B., Londhe, N. D.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adult Algorithms Artificial intelligence Artificial neural networks Brain modeling Brain-computer interface (BCI) Brain-Computer Interfaces Character recognition Communication Aids for Disabled Convolution Data acquisition deep convolution neural network (DCNN) Deep Learning Devanagari script (DS) EEG Electroencephalography Electroencephalography - methods Event-related potentials Event-Related Potentials, P300 - physiology Female Humans Information transfer Learning algorithms Machine learning Machine learning algorithms Male Middle Aged Neural networks P300 speller Signal Processing, Computer-Assisted stacked autoencoder (SAE) Support vector machines Target detection Training Young Adult
ISSN:	0018-9294, 1558-2531, 1558-2531
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