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LSTM multichannel neural networks in mental task classification

Sławomir Opałka (Institute of Information Technology, Faculty of Technical Physics, Information Technology and Applied Mathematics, Lodz University of Technology, Lodz, Poland)
Dominik Szajerman (Institute of Information Technology, Politechnika Lodzka, Lodz, Poland)
Adam Wojciechowski (Institute of Information Technology, Faculty of Technical Physics, Information Technology and Applied Mathematics, Politechnika Lodzka, Lodz, Poland)

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering

ISSN: 0332-1649

Article publication date: 25 June 2019

Issue publication date: 14 August 2019

Abstract

Purpose

The purpose of this paper is to apply recurrent neural networks (RNNs) and more specifically long-short term memory (LSTM)-based ones for mental task classification in terms of BCI systems. The authors have introduced novel LSTM-based multichannel architecture model which proved to be highly promising in other fields, yet was not used for mental tasks classification.

Design/methodology/approach

Validity of the multichannel LSTM-based solution was confronted with the results achieved by a non-multichannel state-of-the-art solutions on a well-recognized data set.

Findings

The results demonstrated evident advantage of the introduced method. The best of the provided variants outperformed most of the RNNs approaches and was comparable with the best state-of-the-art methods.

Practical implications

The approach presented in the manuscript enables more detailed investigation of the electroencephalography analysis methods, invaluable for BCI mental tasks classification.

Originality/value

The new approach to mental task classification, exploiting LSTM-based RNNs with multichannel architecture, operating on spatial features retrieving filters, has been adapted to mental tasks with noticeable results. To the best of the authors’ knowledge, such an approach was not present in the literature before.

Keywords

Citation

Opałka, S., Szajerman, D. and Wojciechowski, A. (2019), "LSTM multichannel neural networks in mental task classification", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 38 No. 4, pp. 1204-1213. https://doi.org/10.1108/COMPEL-10-2018-0429

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited