Brain wave classification using long short-term memory network based OPTICAL predictor

Shiu Kumar,Alok Sharma,Tatsuhiko Tsunoda

doi:10.1038/s41598-019-45605-1

Abstract

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL.

Highlights

Brain-computer interface (BCI) has become a hot topic of research as it is increasingly being used in gaming applications[1] and in stroke rehabilitation[2,3,4,5,6,7] for translating the brain signals of the imagined task into intended movement of the limb that has been paralyzed
We focus on subject-dependent approach and propose an Optimized common spatial pattern (CSP) and long short-term memory (LSTM) based predictor named OPTICAL
We have introduced a new predictor called OPTICAL which utilizes optimized CSP and LSTM network for the classification of EEG signals

Summary

Introduction

Brain-computer interface (BCI) has become a hot topic of research as it is increasingly being used in gaming applications[1] and in stroke rehabilitation[2,3,4,5,6,7] for translating the brain signals of the imagined task into intended movement of the limb that has been paralyzed. Many subject-dependent approaches utilizing multiple frequency bands have been proposed[21,26,27,28,29,30,31,32,33,34,35,36] These methods use multiple filter bands to filter the signal into different sub-bands and utilize CSP for extracting the features. We utilize the rest-state and non-task related EEG signals to show that the proposed method will perform well for real-time classification. For this purpose, we have utilized the one-versus-rest approach (as using the one-versus-rest approach yields substantially better results than using the multi-class classification) for classification of the multi-class MI tasks using the conventional CSP algorithm. For real-time implementation, we achieved an average misclassification rate of 17.78% over 52 subjects using GigaDB dataset

Methods

Results

Discussion

Conclusion