Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

Abhishek Varshney,U Rajendra Acharya,Sibasankar Padhy,Rajesh Kumar Tripathy,Samit Kumar Ghosh

doi:10.3390/electronics10091079

Abstract

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC); and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications.

Highlights

The amount of mental effort performed by each person in response to certain cognitive tasks is called the cognitive workload [1]
We discuss the statistical analysis of the selected entropy features for bad mental arithmetic calculations (BMAC) vs. good mental arithmetic calculations (GMAC) and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC) classification tasks and the results of classification using recurrent neural network (RNN)-based models
Box-plots showing the within-class variations for the Fp1-channel EEG signal dispersion entropy, F7-channel EEG signal slope entropy, C4-channel EEG signal approximation entropy, O1-channel EEG signal sample entropy, and O1-channel EEG signal permutation entropy features for the BFMAC vs. DMAC classification task are shown in Figure 8a–e, respectively

Summary

Introduction

The amount of mental effort performed by each person in response to certain cognitive tasks is called the cognitive workload [1]. Fatimah et al [13] extracted the L2 norm, mean, Shannon entropy, and energy features from the rhythms of each EEG channel They employed a support vector machine (SVM) classifier for the classification of the cognitive workload classes, such as the before mental arithmetic calculation (BFMAC) or rest state and during mental arithmetic calculation (DMAC) or the active state. Electronics 2021, 10, 1079 tures have been used for various applications, such as the detection of generalized and partial epileptic seizures [17,18,19], emotion recognition [20], and brain-computer interface (BCI) [21] applications These non-linear entropy features, such as dispersion entropy [22], slope entropy [23], and other entropy measures [24,25], have not been explored for the mental-arithmetic-based cognitive task classification using EEG signals.

Multi-Channel EEG Database for Mental Arithmetic Tasks

Method

Segmentation of Multi-Channel EEG Recordings

Non-Linear Entropy Features

Results and Discussion

Conclusions