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

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.

Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning

“…Abhishek Varshney et al [4] extracted features such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy. This paper used LSTM, BLSTM, and Gated Recurrent Unit(GRU) for classification of the cognitive workload tasks.…”

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

“…These multi-electrode EEG signals captures the brain's electrical activity during mental tasks along both spatial and temporal directions. The researchers have mostly utilised the temporal variations by extracting several feature extraction methods like entropy, cross-correlation [3], energy, and power spectral density [4]- [6]. Signal decomposition Priyanka Mathur, Vijay Kumar Chakka are with Department of Electrical Engineering, Shiv Nadar University, Greater Noida.…”

Fractal Methods and Power Spectral Density as Means to Explore EEG Patterns in Patients Undertaking Mental Tasks

“…Those features are highly dependent on the application and can be related to several different aspects of the original time series, ranging from power, auto-regressive model coefficients, statistical parameters, fractal coefficients, variance, energy, entropy, and others. Particularly for the automated classification of mental arithmetic tasks, nonlinear entropy features from each multi-channel EEG signal have been used [47]. Overall, the model features can be explored either in time or frequency domains depending on the approach chosen, and it may be complicated to compare them with so-called engineered or hand-designed features from other model approaches, since deep learning models often employ features that cannot be immediately identified or extracted from data using other techniques.…”