A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach

Abstract

Automated eyes-state classification from the EEG signals using nonlinear analysis tools is a new area of research. Based upon the theory of nonlinear analysis, recurrence plots (RPs) and recurrence quantification analysis (RQA) are of greater significance that help in understanding the chaotic and recurrence behavior of the dynamically occurring complex physiological signals. In our study, a novel method is proposed combining the RPs with the machine learning based algorithms for automated classification of EEG signals into eyes-open and eyes-close states. A huge dataset of 109 subjects has been acquired from the PhysioNet database. Each of the six RQA-based measures (recurrence rate, determinism, entropy, laminarity, trapping time, and longest vertical line)has been extracted from 64 EEG channels. Feature selection has been performed using genetic algorithm. The selected features have been averaged and combined to form a six-dimensional input vector which shows statistically significant differences (p < 0.01) between the two states. It is fed to different machine learning based algorithms such as logistic-regression, support vector machine, random forest, k-nearest neighbor, Gaussian naïve Bayes, and adaptive boosting. Logistic regression achieves the highest performance results in terms of accuracy, F1 score, precision, recall, and specificity of 97.27%, 97.17%, 98.26%, 96.36%, and 98.18%, respectively, with the least testing time of the model as 2.52 ms. Therefore, our method might be of greater significance in the development of the practical applications.

Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen–Shannon Distance

“…Even when the attenuation of the alpha waves over the human posterior cortex in the EO condition is a well-known recognized fact for almost a century [14], different approaches have more recently been proposed to obtain an improved discrimination and classification of these baseline brain states [2,7,[15][16][17][18][19]. It has been found, for example, that different entropy measures (permutation entropy, approximate entropy, multiscale entropy, spatial permutation entropy) achieve higher values in the EO condition compared with the EC one [2,16,18,20,21].…”

Eye state recognition based on continuous wavelet transform and improved convolutional neural network

“…Abolfazl Saghafi et al adopted common spatial patterns and the multiple empirical mode decomposition algorithm to extract features from EEG signals, and combined them with machine learning algorithms such as a support vector machine to realize recognition of eye state [26,27]. Ashima Khosla et al adopted genetic algorithm for feature extraction and tested the performance of various machine learning algorithms in eye state recognition [28].…”