Abstract
In this paper, a comparative analysis has been done to estimate a robust classifier to classify motor imagery EEG data. First, segment detection and feature extraction have been done over the raw EEG data. Then the frequency domain features have been extracted using FFT. Six classifiers DNN, SVM, KNN, Naïve Bayes,’ Random Forest, and Decision Tree have been considered for this study. The DNN model configured with four layers and used the binary cross-entropy loss function and sigmoid activation function for all layers. The optimizer used is “Adam” having the default-learning rate of 0.001. In this experiment, for the purpose of the estimation of the performance of various classifiers, the experiment used dataset IVa from BCI Competition III, which consisted of EEG signal data for five subjects, namely ‘aa,’ ‘al,’ ‘av,’ ‘aw,’ and ‘ay.’ The highest average accuracy of 70.32% achieved by the DNN model, whereas the model achieved an accuracy of 80.39% over the subject ‘aw.’ The objective of this experiment encompasses the different models for the classification of various motor tasks from EEG signals.
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This work was supported by the Indian Institute of Information Technology Allahabad, UP, India. The authors are grateful for this support.
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Jana, G.C., Shukla, S., Srivastava, D., Agrawal, A. (2021). Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification. In: Dash, S.S., Das, S., Panigrahi, B.K. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 1172. Springer, Singapore. https://doi.org/10.1007/978-981-15-5566-4_12
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