Rumman Ahmed Prodhan

Rumman Ahmed Prodhan

Lecturer, Department of Computer Science & Engineering

Evaluating the Effectiveness of Classification Algorithms for EEG Sentiment Analysis

Published in Conference: Sentiment Analysis and Deep Learning, ICSADL 2022, 2023

Abstract: Electroencephalogram (EEG) signals from the brain provide additional information about emotional states that we may be unable to convey verbally. Machine learning algorithms can effectively predict the emotion from brain waves. So, we designed research to evaluate the effectiveness of multiple machine learning techniques—Naive Bayes, Logistic Regression, XGBoost, SVM, Decision Tree, Random Forest, KNN, and deep learning models—CNN, LSTM, and Bi-LSTM for classifying sentiment from brain signals. In our experiment, the DEAP dataset is used as a collection of brain signals representing different human sentiments. The Fast Fourier transformation (FFT), which shifts the data to the frequency domain, is used to extract features from the time series EEG data. Among all the algorithms, CNN, KNN, and Random Forest achieved the highest accuracy of 96.64%, 95.8%, and 95.28%, respectively, on the binary classification of valence. The results demonstrate that it is possible to attain accuracy comparable to or even outperform some of the deep learning models by combining appropriate feature extraction techniques (in this case, FFT) with machine learning algorithms.

Authors

Sumya Akter1, Rumman Ahmed Prodhan1, Muhammad Bin Mujib1, Md. Akhtaruzzaman Adnan1, Tanmoy Sarkar Pias2

  1. University of Asia Pacific, Dhaka 1205, Bangladesh
    Email: rumman153@gmail.com, sumyaakter601@gmail.com, binmujib99@gmail.com, adnan.cse@uap-bd.edu
  2. Virginia Tech, Blacksburg, VA 24061, United States
    Email: tanmoysarkar@vt.edu

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Recommended citation: Akter, S., Prodhan, R. A., Mujib, M. B., Adnan, M. A., & Pias, T. S. (2023). Evaluating the Effectiveness of Classification Algorithms for EEG Sentiment Analysis. In *Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022* (pp. 195-212). Singapore: Springer Nature Singapore.