Tetromino pattern based accurate EEG emotion classification model
Article
Article Title | Tetromino pattern based accurate EEG emotion classification model |
---|---|
ERA Journal ID | 5031 |
Article Category | Article |
Authors | Tuncer, Turker, Dogan, Sengul, Baygin, Mehmet and Acharya, U. Rajendra |
Journal Title | Artificial Intelligence in Medicine |
Journal Citation | 123 |
Article Number | 102210 |
Number of Pages | 11 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0933-3657 |
1873-2860 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.artmed.2021.102210 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0933365721002037 |
Abstract | Nowadays, emotion recognition using electroencephalogram (EEG) signals is becoming a hot research topic. The aim of this paper is to classify emotions of EEG signals using a novel game-based feature generation function with high accuracy. Hence, a multileveled handcrafted feature generation automated emotion classification model using EEG signals is presented. A novel textural features generation method inspired by the Tetris game called Tetromino is proposed in this work. The Tetris game is one of the famous games worldwide, which uses various characters in the game. First, the EEG signals are subjected to discrete wavelet transform (DWT) to create various decomposition levels. Then, novel features are generated from the decomposed DWT sub-bands using the Tetromino method. Next, the maximum relevance minimum redundancy (mRMR) features selection method is utilized to select the most discriminative features, and the selected features are classified using support vector machine classifier. Finally, each channel's results (validation predictions) are obtained, and the mode function-based voting method is used to obtain the general results. We have validated our developed model using three databases (DREAMER, GAMEEMO, and DEAP). We have attained 100% accuracies using DREAMER and GAMEEMO datasets. Furthermore, over 99% of classification accuracy is achieved for DEAP dataset. Thus, our developed emotion detection model has yielded the best classification accuracy rate compared to the state-of-the-art techniques and is ready to be tested for clinical application after validating with more diverse datasets. Our results show the success of the presented Tetromino pattern-based EEG signal classification model validated using three public emotional EEG datasets. |
Keywords | Classification; Tetromino; Emotion; EEG; Features; DWT |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Firat University, Turkey |
Ardahan University, Turkiye | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/z1w3y/tetromino-pattern-based-accurate-eeg-emotion-classification-model
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