Abstract
Accurate recognition of human emotions through EEG data is of great significance in human-computer interaction, mental health, intelligent medical care and other fields. EEG signal contains a large number of meaningful and extractable features. Therefore, effective feature selection plays an essential role in reducing feature dimensions and avoiding redundancy. In order to select the emotion related features from hundreds of features and achieve better emotion recognition results, we propose an enhanced firefly algorithm (EFA) for EEG emotion recognition, which is based on brightness-distance based attraction and roulette-based local search strategies. Then, we apply EFA to select features for EEG emotion recognition and provide a novel encoding method of fireflies to distinguish the importance of channels and bands respectively. We conduct comparative experiments to evaluate the performance of EFA on DEAP database. The experimental results confirm the superiority of the proposed method in AUC score.
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Acknowledgement
This study is supported by National Natural Science Foundation of China (71901150, 71901143), Natural Science Foundation of Guangdong (2022A1515012077), Guangdong Province Innovation Team “Intelligent Management and Interdisciplinary Innovation” (2021WCXTD002), Shenzhen Higher Education Support Plan (20200826144104001).
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Niu, B. et al. (2023). Channel Selection for EEG Emotion Recognition via an Enhanced Firefly Algorithm with Brightness-Distance Attraction. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_15
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DOI: https://doi.org/10.1007/978-3-031-20102-8_15
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