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• W2762323924 startingPage "143" @default.
• W2762323924 abstract "There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications." @default.
• W2762323924 created "2017-10-20" @default.
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• W2762323924 date "2018-03-01" @default.
• W2762323924 modified "2023-10-05" @default.
• W2762323924 title "Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors" @default.
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• W2762323924 doi "https://doi.org/10.1016/j.eswa.2017.09.062" @default.
• W2762323924 hasPublicationYear "2018" @default.
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