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• W3005283300 abstract "Intelligent recognition of electroencephalogram (EEG) signals has been an important means to recognize emotions. Traditional user-independent method, which treat <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>s</sup> each individual's EEG data as independent and identically distributed (i.i.d.) samples and ignores destruction on i.i.d. condition caused by individual differences, usually has lower generalization performance. Although user-dependent method could alleviate abovementioned problem, it faces difficulty in collection of sufficient training EEG data for each individual. In order to construct user-dependent model merely based on a small amount of training EEG data, we incorporate transfer learning framework and propose a individual similarity guided transfer modeling method for EEG-based emotion recognition. We first measure the similarities between individuals using maximum mean discrepancy (MMD), then utilize pre-existing EEG data of similar individuals to assist construction of user-dependent model for the target individual using an instance-based transfer learning algorithm named TrAdaBoost. We compared this method with traditional user-independent and user-dependent methods on DEAP dataset. Experimental results showed that our method could transfer useful knowledge from other individuals for user-dependent emotion recognition, which achieved classification accuracies of 66.1% and 66.7% on arousal and valence dimentions, respectively." @default.
• W3005283300 created "2020-02-14" @default.
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• W3005283300 date "2019-11-01" @default.
• W3005283300 modified "2023-10-16" @default.
• W3005283300 title "Individual Similarity Guided Transfer Modeling for EEG-based Emotion Recognition" @default.
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• W3005283300 doi "https://doi.org/10.1109/bibm47256.2019.8982972" @default.
• W3005283300 hasPublicationYear "2019" @default.
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