Affective computation on EEG correlates of emotion from musical and vocal stimuli

Affective interface that acquires and detects the emotion of the user can potentially enhance the human-computer interface experience. In this paper, an affective brain-computer interface (ABCI) is proposed to perform affective computation on electroencephalogram (EEG) correlates of emotion. The pro...

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Bibliographic Details
Main Authors: Khosrowabadi, R., Wahab, A., Kai Keng Ang, Baniasad, M.H.
Format: Conference Proceeding
Language:eng ; jpn
Subjects:
Brain computer interfaces
Classification algorithms
Computer interfaces
Electroencephalography
Emotion recognition
Feature extraction
Heart rate
Humans
Neural networks
Speech
Online Access:Request full text
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Summary:Affective interface that acquires and detects the emotion of the user can potentially enhance the human-computer interface experience. In this paper, an affective brain-computer interface (ABCI) is proposed to perform affective computation on electroencephalogram (EEG) correlates of emotion. The proposed ABCI extracts EEG features from subjects while exposed to 6 emotionally-related musical and vocal stimuli using kernel smoothing density estimation (KSDE) and Gaussian mixture model probability estimation (GMM). A classification algorithm is subsequently used to learn and classify the extracted EEG features. An inter-subject validation study is performed on healthy subjects to assess the performance of ABCI using a selection of classification algorithms. The results show that ABCI that employed the Bayesian network and the one-rule classifier yielded a promising inter-subject validation accuracy of 90%.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2009.5178748