Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network

For EEG-based emotion recognition tasks, there are many irrelevant channel signals contained in multichannel EEG data, which may cause noise and degrade the performance of emotion recognition systems. In order to tackle this problem, we propose a novel deep belief network (DBN) based method for exam...

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Bibliographic Details
Published in2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) pp. 154 - 157
Main Authors Wei-Long Zheng, Hao-Tian Guo, Bao-Liang Lu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Accuracy
Electrodes
Electroencephalography
Emotion recognition
Feature extraction
Support vector machines
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Summary:For EEG-based emotion recognition tasks, there are many irrelevant channel signals contained in multichannel EEG data, which may cause noise and degrade the performance of emotion recognition systems. In order to tackle this problem, we propose a novel deep belief network (DBN) based method for examining critical channels and frequency bands in this paper. First, we design an emotion experiment and collect EEG data while subjects are watching emotional film clips. Then we train DBN for recognizing three emotions (positive, neutral, and negative) with extracted differential entropy features as input and compare DBN with other shallow models such as KNN, LR, and SVM. The experiment results show that DBN achieves the best average accuracy of 86.08%. We further explore critical channels and frequency bands by examining the weight distribution learned by DBN, which is different from the existing work. We identify four profiles with 4, 6, 9 and 12 channels, which achieve recognition accuracies of 82.88%, 85.03%, 84.02%, 86.65%, respectively, using SVM.
ISSN:1948-3546
DOI:10.1109/NER.2015.7146583