A novel EEG-based emotion recognition approach for e-healthcare applications
Open Access
- 4 April 2016
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 162-164
Abstract
Electroencephalogram (EEG) signals play an important role in e-healthcare systems, especially to recognise the mental state of patients that could need a special care. This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients for Ambient Assisted Living (AAL). The proposed approach combines wavelet energy, modified energy and wavelet entropy features to classify four different classes of emotions. Three different classifiers are used (quadratic discriminant analysis (QDA), k-nearest neighbor, and support vector machines) to recognise the emotion of patients robustly. The new approach is tested using the EEG database \"DEAP\" using four electrodes. It shows high performance compared to existing algorithms. An overall classification accuracy of 83.2% is obtained.Keywords
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