Abstract
This paper proposes the use of time-frequency and wavelet transform features for emotion recognition via EEG signals. The proposed experiment has been carefully designed with EEG electrodes placed at FP1 and FP2 and using images provided by the Affective Picture System (IAP), which was developed by the University of Florida. A total of two time-domain features, two frequen-cy-domain features, as well as discrete wavelet transform coefficients have been studied using Artificial Neural Network (ANN) as the classifier, and the best combination of these features has been determined. Using the data collected, the best detection accuracy achievable by the proposed schemed is about 81.8%.
Highlights
EEG carries important information on the responses to stimuli in the human brain
This paper proposes the use of time-frequency and wavelet transform features for emotion recognition via EEG signals
The proposed experiment has been carefully designed with EEG electrodes placed at FP1 and FP2 and using images provided by the Affective Picture System (IAP), which was developed by the University of Florida
Summary
EEG carries important information on the responses to stimuli in the human brain. By studying the pattern of the brain signal waveforms, we can identify the types of emotion up to a certain level of accuracy. An emotion recognition system can help in understanding the cognitive functions of the brain. It can enable the command and control of machines such as the cursor of a computer, wheelchairs, or a robotic arm. ANN classifier has been used to classify brain signals of subjects engaging in mental tasks [4]. Another classifier that has been used to classify emotions is the Fuzzy C Means (FCM) Clustering [5]. A new combination of features have been proposed and the ANN classifier has been used to further improve the emotion classification accuracy.
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