Emotion detection using physiological signals EEG & ECG
Emotion modeling and identification has attracted substantial interest from disciplines including computer science, cognitive science and psychology. Despite the fact that a lot of qualitative studies have been carried out on emotion, less investigated aspects include the quantifying of physiologi...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Convergence Information Society(CIS)
2016
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/51279/ http://irep.iium.edu.my/51279/ http://irep.iium.edu.my/51279/1/IJACT-amjad.pdf |
| Summary: | Emotion modeling and identification has attracted substantial interest from disciplines including
computer science, cognitive science and psychology. Despite the fact that a lot of qualitative studies
have been carried out on emotion, less investigated aspects include the quantifying of physiological
signals. This paper presents two physiological signals which are ECG and EEG and shows analysis of
its emotional properties. A solution based on the short Fourier transform is proposed for the
recognition of dynamically developing emotion patterns on ECG and EEG. Features extraction that
are used in this paper are Kernel Density Estimation known as (KDE) and Mel-frequency cepstral
coefficients known as MFCC. The classifier that is used in this work is Multi-layer Perceptron known
as MLP, classification features are based on the valence and arousal. The experimental setup
presented in this work for the elicitation of emotions is based on passive valence /arousal. The results
shows that the ECG signal has direct relationship with the arousal factor rather than the valence
factor. Also, EEG signal using 19 channels reported high accuracy results for determining emotions. |
|---|