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Emotion Recognition using EEG Signals

Babasab Gadade, Dr. N. K. Cauvery


Emotion is the affective state and the complex experience of consciousness. An Emotion is characterized by the impulse of brain neuron. Brain Computer Interface is the technology that builds the communicates between human brain and computer system. The research on BCI applications is an emerging field that encourages to work with human brain and helps to analyze the human brain functionalities based on the state of the brain. This paper recognizes four different emotions using DEAP EEG data. The Fast Fourier Transformation is used to transfer the EEG data from time domain to frequency domain. Bandpass filter 4 – 45 Hz is used to extract alpha, beta, gamma and theta frequencies. The power of the frequency band is calculated and features are extracted. The Relief-F algorithm is used to select the highest weight features. The Ten-fold cross validation technique is used to train and test the classifier. The classifier Probabilistic Neural Network is used to classify the four different emotions namely, Arousal, Valence, Dominance and Liking. The average accuracy of the classifier for each emotion is 85% to 92%.


EEG (Electroencephalography); PNN (Probabilistic Neural Network); Emotion Recognition; KNN (K-Nearest Neighbor) – Feature Selection.

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