Using minimal number of electrodes for emotion detection using brain signals produced from a new elicitation technique

Emotion is an important aspect in the interaction between humans. There is a great interest for detecting emotions automatically. Current approaches for emotion detection using EEG are not practical for real-life situations because researchers ask participants to reduce any motion and facial muscle movement, reject noisy EEG data and rely on large number of electrodes. In this paper, we propose an approach that analyses highly contaminated brain signals. We then extract relevant features for the emotion detection task based on neuroscience findings. We reached an average accuracy of 51%, 53%, 58% and 61% for joy, anger, fear and sadness, respectively. We are also applying our approach on fewer number of electrodes that ranges from 4 to 25 electrodes and we reached an average classification accuracy of 33% for joy emotion, 38% for anger, 33% for fear and 37.5% for sadness using 4 or 6 electrodes only.

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