Performance analysis of EEG based emotion recognition using deep learning models

ABSTRACT

Emotion is an important factor that decides the the state of the mind of an individual. However, there are many people who cannot express their emotions explicitly due to various psychological or physiological issues. The recent technology development in interdisciplinary techniques has made the emotion recognition easy. The main objective of this paper is to uncover the contribution of hybrid deep learning model in classifying the emotions. This novel hybrid Bi-LSTM model is applied to three dimensional VAD model to classify 16 emotions. In addition, this paper addresses their performance comparison with respect to other models. Deep learning models like CNN, LSTM, Hybrid Bi-LSTM, and Hybrid Bi-GRU were used for experimentation. FFT is used to convert from time domain to frequency domain in all the models. The performance measure of these models is estimated in terms of accuracy, precision, recall, and F1score. The estimated accuracy of different deep learning models are approximated to be 87.5% for CNN, 88.7 % for LSTM, 93.9% for Hybrid Bi-LSTM, and 92.2% for Hybrid GRU based on the number of subjects. These comparisons have helped to find the suitable deep learning model for emotion recognition.

KEYWORDS:

• Brain Computer Interface
• Emotion recognition
• Deep learning model
• DEAP dataset
• EEG signals

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.