EEG-based emotion recognition using random Convolutional Neural Networks

Emotion recognition based on electroencephalogram (EEG) signals is helpful in various fields, including medical healthcare. One possible medical application is to diagnose emotional disorders in patients. Humans tend to work and communicate efficiently when in a good mood. On the other hand, negative emotions can harm physical and mental health. Traditional EEG-based methods usually extract time-domain and frequency-domain features before classifying them. Convolutional Neural Networks (CNN) enables us to extract features and classify them end-to-end. However, most CNN methods use backpropagation to train their models, which can be computationally expensive, primarily when a complex model is used. Inspired by the successes of Random Vector Functional Link and Convolutional Random Vector Functional Link, we propose using a randomized CNN model for emotion recognition that removes the need for a backpropagation method. Also, we expand our randomized CNN method to a deep and ensemble version to improve emotion recognition performance. We do experiments on the commonly used publicly available Database for Emotion Analysis using the Physiological Signals (DEAP) dataset to evaluate our randomized CNN models. Results on the DEAP dataset show our models outperform all other models, with at least 95% accuracy for all subjects. Our ensemble version outperforms our shallow version, winning the shallow version in most subjects.

Automated summary

Emotion recognition based on EEG signals is crucial in medical healthcare, helping diagnose emotional disorders in patients. A randomized CNN model is proposed to improve emotion recognition performance without the need for backpropagation.