EEG-based emotion recognition using random Convolutional Neural Networks. (November 2022)

• Record Type: Journal Article
• Authors: Cheng, Wen Xin; Gao, Ruobin; Suganthan, P.N.; Yuen, Kum Fai
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: Emotion recognition -- Randomized neural networks -- Convolutional Neural Networks -- Deep learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105349
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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. Highlights: A randomized CNN is proposed to classify EEG signals. Randomized CNN are expanded into deep and ensemble version for improved performance. Randomized CNN outperforms other EEG-based models. Deep and ensemble version substantially outperforms shallow Randomized CNN.