Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 15; p. 5135
• Main Authors: Mai, Ngoc-Dau, Lee, Boon-Giin, Chung, Wan-Young
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.07.2021; MDPI
• Subjects: affective computing; Brain research; Datasets; Electrodes; electroencephalogram (EEG); Electroencephalography; emotion recognition; Emotions; entropy measures; Experiments; Humans; Machine Learning; multi-layer perceptron (MLP); Neural Networks, Computer; Physiology; Principal components analysis; Skin; Social networks; Software; Support Vector Machine; support vector machine (SVM); Variance analysis; Wavelet transforms; one-dimensional convolutional neural network (1D-CNN); affective computing; support vector machine (SVM); entropy measures; emotion recognition; multi-layer perceptron (MLP); electroencephalogram (EEG)
• DOI: 10.3390/s21155135
• ISSN: 1424-8220

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.