Human attention detection system using deep learning and brain–computer interface

• Published in: Neural computing & applications Vol. 36; no. 18; pp. 10927 - 10940
• Main Authors: Nair, S. Anju Latha, Megalingam, Rajesh Kannan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Biofeedback; Cameras; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Education; Electroencephalography; Entropy (Information theory); Graphical user interface; Human-computer interface; Image Processing and Computer Vision; Medical research; Original Article; Probability and Statistics in Computer Science; Signal classification; System effectiveness; Deep learning; OpenBCI; Student attention; EEG; Mutual information; Brain–computer interface
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09628-8

Brain–Computer Interface is tested as a successful method in improving human cognitive functions such as attention and memory. Attention plays a significant role in areas ranging from a person’s day-to-day life to educational domain and professional activities. When attention is evaluated using camera-based techniques, the users may suffer privacy issues. Using Brain–Computer Interface (BCI) to obtain a measure of attention will be useful in this regard. The paper proposes a Human Attention Recognition System (HARS) in which EEG signal acquisition is used to obtain the attention of the individual, Renyi’s entropy-based mutual information method is used for feature selection and a deep learning-based classifier is used to classify the signals. HADS is not camera-based: therefore, faces of the subjects are not revealed. EEG signals were collected using the Ultracortex Mark III dry electrodes and were visualised using OpenBCI GUI (Graphical User Interface). The experiment is validated using the publicly available Confused Student EEG dataset from Kaggle, giving an accuracy of 99.21%. The results indicate that the proposed method can identify attention levels accurately and can be effectively used in educational systems, biofeedback and medical research.