Unified pipeline for generalized mental state detection using EEG signals

• Published in: Expert systems with applications Vol. 297; p. 129422
• Main Authors: Wang, Yinghao, Elrawas, Rayan, Nguyen, Anh-Dung, Girard, Maxime, Mozharovskyi, Pavlo, Tartaglione, Enzo, Nguyen, Van-Tam
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2026
• Subjects: Attention; Brain-computer interfaces (BCIs); Cross-subject generalisation; Electroencephalography (EEG); Machine learning; Mental state detection; Electroencephalography (EEG); Cross-subject generalisation; Brain-computer interfaces (BCIs); Mental state detection; Attention; Machine learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2025.129422

•An end-to-end optimized pipeline for classifying mental states.•Tailed to various mainstream ML algorithms.•Achieve SOTA across different classification paradigms.•Demonstrating exceptional generalizability across subjects. Mental states, a complex union of cognitive, emotional, and perceptual conditions, fundamentally shape how individuals perceive and interact with their surroundings. Detecting these states is vital, as it reveals the underlying processes that govern behaviour and enables targeted interventions across diverse fields such as mental health, education, and human-computer interaction. Generalisability across subjects and trials is essential to ensure that these interventions are effective and reliable in varied real-world settings, thereby enhancing their practical applicability. In this paper, we introduce an end-to-end optimised pipeline for classifying mental states from electroencephalography (EEG) signals. Through quantitative studies of data preprocessing and feature enhancement of continuous data collected under less stringent conditions, our pipeline utilises specially designed, cutting-edge, lightweight classifiers and achieves new state-of-the-art performance. Specifically addressing the challenge of generalisability in EEG signal research, our pipeline demonstrates robust performance, achieving a peak accuracy of 79.1 % and an average of 71.9 % in cross-subject scenarios, and a high of 89.3 % with an average of 85.4 % in cross-trial evaluations.