Lightweight deep learning models for EEG decoding: a review

Brain-computer interface (BCI) technology enables direct communication between the human brain and external devices by decoding electroencephalography (EEG)signals into actionable commands. As a noninvasive and portable modality, EEG-based BCIs hold promise for applications ranging from neurorehabilitation to assistive technologies. However, their performance depends critically on the accurate extraction of relevant neural features and the reliable recognition of underlying patterns. Deep learning has transformed this process. By automatically learning complex, task-relevant representations from raw or minimally processed EEG data, deep neural networks have surpassed many traditional handcrafted feature approaches in both accuracy and adaptability. Yet, the substantial computational and memory demands of many deep learning architectures limit their deployment in portable or real-time BCI systems. This challenge has motivated a growing interest in lightweight models-architectures optimized to reduce complexity while preserving or even enhancing performance. This paper provides a systematic review of such lightweight deep learning models for EEG signal classification. To organize this landscape, existing approaches are categorized into three main strategies: (1) information integration strategies based on multi-scale feature fusion, (2) hidden layer optimization strategies, and (3) hybrid improvement strategies based on structural optimization. The review synthesizes recent advances, identifies emerging trends, and outlines potential directions for future research. These insights aim to inform the design of efficient and robust EEG classification architectures capable of meeting the practical demands of real-world BCI applications.