Systematic Benchmarking of Single-Channel EEG for Cognitive State Recognition

Cognitive state recognition has emerged as a key research topic at the intersection of neuroscience and artificial intelligence. While multi-channel EEG systems achieve high recognition accuracy, their complexity and cost substantially hinder large-scale deployment. In contrast, single-channel EEG has attracted increasing interest due to its portability and accessibility. Nevertheless, its feasibility for reliable cognitive state recognition remains uncertain, as a single electrode records only limited neural activity. To address this issue, we conducted a systematic set of experiments to comprehensively evaluate the potential of single-channel EEG systems in cognitive state assessment. The evaluation encompasses a broad spectrum of widely adopted tasks, including motor imagery, emotion recognition, and workload estimation. We benchmarked 17 representative EEG analysis approaches, ranging from traditional machine learning algorithms to advanced deep learning models, including methods specifically tailored for single-channel EEG. Furthermore, we introduce a novel quantitative metric to assess electrode placement effectiveness, thereby reducing evaluation bias caused by algorithmic differences. Experimental results reveal that single-channel EEG systems still face considerable challenges in cognitive state recognition. Specifically, their accuracy remains significantly lower than that of multi-channel systems, and task performance is highly sensitive to electrode location, with optimal placements exhibiting strong task dependency. These findings suggest that future research should emphasize the development of more effective single-channel EEG analysis techniques and adaptive electrode optimization strategies capable of dynamically adjusting placements according to task requirements.