FOCUS: A Lightweight Mamba-2 Model for EEG-Based Cognitive Workload Classification

Authors: Ioannis Kyprakis , Stefanos Gkikas , Chariklia Chatzaki , Zongyi Jiang , Randy Gomez , Raul Fernandez Rojas

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Abstract: Cognitive workload estimation from physiological signals is increasingly relevant in high-risk environments, where continuous and unobtrusive monitoring can inform adaptive systems and safety interventions. This paper presents FOCUS (Frequency-band Optimized Cognitive-load Ultralight SSM), a compact Mamba-2-based model for EEG-based binary workload classification under constrained data conditions. Rather than learning frequency sensitivity from data, FOCUS encodes preprocessed multi-channel EEG using a fixed FFT decomposition that covers the five canonical EEG bands. The proposed model combines depthwise-separable convolution and a weight-shared encoder-bottleneck Mamba-2 pair, with 0.04 million parameters and 0.11 GFLOPs. Evaluated in a pilot study with 11 participants under leave-one-subject-out cross-validation across three cognitively distinct tasks, FOCUS achieves 74.75% accuracy on the full dataset at 0.08 ms GPU model-forward latency. In the weight-sharing ablation, the shared configuration achieves 4.55 percentage points higher accuracy on the full dataset than the non-shared variant while reducing the SSM parameter budget; however, this difference does not reach statistical significance under a two-sided Wilcoxon signed-rank test ($p=0.083$). Overall, the selected FOCUS configuration provides a favorable trade-off between classification performance and computational cost within this controlled pilot setting.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.32361 [cs.AI]

(or

arXiv:2610.32361v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.32361

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arXiv-issued DOI via DataCite (pending registration)

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From: Stefanos Gkikas [ view email ]

[v1]

Fri, 23 Oct 2026 08:54:09 UTC (270 KB)

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