Meta-cognitive Multi-scale Hierarchical Reasoning for Motor Imagery Decoding

TL;DR

This paper presents a hierarchical, meta-cognitive decoding framework for motor imagery classification in EEG signals, improving robustness and accuracy by multi-scale processing and confidence-guided refinement.

Contribution

It introduces a novel multi-scale hierarchical signal processing and introspective uncertainty estimation framework for EEG-based MI decoding, enhancing robustness across subjects.

Findings

Improved average classification accuracy across all backbones.

Reduced inter-subject variance indicating increased robustness.

Enhanced reliability of MI-based BCI systems.

Abstract

Brain-computer interface (BCI) aims to decode motor intent from noninvasive neural signals to enable control of external devices, but practical deployment remains limited by noise and variability in motor imagery (MI)-based electroencephalogram (EEG) signals. This work investigates a hierarchical and meta-cognitive decoding framework for four-class MI classification. We introduce a multi-scale hierarchical signal processing module that reorganizes backbone features into temporal multi-scale representations, together with an introspective uncertainty estimation module that assigns per-cycle reliability scores and guides iterative refinement. We instantiate this framework on three standard EEG backbones (EEGNet, ShallowConvNet, and DeepConvNet) and evaluate four-class MI decoding using the BCI Competition IV-2a dataset under a subject-independent setting. Across all backbones, the proposed components improve average classification accuracy and reduce inter-subject variance compared to the corresponding baselines, indicating increased robustness to subject heterogeneity and noisy trials. These results suggest that combining hierarchical multi-scale processing with introspective confidence estimation can enhance the reliability of MI-based BCI systems.