Entropy-Based Dual-Teacher Distillation for Efficient Motor Imagery EEG Classification.

Overview

1. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China

Abstract

Motor imagery (MI) EEG classification is a key component of noninvasive brain–computer interfaces (BCIs) and often must satisfy strict latency constraints in online or edge deployments. Although ensembling can reliably improve MI decoding accuracy, its inference cost grows linearly with the number of ensemble members, making it impractical for low-latency applications. To address these issues, we propose an entropy-based dual-teacher distillation framework that transfers ensemble teacher knowledge to a single deployable backbone. From an information theoretic perspective, two failure modes are common in small and noisy MI datasets: elevated predictive entropy (noisy decisions) and large fluctuation across late training epochs (unstable convergence and unreliable checkpoint selection). Thus, we introduce an exponential moving average (EMA) teacher with entropy-gated activation as a low-pass filter in parameter space to reduce the student’s prediction noise. In addition, a two-stage cosine annealing schedule is employed to suppress late-stage oscillations and improve the robustness of final checkpoint selection. Experiments on two public MI benchmarks (BCI Competition IV-2a and IV-2b) with three representative backbones (EEGNet, ShallowConvNet, and ATCNet) under the subject dependent protocol show consistent accuracy gains over the ensemble teacher and strong distillation baselines. On IV-2a, our method achieves an average accuracy of 0.7713 across the backbones, surpassing both the original models (0.7222) and the corresponding ensembles (0.7482); on IV-2b, it achieves 0.8583 versus 0.8432 (original) and 0.8529 (ensemble).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• bnci-horizon-2020.eu/database/data-sets, at bnci-horizon-2020.eu; found in “Data Availability Statement”

Data Availability Statement

The BCI Competition IV 2a and 2b datasets utilized in this study are publicly accessible. To access it, visit following website: http://bnci-horizon-2020.eu/database/data-sets (accessed on 12 January 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 1 funder, 39 references.

Cite

This paper

Xu, Z., & Yu, Z. (2026). Entropy-Based Dual-Teacher Distillation for Efficient Motor Imagery EEG Classification. Entropy (Basel, Switzerland), 28(3), 310. https://doi.org/10.3390/e28030310

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