# An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing

arXiv: https://arxiv.org/abs/2004.00077 · HTML: https://mlsanity.com/paper/2004.00077/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices](https://mlsanity.com/paper/2103.06709/) (2021)
- [CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion](https://mlsanity.com/paper/2311.07788/) (2023)
- [Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters](https://mlsanity.com/paper/2510.08059/) (2025)
- [On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface](https://mlsanity.com/paper/2409.00083/) (2024)
- [Genetic algorithm for feature selection of EEG heterogeneous data](https://mlsanity.com/paper/2103.07117/) (2021)
- [AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification](https://mlsanity.com/paper/2506.21338/) (2025)
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- [Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)](https://mlsanity.com/paper/2502.05332/) (2025)
- [Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces](https://mlsanity.com/paper/2102.11221/) (2021)
- [The evolution of AI approaches for motor imagery EEG-based BCIs](https://mlsanity.com/paper/2210.06290/) (2022)
- [On using AI for EEG-based BCI applications: problems, current challenges and future trends](https://mlsanity.com/paper/2506.16168/) (2025)
- [Affordable EEG, Actionable Insights: An Open Dataset and Evaluation Framework for Epilepsy Patient Stratification](https://mlsanity.com/paper/2511.01879/) (2025)
- [Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing](https://mlsanity.com/paper/2601.00020/) (2025)
- [Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2601.07556/) (2026)
- [NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfaces](https://mlsanity.com/paper/2609.09786/) (2026)
- [EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review](https://mlsanity.com/paper/2609.32930/) (2026)

## Abstract (arXiv)

This paper presents an accurate and robust embedded motor-imagery brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet, matches the requirements of memory footprint and computational resources of low-power microcontroller units (MCUs), such as the ARM Cortex-M family. Furthermore, the paper presents a set of methods, including temporal downsampling, channel selection, and narrowing of the classification window, to further scale down the model to relax memory requirements with negligible accuracy degradation. Experimental results on the Physionet EEG Motor Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and 65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global validation, outperforming the state-of-the-art (SoA) convolutional neural network (CNN) by 2.05%, 5.25%, and 5.48%. Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6x memory footprint reduction and a small accuracy loss of 2.51% with 15x reduction. The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 4.28mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI.