On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface

• URL: http://arxiv.org/abs/2409.00083v1
• Date: Sun, 25 Aug 2024 08:23:51 GMT
• Title: On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface
• Authors: Sizhen Bian, Pixi Kang, Julian Moosmann, Mengxi Liu, Pietro Bonazzi, Roman Rosipal, Michele Magno,
• Abstract summary: This paper implements a lightweight and efficient on-device learning engine for wearable motor imagery recognition. We demonstrate a remarkable accuracy gain of up to 7.31% with respect to the baseline with a memory footprint of 15.6 KByte. Our tailored approach exhibits inference time of 14.9 ms and 0.76 mJ per single inference and 20 us and 0.83 uJ per single update during online training.
• Score: 2.1710886744493263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have garnered significant interest across various domains, including rehabilitation and robotics. Despite advancements in neural network-based EEG decoding, maintaining performance across diverse user populations remains challenging due to feature distribution drift. This paper presents an effective approach to address this challenge by implementing a lightweight and efficient on-device learning engine for wearable motor imagery recognition. The proposed approach, applied to the well-established EEGNet architecture, enables real-time and accurate adaptation to EEG signals from unregistered users. Leveraging the newly released low-power parallel RISC-V-based processor, GAP9 from Greeenwaves, and the Physionet EEG Motor Imagery dataset, we demonstrate a remarkable accuracy gain of up to 7.31\% with respect to the baseline with a memory footprint of 15.6 KByte. Furthermore, by optimizing the input stream, we achieve enhanced real-time performance without compromising inference accuracy. Our tailored approach exhibits inference time of 14.9 ms and 0.76 mJ per single inference and 20 us and 0.83 uJ per single update during online training. These findings highlight the feasibility of our method for edge EEG devices as well as other battery-powered wearable AI systems suffering from subject-dependant feature distribution drift.

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