A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2112.10026v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain–Machine Interfaces
[article]
<span title="2021-12-18">2021</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Motor imagery brain--machine interfaces enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units, for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost trade-off for embedded BMI solutions. Our
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... ral Riemannian classifier reaches 75.1% accuracy on a 4-class MI task. The accuracy is further improved by tuning different types of classifiers to each subject, achieving 76.4%. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1% and 1.4%, respectively, which is still up to 4.1% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU within an energy budget of merely 198uJ and taking only 16.9ms per classification. Classifying samples continuously, overlapping the 3.5s samples by 50% to avoid missing user inputs allows for operation at just 85uW. Compared to related works in embedded MI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energy trade-off for near-sensor classification.
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