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Motor imagery brain--machine interfaces enable us to control machines bymerely thinking of performing a motor action. Practical use cases require awearable solution where the classification of the brain signals is done locallynear the sensor using machine learning models embedded on energy-efficientmicrocontroller units, for assured privacy, user comfort, and long-term usage.In this work, we provide practical insights on the accuracy-cost trade-off forembedded BMI solutions. Our multispectral Riemannian classifier reaches 75.1%accuracy on a 4-class MI task. The accuracy is further improved by tuningdifferent types of classifiers to each subject, achieving 76.4%. We furtherscale down the model by quantizing it to mixed-precision representations with aminimal 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 merely198uJ and taking only 16.9ms per classification. Classifying samplescontinuously, overlapping the 3.5s samples by 50% to avoid missing user inputsallows for operation at just 85uW. Compared to related works in embeddedMI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energytrade-off for near-sensor classification.

Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces