  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 multispectral 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.
