  With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may 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 (MCUs), for assured privacy, user
comfort, and long-term usage. In this work, we provide practical insights on
the accuracy-cost tradeoff for embedded BMI solutions. Our proposed
Multispectral Riemannian Classifier reaches 75.1% accuracy on 4-class MI task.
We further scale down the model by quantizing it to mixed-precision
representations with a minimal accuracy loss of 1%, which is still 3.2% more
accurate than the state-of-the-art embedded convolutional neural network. We
implement the model on a low-power MCU with parallel processing units taking
only 33.39ms and consuming 1.304mJ per classification.
