Sub-100 $\mu$W Multispectral Riemannian Classification for EEG-Based Brain–Machine Interfaces

• Published in: IEEE transactions on biomedical circuits and systems Vol. 15; no. 6; pp. 1149 - 1160
• Main Authors: Wang, Xiaying, Cavigelli, Lukas, Schneider, Tibor, Benini, Luca
• Format: Journal Article
• Language: English
• Published: United States 01.12.2021
• Subjects: Algorithms; Brain-Computer Interfaces; Electroencephalography; Imagination; Neural Networks, Computer
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2021.3137290

Motor imagery (MI) brain-machine interfaces (BMIs) 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 (MCUs), 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 198 μJ and taking only 16.9 ms per classification. Classifying samples continuously, overlapping the 3.5 s samples by 50% to avoid missing user inputs allows for operation at just 85 μW. 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.