MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain–Machine Interfaces With EEG Channel Selection

A brain–machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement. It plays a key role in prosthesis control and motor rehabilitation. To improve user comfort, preserve data privacy, and reduce the system’s latency, a new trend in wearable BMIs is to execute algorithms on low-power microcontroller units (MCUs) embedded on edge devices to process the electroencephalographic (EEG) data in real-time close to the sensors. However, most of the classification models presented in the literature are too resource-demanding for low-power MCUs. This article proposes an efficient convolutional neural network (CNN) for EEG-based MI classification that achieves comparable accuracy while being orders of magnitude less resource-demanding and significantly more energy-efficient than state-of-the-art (SoA) models. To further reduce the model complexity, we propose an automatic channel selection method based on spatial filters and quantize both weights and activations to 8-bit precision with negligible accuracy loss. Finally, we implement and evaluate the proposed models on leading-edge parallel ultralow-power (PULP) MCUs. The final two-class solution consumes as little as
$30 ~\mu \text{J}$
/inference with a runtime of 2.95 ms/inference and an accuracy of 82.51% while using
$6.4\times $
fewer EEG channels, becoming the new SoA for embedded MI-BMI and defining a new Pareto frontier in the three-way trade-off among accuracy, resource cost, and power usage.