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In recent years, deep learning (DL) has contributed significantly to theimprovement of motor-imagery brain-machine interfaces (MI-BMIs) based onelectroencephalography(EEG). While achieving high classification accuracy, DLmodels have also grown in size, requiring a vast amount of memory andcomputational resources. This poses a major challenge to an embedded BMIsolution that guarantees user privacy, reduced latency, and low powerconsumption by processing the data locally. In this paper, we proposeEEG-TCNet, a novel temporal convolutional network (TCN) that achievesoutstanding accuracy while requiring few trainable parameters. Its low memoryfootprint and low computational complexity for inference make it suitable forembedded classification on resource-limited devices at the edge. Experimentalresults on the BCI Competition IV-2a dataset show that EEG-TCNet achieves77.35% classification accuracy in 4-class MI. By finding the optimal networkhyperparameters per subject, we further improve the accuracy to 83.84%.Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCIBenchmarks (MOABB), a large scale test benchmark containing 12 different EEGdatasets with MI experiments. The results indicate that EEG-TCNet successfullygeneralizes beyond one single dataset, outperforming the currentstate-of-the-art (SoA) on MOABB by a meta-effect of 0.25.

EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces