Deep Multi-band EEG Learning for Motor Imagery Classification with Dry Electrodes

Brain-computer interfaces (BCIs) establish a direct neural link between the brain and external devices, enabling groundbreaking applications in motor imagery (MI)—a process in which individuals mentally simulate movements without physical execution. While non-invasive consumer BCI devices are gaining popularity, their effectiveness is often constrained by limited spatial and temporal resolution. This paper introduces WavEEGNet, a novel deep-learning approach for Electroencephalography (EEG)-based motor imagery classification. The proposed neural architecture consists of two stages: first, multiple convolutional encoders independently extract temporal and spatial features from four EEG frequency bands (delta, theta, alpha, and beta); second, a convolutional neural network with residual connections integrates these features to enhance classification performance. Processing each frequency band separately allows the model to capture distinct neurophysiological patterns associated with motor imagery, minimizing interference across bands and improving feature representation. The approach was evaluated on a dataset from a wearable BCI equipped with eight dry electrodes, offering a non-invasive and cost-effective solution for real-world applications. Our method achieved an accuracy improvement of over 10% compared to traditional hand-crafted feature-based techniques, along with statistically significant gains over other deep learning approaches.