Wearable Brain Computer Interface

Wearable brain-computer interfaces (BCIs) aim to translate brain activity into control signals for external devices, primarily focusing on applications in neurorehabilitation and neurorobotics. Current research emphasizes developing robust and efficient algorithms, often employing deep learning architectures like EEGNet and convolutional neural networks, to decode brain signals from various modalities (EEG, sEMG) even with noisy or unstable data, and to address challenges like individual user variability and bandwidth limitations in wireless sensor networks. These advancements are significant for improving the reliability and practicality of BCIs, potentially leading to more effective assistive technologies and a deeper understanding of brain-behavior relationships.