Hand movements are crucial for human interaction and expression. However, many individuals with neurological disorders, such as stroke, spinal cord injury, and locked-in syndrome, experience impaired hand function, limiting their daily activities and self-expression. Developing technologies that restore or augment hand function is essential for improving their quality of life and social inclusion. Additionally, these technologies offer valuable advantages for healthy users, enabling a more natural and seamless interaction with virtual reality (VR) environments. This project aims to develop new deep learning techniques to accurately decode 3D hand position from wearable EEG Brain-Computer Interface (BCI) signals. The ultimate goal is to create a reliable and efficient method for decoding hand movements from EEG signals, which can significantly impact the field of VR. Moreover, these technologies have proven to be effective in rehabilitating stroke patients who have lost their hand mobility. This technology can be used as part of neurofeedback therapy to translate imagined movements into a virtual environment.
Developing new deep learning techniques to accurately decode 3D hand position from wearable EEG Brain-Computer Interface (BCI) signals
Molecular Bionics Engineering BSc, Computational Neuroscience, Cognition and AI MSc
Prof Damien Coyle
Dr Elena Seminati
