​EEG-Based Assistive Robots for Rehabilitation

Electroencephalography (EEG)-based brain-computer interface (BCI) systems have shown great potential for rehabilitating patients with motor impairments as well as for augmenting human working capacity, either physically or cognitively.

In addition, EEG-based BCI systems have been successfully developed into assistive devices for rehabilitation purposes. For example, BCI-hand robots in the rehabilitation of an affected hand following stroke.

Nevertheless, it is important to emphasize that there is currently insufficient evidence to support the use of such systems within clinical settings. The benefits of these systems rest on conjectures that require empirical evidence, therefore, more research efforts are needed to bridge the gap between theory and practice.

This could be achieved by addressing the inherent challenges of the EEG modality, such as automatic artefact correction, and truly exploring advanced methods of EEG feature extraction and classification. In addition, exploring new technologies that are responsive for the control signals generated by the EEG data including the building of modern techniques for human intent recognition. This Research Topic will cover any methods to develop EEG-based assistive devices for rehabilitation purposes. For example, a thought-to-text system that provides assistance to paralyzed patients. Such systems enable disabled patients to communicate with the people around them.

Themes relevant to this Research Topic include, but are not limited to:

• EEG-based BCI control schemes for control of assistive robots
• EEG-based BCI adaptive brain-robot interfaces
• Multichannel EEG-BCI communication systems
• Virtual Reality-based EEG-BCI Training Systems
• Internet of Things based assistive devices
• Deep learning techniques for efficient feature extraction
• Wearable and Non-invasive assistive technologies

Keywords: BCI driven robotic systems, EEG-BCI assistive technology, BCI and robotics, robot-assisted rehabilitation, Assistive Devices, EEG-based robotics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.