image: (a) Emotiv EPOC headset. (b) DSI-24 headset. (c) OpenBCI Ultracortex “Mark IV” headset. (d) Jure et al. from National University of Entre Ríos presented a functional electrical stimulation (FES) based BCI system, which was made up of an Emotiv EPOC headset for EEG signal recording. (e) Tabering et al. from National University of Entre Ríos used the BCI-FES system to perform neurorehabilitation therapy for patients with sequelae of ischemic stroke and evaluated the effects. Before and after the intervention, scores of the quality of movement (left) and quality of life (right) were measured for each stroke patient. (f) Taleb et al. from Wasit University designed a BCI system based on the Emotiv EPOC device (Figure 2(f) left) for self-managed neurofeedback (NFB) treatment of people with chronic SCI (left), and results showed that users had successfully regulated their brainwaves in a frequency-specific manner (right). (g-h) Zulauf-Czaja et al. from University of Glasgow presented a BCI-FES system based on an Emotiv EPOC headset. The system obtained an accuracy of 70–90%, and the median activation time of FES remained constant across sessions. (i) Choi et al. from Eulji University School of Medicine Illustration designed an action observation BCI system based on the DSI-24 EEG headset and detected the participants’ attention level by analyzing Mu rhythm power.
Credit: a) ©2016 EDP Sciences. Reprinted, with permission, from Swee et al. (b) https://www.neurospec.com/Products/Details/1079/dsi-24 (c) https://docs.openbci.com/AddOns/Headwear/MarkIV/ (d) ©2016 IOPscience. Reprinted, with permission, from Jure et al., BCI-FES system for neuro-rehabilitation of stroke patients. Journal of Physics: Conference Series. 2016;705(1):012058. DOI: 10.1088/1742-6596/705/1/012058. (e) Used with permission of SAGE Publications Ltd., from Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface, Tabernig et al., 5, 2018; permission conveyed through Copyright Clearance Center, Inc. (f) ©2019 BMC. Reprinted, with permission, from Al-Taleb et al. (g-h) ©2021 BMC. Reprinted, with permission, from Zulauf-Czaja et al. (i) ©2019 MDPI. Reprinted, with permission, from Choi et al.
The latest review in Health Data Science, a Science Partner Journal, highlights significant advancements in wearable electroencephalogram (EEG) technologies for non-invasive brain-computer interfaces (BCIs). This review is particularly valuable for researchers and clinicians new to BCI applications, offering insights into mainstream wearable non-invasive BCIs and the latest research reports.
Professor Zhihong Li from Peking University underscores the need for wearable BCIs, noting their potential to facilitate intelligent medical care beyond laboratory and clinical settings. “Wearable BCI systems are essential in the burgeoning field of intelligent medical care,” says Li.
The review emphasizes the practicality of BCIs in continuous monitoring of intermittent neurological diseases like epilepsy and migraine, as well as in controlling assistive devices in real-life scenarios. “For effective daily use, the wearability of BCIs is crucial,” explains Dr. Junshi Li from Peking University. Unlike other non-invasive BCIs, EEG can be acquired through scalp-based electrodes, making it more suitable for everyday wear.
Categorized into scalp-, forehead-, and ear-EEG based on recording locations, the review addresses the unique advantages and challenges of each. Scalp-EEG, though offering richer brain information, faces challenges in wearability and hair interference. On the other hand, forehead- and ear-EEG are more user-friendly, with ear-EEG poised to become a mainstream technology in the future.
“Improvements in ear-EEG signal mapping and data processing algorithms will likely make ear-EEG-based wearable BCIs a leading technology,” predicts Jiayan Zhang, a doctoral candidate at Peking University.
Despite the advancements, wearable BCI technology still requires further development, including addressing individual differences in EEG signals and enhancing system robustness against user and environmental interferences. Zhang envisions a future with simpler, more functional, and automated EEG-based wearable BCI devices, significantly advancing medical technology in areas like disease prediction, diagnosis, treatment, and auxiliary equipment control.
Journal
Health Data Science
Article Title
Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review
Article Publication Date
19-Dec-2023