Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm
- Submitting institution
-
The University of Kent
- Unit of assessment
- 12 - Engineering
- Output identifier
- 17874
- Type
- D - Journal article
- DOI
-
10.1038/s42256-019-0091-7
- Title of journal
- Nature Machine Intelligence
- Article number
- -
- First page
- 412
- Volume
- 1
- Issue
- 9
- ISSN
- 2522-5839
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- URL
-
https://kar.kent.ac.uk/75551/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
8
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes the first portable Brain Machine Interface (BMI) system based on soft electronics and deep learning. Our approach demonstrates the feasibility of a universal BMI with only two electrodes, a significant improvement in terms of real-world usability. This work has attracted media coverage, including Forbes and has led to further collaboration in using the proposed sensor-algorithm set-up for home-based sleep monitoring (Georgia Tech), and also detection of anxiety of patients going through cancer treatment (Memorial Sloan Kettering Cancer Center).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
