The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Biosensor Technologies for Augmented Brain–Computer Interfaces in the Next Decades
Abstract
The study of brain-computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of real-life applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kim, D.-H., Lu, N., Ma, R., Kim, Y.-S., Kim, R.-H., Wang, S., Wu, J., Won, S. M., Tao, H., Islam, A., et al. (2011) · Science · doi:10.1126/science.1206157 · Cited by 4,836
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R., Miller, G. A., Ritter, W., Ruchkin, D. S., Rugg, M. D., et al. (2000) · Psychophysiology · doi:10.1111/1469-8986.3720127 · Cited by 2,157
A reversible wet/dry adhesive inspired by mussels and geckos
Lee, H., Lee, B. P., & Messersmith, P. B. (2007) · Nature · doi:10.1038/nature05968 · Cited by 2,043
Modulation of visual responses by behavioral state in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483
Novel dry electrodes for ECG monitoring
Gruetzmann, A., Hansen, S., & Müller, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/11/005 · Cited by 248
Brain–Computer Interface Technologies in the Coming Decades
Lance, B. J., Kerick, S. E., Ries, A. J., Oie, K. S., & McDowell, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2184830 · Cited by 231
Self-citationLin, C.-T., Ko, L.-W., Chang, M.-H., Duann, J.-R., Chen, J.-Y., Su, T.-P., & Jung, T.-P. (2010) · Gerontology · doi:10.1159/000230807 · Cited by 133
Self-citation
Cited by
The temple university hospital EEG data corpus
Obeid, I., & Picone, J. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00196 · Cited by 528
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Wu, D., Xu, Y., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007453 · Cited by 434
Progress in Brain Computer Interface: Challenges and Opportunities
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.578875 · Cited by 365
Brain–Computer Interface Technologies in the Coming Decades
Lance, B. J., Kerick, S. E., Ries, A. J., Oie, K. S., & McDowell, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2184830 · Cited by 231
Self-citationAricò, P., Borghini, G., Di Flumeri, G., Colosimo, A., Pozzi, S., & Babiloni, F. (2016) · Progress in Brain Research · doi:10.1016/bs.pbr.2016.04.021 · Cited by 145
Metadata
- Authors
- Liao, L.-D., Lin, C.-T., McDowell, K., Wickenden, A. E., Gramann, K., Jung, T.-P., Ko, L.-W., & Chang, J.-Y.
- Venue
- Proceedings of the IEEE
- Published
- 2012-05
- Volume
- 100
- Issue
- Special Centennial Issue
- Pages
- 1553-1566
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2100535248
- Cited by
- 169
- References
- 7
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Biosensor Technologies for Augmented Brain–Computer Interfaces in the Next Decades. https://app.cochat.ai/papers/10.1109/jproc.2012.2184829
- The paper
- Liao, L.-D., Lin, C.-T., McDowell, K., Wickenden, A. E., Gramann, K., Jung, T.-P., Ko, L.-W., & Chang, J.-Y. (2012). Biosensor Technologies for Augmented Brain–Computer Interfaces in the Next Decades. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2012.2184829
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.