Using NIRS as a predictor for EEG-based BCI performance

Siamac Fazli, Jan Mehnert, Jens Steinbrink, Benjamin Blankertz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multimodal recordings of EEG and NIRS of 14 subjects are analyzed in the context of sensory-motor based Brain Computer Interface (BCI). Our findings indicate that performance fluctuations of EEG-based BCI control can be predicted by preceding Near-Infrared Spectroscopy (NIRS) activity. These NIRS-based predictions are then employed to generate new, more robust EEG-based BCI classifiers, which enhance classification significantly, while at the same time minimize performance fluctuations and thus increase the general stability of BCI performance.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages4911-4914
Number of pages4
DOIs
Publication statusPublished - 2012 Dec 14
Externally publishedYes
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: 2012 Aug 282012 Sep 1

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
CountryUnited States
CitySan Diego, CA
Period12/8/2812/9/1

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Fazli, S., Mehnert, J., Steinbrink, J., & Blankertz, B. (2012). Using NIRS as a predictor for EEG-based BCI performance. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 4911-4914). [6347095] https://doi.org/10.1109/EMBC.2012.6347095