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Using NIRS as a predictor for EEG-based BCI performance

Standard

Using NIRS as a predictor for EEG-based BCI performance. / Fazli, Siamac; Mehnert, Jan; Steinbrink, Jens; Blankertz, Benjamin.

In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, Vol. 2012, 2012, p. 4911-4.

Research output: SCORING: Contribution to journal › Conference abstract in journal › Research › peer-review

Harvard

Fazli, S, Mehnert, J, Steinbrink, J & Blankertz, B 2012, 'Using NIRS as a predictor for EEG-based BCI performance', Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2012, pp. 4911-4. https://doi.org/10.1109/EMBC.2012.6347095

APA

Fazli, S., Mehnert, J., Steinbrink, J., & Blankertz, B. (2012). Using NIRS as a predictor for EEG-based BCI performance. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2012, 4911-4. https://doi.org/10.1109/EMBC.2012.6347095

Vancouver

Fazli S, Mehnert J, Steinbrink J, Blankertz B. Using NIRS as a predictor for EEG-based BCI performance. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2012;2012:4911-4. https://doi.org/10.1109/EMBC.2012.6347095

Bibtex

@article{01b6a6b202a7462697b8a2b52a9093a0,
title = "Using NIRS as a predictor for EEG-based BCI performance",
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.",
keywords = "Adult, Algorithms, Brain Mapping/methods, Brain-Computer Interfaces, Cerebral Cortex/physiology, Electroencephalography/methods, Feedback, Sensory/physiology, Female, Humans, Male, Oxygen Consumption/physiology, Reproducibility of Results, Sensitivity and Specificity, Spectroscopy, Near-Infrared/methods, Young Adult",
author = "Siamac Fazli and Jan Mehnert and Jens Steinbrink and Benjamin Blankertz",
year = "2012",
doi = "10.1109/EMBC.2012.6347095",
language = "English",
volume = "2012",
pages = "4911--4",
journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference",
issn = "2375-7477",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Using NIRS as a predictor for EEG-based BCI performance

AU - Fazli, Siamac

AU - Mehnert, Jan

AU - Steinbrink, Jens

AU - Blankertz, Benjamin

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Adult

KW - Algorithms

KW - Brain Mapping/methods

KW - Brain-Computer Interfaces

KW - Cerebral Cortex/physiology

KW - Electroencephalography/methods

KW - Feedback, Sensory/physiology

KW - Female

KW - Humans

KW - Male

KW - Oxygen Consumption/physiology

KW - Reproducibility of Results

KW - Sensitivity and Specificity

KW - Spectroscopy, Near-Infrared/methods

KW - Young Adult

U2 - 10.1109/EMBC.2012.6347095

DO - 10.1109/EMBC.2012.6347095

M3 - Conference abstract in journal

C2 - 23367029

VL - 2012

SP - 4911

EP - 4914

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

SN - 2375-7477

ER -