Taming fNIRS-based BCI Input for Better Calibration and Broader Use.

Wang, Liang.

Huang, Zhe.

Zhou, Ziyu.

McKeon, Devon.

Blaney, Giles.

Hughes, Michael C.

Jacob, Robert J. K.

2021

open in viewer
Description
  • Keywords: BCI, Brain-Computer Interface, cognitive workload, data augmentation, fNIRS, implicit interfaces, machine learning, n-back task, near-infrared spectroscopy, neural networks.

    Topic: Computing methodologies / Machine learning / Machine learning approaches / Neural networks

    Topic: Computing methodologies

    Topic: Computing methodologies / Machine learning

    Topic: ... read more
This object is in collection Permanent URL Citation
  • Liang Wang, et. al. "Taming fNIRS-based BCI Input for Better Calibration and Broader Use." The 34th Annual ACM Symposium on User Interface Software and Technology, 2021
ID:
cc08hv98c
To Cite:
TARC Citation Guide    EndNote
Usage:
Detailed Rights
Rights Note:
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.
DOI:
10.1145/3472749.3474743