Closed Loop Feedback fNIRS Brain Computer Interface for Increasing Classification Accuracy in a Left Versus Right Hand Movement Task

Antonio Ortega-Martinez; Alexander von Lühmann; Alexander von Lühmann; David A. Boas; Meryem A. Yücel

doi:10.1364/BRAIN.2022.BM2C.8

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper BM2C.8
•https://doi.org/10.1364/BRAIN.2022.BM2C.8

Closed Loop Feedback fNIRS Brain Computer Interface for Increasing Classification Accuracy in a Left Versus Right Hand Movement Task

Antonio Ortega-Martinez, Alexander von Lühmann, David A. Boas, and Meryem A. Yücel

Optics and the Brain 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

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Functional Neuroimaging (BM2C)

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A. Ortega-Martinez, A. von Lühmann, D. A. Boas, and M. A. Yücel, "Closed Loop Feedback fNIRS Brain Computer Interface for Increasing Classification Accuracy in a Left Versus Right Hand Movement Task," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper BM2C.8.

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Abstract

Feedback in brain computer interfaces can increase classification accuracy. We present a pipeline for real-time multimodal Kalman filter regression and classification of fNIRS signals. We demonstrate the visual feedback in a motor imagery task.

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