On the Reliability of Frequency-Domain Features for fNIRS BCIs in the Presence of Pain
Author:
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9672252/9672254/09672256.pdf?arnumber=9672256
Reference18 articles.
1. Enabling Communication for Locked-in Syndrome Patients using Deep Learning and an Emoji-based Brain Computer Interface
2. The Dorsolateral Prefrontal Cortex in Acute and Chronic Pain
3. fNIRS Approach to Pain Assessment for Non-verbal Patients
4. A machine learning approach for the identification of a biomarker of human pain using fNIRS;rojas;Scientific Reports,2019
5. Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation
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2. Hierarchical Classification Strategy for Mitigating the Impact of The Presence of Pain in fNIRS-based BCIs;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24
3. Investigation of the Performance of fNIRS-based BCIs for Assistive Systems in the Presence of Acute Pain;Signal Processing in Medicine and Biology;2023
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