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Related Experiment Video

Updated: May 14, 2026

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Speaking mode recognition from functional Near Infrared Spectroscopy.

Christian Herff¹, Felix Putze, Dominic Heger

¹Cognitive Systems Lab, Karlsruhe Institute of Technology, Adenauerring 4, 76131 Karlsruhe, Germany. christian.herff@kit.edu

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

|February 1, 2013

Summary

Functional Near Infrared Spectroscopy (fNIRS) Brain Computer Interfaces (BCIs) can now detect speech. This study shows fNIRS signals can distinguish between audible, silent, and imagined speech with high accuracy.

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Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Speech is a primary human communication method.
Functional Near Infrared Spectroscopy (fNIRS) is a growing Brain Computer Interface (BCI) technology.
Limited research exists on fNIRS for speech-related BCI tasks.

Purpose of the Study:

To investigate the feasibility of using fNIRS signals for speech detection and classification in BCIs.
To determine if fNIRS can differentiate between various speech production modes (audible, silent, imagined).

Main Methods:

Experiments were conducted on 5 subjects performing speech tasks (audible, silent, imagined) and non-speech tasks.
fNIRS signals were recorded during these tasks.
Support Vector Machines and Mutual Information based Best Individual Feature methods were used for classification.

Main Results:

fNIRS signals successfully distinguished speech segments from non-speech segments with 69%-88% accuracy.
Three distinct speaking modes were classified with 61% accuracy.
Classification performance is comparable to fNIRS-based motor imagery BCIs.

Conclusions:

Speech production generates detectable neural signals via fNIRS.
fNIRS is a promising modality for developing speech-based BCIs.
This research opens new avenues for communication restoration technologies.