23 June 2017 Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks
Author Affiliations +
Neurophotonics, 4(2), 021107 (2017). doi:10.1117/1.NPh.4.2.021107
Neurofeedback is a method for using neural activity displayed on a computer to regulate one’s own brain function and has been shown to be a promising technique for training individuals to interact with brain–machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications. We hypothesized that fNIRS neurofeedback would enhance activity in motor cortex during a motor imagery task. Twenty-two participants performed active and imaginary right-handed squeezing movements using an elastic ball while wearing a 98-channel fNIRS device. Neurofeedback traces representing localized cortical hemodynamic responses were graphically presented to participants in real time. Participants were instructed to observe this graphical representation and use the information to increase signal amplitude. Neural activity was compared during active and imaginary squeezing with and without neurofeedback. Active squeezing resulted in activity localized to the left premotor and supplementary motor cortex, and activity in the motor cortex was found to be modulated by neurofeedback. Activity in the motor cortex was also shown in the imaginary squeezing condition only in the presence of neurofeedback. These findings demonstrate that real-time fNIRS neurofeedback is a viable platform for brain–machine interface applications.
Pawan Lapborisuth, Xian Zhang, Adam Noah, Joy Hirsch, "Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks," Neurophotonics 4(2), 021107 (23 June 2017). http://dx.doi.org/10.1117/1.NPh.4.2.021107
Submission: Received 6 March 2017; Accepted 1 June 2017

Near infrared spectroscopy




Human-machine interfaces

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