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Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication
Frontiers in Neuroscience
  • Androu Abdalmalak, Western University
  • Daniel Milej, Western University
  • Lawrence C.M. Yip, Western University
  • Ali R. Khan, Western University
  • Mamadou Diop, Western University
  • Adrian M. Owen, Western University
  • Keith St. Lawrence, Western University
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© 2020 Abdalmalak, Milej, Yip, Khan, Diop, Owen and St. Lawrence. Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for “mental communication” on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for “yes” and to stay relaxed for “no.” The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as “yes” or “no” responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.

Citation Information
Androu Abdalmalak, Daniel Milej, Lawrence C.M. Yip, Ali R. Khan, et al.. "Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication" Frontiers in Neuroscience (2020)
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