AUTHOR=Abdalmalak Androu , Milej Daniel , Yip Lawrence C. M. , Khan Ali R. , Diop Mamadou , Owen Adrian M. , St. Lawrence Keith 

TITLE=Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00105

DOI=10.3389/fnins.2020.00105

ISSN=1662-453X

ABSTRACT=Brain-computer interfaces (BCI) are becoming increasingly popular as a tool to improve the quality of life of patients with brain injuries. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of the enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. In this study, we present the first account of TR-fNIRS based BCI for 'mental communication' on healthy participants. 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 concentration 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 a 'yes' or 'no' responses. A lineardiscriminant analysis (LDA) and a 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 and mean arterial pressure) were recorded on seven of the twenty-one participants during motor imagery and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. To our knowledge, this is the first report of TR-fNIRS being used as a BCI on healthy controls, and our work suggests that TR-fNIRS would be suitable as a BCI for patients with brain injuries.