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

Abstract

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 nearinfrared 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 timeof-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.

Decoding visual information from high-density diffuse optical tomography neuroimaging data

“…Finally, we reasoned that the increased space-bandwidth product afforded by our high-density imaging array may enable us to distinguish a larger number of targets than prior optical decoding studies that have mostly performed binary or 4-way decoding ( Abdalmalak et al, 2020 ; Emberson et al, 2017 ; Hosseini et al, 2011 ; Luu and Chau, 2009 ; Sitaram et al, 2007 ). As a result, we increased the complexity of our decoding and performed the 18-way and 36-way classification experiments with the moving checkerboard ring and wedge stimuli.…”

Optical brain imaging and its application to neurofeedback

“…Besides determining molecule concentrations from absorption changes, these approaches also provide scattering information and thus enable the separation of different tissue layers. Recently, a first optical BCI was introduced based on such time-resolved f NIRS signal potentially applicable for mental communication in patients with brain injury or stroke ( Abdalmalak et al, 2020 ).…”

“…Later, also classification of sensorimotor and temporal cortical oxygenation and de-oxygenation following simple questions with known positive or negative answers (e.g., “Your name is Giulia”) was attempted ( Gallegos-Ayala et al, 2014 ). In healthy volunteers, classification of NIRS-responses associated with simple “yes” and “no” answers was successfully demonstrated and did not require any specific conditioning of cortical responses ( Abdalmalaket al, 2020 , Rezazadeh Sereshkehet al, 2019 , Taninoet al, 2015 ). Due to the limited number of CLIS patients and significant challenges to ascertain a sufficient level of alertness ( Soekadar et al, 2013 ), it remains open whether and to what degree communication in CLIS can be restored.…”

Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS

“…to identify different tasks. Abdalmalak et al extracted the oxyhemoglobin (HbO) mean value to identify the two classes of mental tasks, and the classification accuracy achieved by support vector machines (SVM) was 76% [ 4 ]. Sereshkeh et al extracted the HbO mean value to identify the three classes of mental tasks, and the classification accuracy achieved by linear discriminant analysis (LDA) was 83.8% [ 5 ].…”