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Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication - PubMed
Source: pubmed.ncbi.nlm.nih.gov
Article summary:
1. This study presents the first account of time-resolved functional near-infrared spectroscopy (TR-fNIRS) based brain-computer interface (BCI) for “mental communication” on healthy participants.
2. Twenty-one participants were asked a series of questions and instructed to imagine playing tennis for “yes” and to stay relaxed for “no”, with the change in the mean time-of-flight of photons used to calculate the change in concentrations of oxy- and deoxyhemoglobin.
3. Linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify responses, with overall accuracies achieved for all participants being 75% and 76%, respectively.
Article analysis:
This article provides an assessment of time-resolved fNIRS for brain-computer interface applications of mental communication. The study is well designed, with twenty one healthy participants recruited and 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, which 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, with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers 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 article is generally trustworthy, as it is well designed with clear methodology outlined throughout. It also includes physiological parameters such as heart rate (HR) and mean arterial pressure (MAP), which are recorded on seven out of twenty one participants during motor imagery (MI) and rest periods in order to investigate changes between conditions. Furthermore, no significant difference in these parameters was found between conditions, suggesting that TR fNIRS could be suitable as a BCI for patients with brain injuries.
The only potential bias that could be identified is that there may have been some selection bias when recruiting participants due to their age range being 18–30 years old; however this does not significantly affect the reliability or trustworthiness of the article itself.