Decoding Different Working Memory States During An Operation Span Task From Prefrontal Fnirs Signals

Decoding human mental states non-invasively has a number of potential applications such as brain-computer interface (BCI) [1,2], understanding the perceptual and cognitive mechanisms [2], prediction of cognitive behaviour and assistant diagnoses, etc. [3,4] Among them, BCI systems by invasive or noninvasive means provide a promising strategy to establish communication with individuals who suffer from severe motor disabilities but preserve mental abilities such as stroke and amyotrophic lateral sclerosis (ALS) [5-7]. However, the need to implant objects limits the applications of invasive approach [8]. Several non-invasive neuroimaging modalities have garnered increasing attention in BCIs such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functionalWe propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.