Assessment and Classification of Mental Workload in the Prefrontal Cortex (PFC) Using Fixed-Value Modified Beer-Lambert Law

Optical-neuro-imaging based functional Near-Infrared Spectroscopy (fNIRS) has been in use for several years in the fields of brain research to measure the functional response of brain activity and apply it in fields such as Neuro-rehabilitation, Brain-Computer Interface (BCI) and Neuroergonomics. In this paper we have enhanced the classification accuracy of a Mental workload task using a novel Fixed-Value Modified Beer-Lambert law (FV-MBLL) method. The hemodynamic changes corresponding to mental workload are measured from the Prefrontal Cortex (PFC) using fNIRS. The concentration changes of oxygenated and deoxygenated hemoglobin (
${\Delta \text {c}}_{\mathrm {HbO}}\left ({\mathrm {t} }\right) $
and
${\Delta \text {c}}_{\mathrm {HbR}}\left ({\mathrm {t} }\right))$
of 20 participants are recorded for mental workload and rest. The statistical analysis shows that data obtained from fNIRS is statistically significant with p < 0.0001 and t-values from the MBLL data with>1.97 at confidence level of 0.95. The Support Vector Machine (SVM) classifier is used to discriminate mental math (coding) task from rest. Four features, namely mean, peak, slope and variance, are calculated on data processed through two different variants of Beer-lambert Law i.e., MBLL and FV-MBLL for tissue blood flow. The optimal combination of the mean and peak values classified by SVM yielded the highest accuracy, 75%. This accuracy is further enhanced using the same feature combination, to 94% when those features are calculated using the novel algorithm FV-MBLL (with its optical density modelled form the first 4 sec stimulus data). The proposed technique can be effectively used with greater accuracies in the application of fNIRS for functional brain imaging and Brain-Machine Interface.