Abstract
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 Neuro-ergonomics. 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 (Δc HbO (t) and Δc HbR (t)) of 20 participants are recorded for mental workload and rest. The statistical analysis shows that data obtained from fNIRS is statistically significant with p 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 FVMBLL 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.
Highlights
Stress measurement is a key factor in enhancement of efficiency of a task
Four main data classification features of the Modified Beer-Lambert law (MBLL) and Fixed-Value Modified Beer-Lambert law (FV-MBLL) data were used in support vector machine (SVM) classifiers to measure the mentalworkload / rest-state discriminative accuracies
The significances of the discriminative accuracies were arbitrated across both MBLL and FV-MBLL data sets obtained from 20 participants
Summary
Stress measurement is a key factor in enhancement of efficiency of a task. If the stress of a person can be predicted, the quality of work and health of the person can be improved. The measurement of stress can be done in different ways: some techniques [1]–[3] use facial gestures, some use questioners to assess the stress level, some methods involve recording of respiration and cardiac activity and recent researches use brain signal recording to monitor the stress and anxiety state in the brain. Neuronal signals recorded by electroencephalography (EEG) are used to monitor the cognitive state of the brain. EEG records stress state in the form of passive neuronal activity of the brain. Several EEG researches [4]–[6] have shown that mental workload can be measured from dorsolateral region of the brain. The anxiety, fatigue stress can be measured from the same brain regions [7], [8]
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