Functional Near-Infrared Spectroscopy (fNIRS) is gaining popularity in detection and classification of cognitive and emotional states. In addition to hemodynamic responses arising from functional activity changes in the brain areas of interest, fNIRS signals contain components related to other physiological processes, such as respiration (frequency oscillations around 0.3 Hz) and cardiac pulsation (around 1 Hz). While heart rate and respiration measures have been successfully used as separate modalities to assess mental workload, these components are often discarded in fNIRS studies during the pre-processing. In this study, we examined whether including features related to heart and breathing rate improves the accuracy of mental workload level classification. Data collected with wearable fNIRS devices from 14 healthy participants performing mental workload task (n-back) were used to extract features for the classification. Machine learning classifiers were trained and tested using conventional features separately and in combination with the features derived from the oscillatory activity of respiration and heart pulsation. By comparing the performance, we demonstrated the effect of including proposed features on the classification accuracy of mental workload. In future studies, the examined features might be beneficial for other classification problems where modulations in heart and breathing rates are expected.
Functional near infrared spectroscopy (fNIRS) is used for brain hemodynamic assessment. Cortical hemodynamics are reliably estimated when the recorded signal has a sufficient quality. This is acquired when fNIRS optodes have proper scalp coupling. A lack of proper scalp coupling causes false positives and false negatives. Therefore, developing an objective algorithm for determining fNIRS signal quality is of great importance. In this study, we developed a machine learning-based algorithm for quantitatively rating fNIRS signal quality. Our promising results confirm the efficacy of the algorithm in determining fNIRS signal quality and hence decreasing misinterpretations.
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