Classification of FNIRS Using Wigner-ville Distribution and CNN

Consumers undergo an intellectual burden when working with technological programs. Mostly in situations of several activities. For instance, while communicating when driving with the navigation device. It is not necessary to divert users from their primary duties in such circumstances. In memory cycles and related workload, the pre-frontal cortex (PFC) has a significant role to play. In this study, we have used data from 10 participants to evaluate the task behaviors in PFC with usable near-infrared spectroscopy (fNIRS), which is a non-invasive imaging modality. In classification, CNN research has been state of the art. This has undermined the need to extract features manually. In order to assess the mental workload, we implemented a time-frequency approach with CNN approach. Rather than traditional CNN network we used ResNet50 pretrained network here. Application of Wigner-Ville Distribution in Functional Imaging is introduced here. The proposed CNN approach achieves a considerable average improvement relative to conventional methods. The results across differences in time window length are benchmarked. Satisfactory result obtained with twenty five second window for which the CNN yields 98% correct classification where traditional CNN achieved 89% accuracy.