  Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive form of
Brain-Computer Interface (BCI). It is used for the imaging of brain
hemodynamics and has gained popularity due to the certain pros it poses over
other similar technologies. The overall functionalities encompass the capture,
processing and classification of brain signals. Since hemodynamic responses are
contaminated by physiological noises, several methods have been implemented in
the past literature to classify the responses in focus from the unwanted ones.
However, the methods, thus far does not take into consideration the uncertainty
in the data or model parameters. In this paper, we use a Bayesian Neural
Network (BNN) to carry out a binary classification on an open-access dataset,
consisting of unilateral finger tapping (left- and right-hand finger tapping).
A BNN uses Bayesian statistics to assign a probability distribution to the
network weights instead of a point estimate. In this way, it takes data and
model uncertainty into consideration while carrying out the classification. We
used Variational Inference (VI) to train our model. Our model produced an
overall classification accuracy of 86.44% over 30 volunteers. We illustrated
how the evidence lower bound (ELBO) function of the model converges over
iterations. We further illustrated the uncertainty that is inherent during the
sampling of the posterior distribution of the weights. We also generated a ROC
curve for our BNN classifier using test data from a single volunteer and our
model has an AUC score of 0.855.
