  Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost
method used to study the brain's blood flow pattern. Such patterns can enable
us to classify performed by a subject. In recent research, most classification
systems use traditional machine learning algorithms for the classification of
tasks. These methods, which are easier to implement, usually suffer from low
accuracy. Further, a complex pre-processing phase is required for data
preparation before implementing traditional machine learning methods. The
proposed system uses a Bi-Directional LSTM based deep learning architecture for
task classification, including mental arithmetic, motor imagery, and idle state
using fNIRS data. Further, this system will require less pre-processing than
the traditional approach, saving time and computational resources while
obtaining an accuracy of 81.48\%, which is considerably higher than the
accuracy obtained using conventional machine learning algorithms for the same
data set.
