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Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-costmethod used to study the brain's blood flow pattern. Such patterns can enableus to classify performed by a subject. In recent research, most classificationsystems use traditional machine learning algorithms for the classification oftasks. These methods, which are easier to implement, usually suffer from lowaccuracy. Further, a complex pre-processing phase is required for datapreparation before implementing traditional machine learning methods. Theproposed system uses a Bi-Directional LSTM based deep learning architecture fortask classification, including mental arithmetic, motor imagery, and idle stateusing fNIRS data. Further, this system will require less pre-processing thanthe traditional approach, saving time and computational resources whileobtaining an accuracy of 81.48\%, which is considerably higher than theaccuracy obtained using conventional machine learning algorithms for the samedata set.

A Ternary Bi-Directional LSTM Classification for Brain Activation Pattern Recognition Using fNIRS