  Functional near-infrared spectroscopy (fNIRS) is a non-invasive, economical
method used to study its blood flow pattern. These patterns can be used to
classify tasks a subject is performing. Currently, most of the classification
systems use simple machine learning solutions for the classification of tasks.
These conventional machine learning methods, which are easier to implement and
interpret, usually suffer from low accuracy and undergo a complex preprocessing
phase before network training. The proposed method converts the raw fNIRS time
series data into an image using Gramian Angular Summation Field. A Deep
Convolutional Neural Network (CNN) based architecture is then used for task
classification, including mental arithmetic, motor imagery, and idle state.
Further, this method can eliminate the feature selection stage, which affects
the traditional classifiers' performance. This system obtained 87.14% average
classification accuracy higher than any other method for the dataset.
