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

A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data