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

TL;DR

This paper introduces a deep learning system that transforms fNIRS data into images using Gramian Angular Summation Field and employs CNNs for task classification, achieving higher accuracy than traditional methods.

Contribution

It presents a novel approach converting raw fNIRS signals into images for CNN-based classification, eliminating the need for feature selection and improving accuracy.

Findings

Achieved 87.14% average classification accuracy.

Outperformed traditional machine learning methods.

Simplified preprocessing by removing feature selection step.

Abstract

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.