A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data
This work addresses the need for more accurate and less complex classification systems in fNIRS neuroimaging for tasks like mental arithmetic and motor imagery, representing an incremental improvement over existing methods.
The paper tackled the problem of low accuracy and complex preprocessing in fNIRS task classification by converting raw time series data into images using Gramian Angular Summation Field and applying a Deep CNN, achieving 87.14% average classification accuracy.
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.