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

TL;DR

This paper introduces a ternary bi-directional LSTM model for classifying brain activity patterns from fNIRS data, achieving higher accuracy with less pre-processing than traditional methods.

Contribution

It presents a novel deep learning architecture that improves classification accuracy and reduces pre-processing complexity for fNIRS-based brain state recognition.

Findings

Achieved 81.48% classification accuracy.

Reduced data pre-processing requirements.

Outperformed traditional machine learning methods.

Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. Such patterns can enable us to classify performed by a subject. In recent research, most classification systems use traditional machine learning algorithms for the classification of tasks. These methods, which are easier to implement, usually suffer from low accuracy. Further, a complex pre-processing phase is required for data preparation before implementing traditional machine learning methods. The proposed system uses a Bi-Directional LSTM based deep learning architecture for task classification, including mental arithmetic, motor imagery, and idle state using fNIRS data. Further, this system will require less pre-processing than the traditional approach, saving time and computational resources while obtaining an accuracy of 81.48\%, which is considerably higher than the accuracy obtained using conventional machine learning algorithms for the same data set.