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arXiv · 2101.05892

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

Sajila D. Wickramaratne·MD Shaad Mahmud
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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.

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Sajila D. Wickramaratne, MD Shaad Mahmud. 2021-01-14. A Ternary Bi-Directional LSTM Classification for Brain Activation Pattern Recognition Using fNIRS. https://arxiv.org/abs/2101.05892

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