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

• URL: http://arxiv.org/abs/2101.05892v1
• Date: Thu, 14 Jan 2021 22:21:15 GMT
• Title: A Ternary Bi-Directional LSTM Classification for Brain Activation Pattern Recognition Using fNIRS
• Authors: Sajila D. Wickramaratne and MD Shaad Mahmud
• Abstract summary: Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. The proposed system uses a Bi-Directional LSTM based deep learning architecture for task classification.
• Score: 0.15229257192293197
• License: http://creativecommons.org/licenses/by/4.0/
• 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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