A machine learning approach to classify working memory load from optical neuroimaging data

Manob Jyoti Saikia; Shiba Kuanar; Debanjan Borthakur; Maria Vinti; Thupten Tendhar

doi:10.1117/12.2578952

5 March 2021 A machine learning approach to classify working memory load from optical neuroimaging data

Manob Jyoti Saikia, Shiba Kuanar, Debanjan Borthakur, Maria Vinti, Thupten Tendhar

Author Affiliations +

Proceedings Volume 11629, Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics; 116292K (2021) https://doi.org/10.1117/12.2578952
Event: SPIE BiOS, 2021, Online Only

Conference Poster

Abstract

Functional near-infrared spectroscopy (fNIRS) is a brain imaging methodology that is appertaining to imaging the hemodynamic responses or blood oxygenation level-dependency of the brain. Machine learning (ML) has the potential to classify different mental states from the fNIRS signal to build a brain-computer interface (BCI) system. In this work, we have used a portable fNIRS system, WearLight, to image the prefrontal cortex of the brain of 12 participants while performing n-back working memory (WM) tasks. We converted the time-series raw data to the hemodynamic response by implementing a processing pipeline. A simple method based on checking the hemodynamic signal level of all the channels during task blocks with respect to the rest periods was used to identify the thee most dominant channels. We extracted eight important features from the hemodynamic brain signals to construct the feature matrix and trained six different k-nearest neighbors (k-NN) ML classifiers. The performance of the six k-NN classifiers was evaluated with the new experimental data sets. The results have shown that Weighted and Fine k-NN performed best (75%) in classifying the 4 different WM loads. The method is encouraging for real-time classification of the mental workload using a portable fNIRS system augmented by advanced machine learning techniques.

Conference Presentation

Citation Download Citation

Manob Jyoti Saikia, Shiba Kuanar, Debanjan Borthakur, Maria Vinti, and Thupten Tendhar "A machine learning approach to classify working memory load from optical neuroimaging data", Proc. SPIE 11629, Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics, 116292K (5 March 2021); https://doi.org/10.1117/12.2578952

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