<b>fNIRS Dataset during Hand-gripping Activity</b>
<p dir="ltr">The dataset comprises <b>functional near-infrared spectroscopy (fNIRS) recordings of hand-gripping motor activity</b>. The data is provided in three forms:</p><ol><li><b>Raw Data</b>: Unprocessed optical density values recorded u...
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2025
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| Summary: | <p dir="ltr">The dataset comprises <b>functional near-infrared spectroscopy (fNIRS) recordings of hand-gripping motor activity</b>. The data is provided in three forms:</p><ol><li><b>Raw Data</b>: Unprocessed optical density values recorded using the <b>nirSpot-2</b> system.</li><li><b>Processed Data</b>: Data converted into <b>oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentration changes</b> using standard preprocessing techniques.</li><li><b>Labeled Data</b>: A fully processed and labeled version of the dataset, facilitating classification tasks.</li></ol><p dir="ltr">Further details regarding the dataset, including acquisition methodology, preprocessing steps, and potential applications, are provided in the manuscript:</p><p dir="ltr">Akhter, J., Naseer, N., Nazeer, H., Khan, H., & Mirtaheri, P. (2024). Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer Interface Application. <i>Sensors</i>, <i>24</i>(10), 3040.</p> |
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