Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity
Abstract Background Functional near-infrared spectroscopy (fNIRS) is a tool to assess brain activity during cognitive testing. Despite its usefulness, its feasibility in assessing mental workload remains unclear. This study was to investigate the potential use of convolutional neural networks (CNNs)...
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Neurology |
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| Online Access: | https://doi.org/10.1186/s12883-023-03504-z |
| _version_ | 1827581569605304320 |
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| author | Jin-Hyuck Park |
| author_facet | Jin-Hyuck Park |
| author_sort | Jin-Hyuck Park |
| collection | DOAJ |
| description | Abstract Background Functional near-infrared spectroscopy (fNIRS) is a tool to assess brain activity during cognitive testing. Despite its usefulness, its feasibility in assessing mental workload remains unclear. This study was to investigate the potential use of convolutional neural networks (CNNs) based on functional near-infrared spectroscopy (fNIRS)-derived signals to classify mental workload in individuals with mild cognitive impairment. Methods Spatial images by constructing a statistical activation map from the prefrontal activity of 120 subjects with MCI performing three difficulty levels of the N-back task (0, 1, and 2-back) were used for CNNs. The CNNs were evaluated using a 5 and 10-fold cross-validation method. Results As the difficulty level of the N-back task increased, the accuracy decreased and prefrontal activity increased. In addition, there was a significant difference in the accuracy and prefrontal activity across the three levels (p’s < 0.05). The accuracy of the CNNs based on fNIRS-derived spatial images evaluated by 5 and 10-fold cross-validation in classifying the difficulty levels ranged from 0.83 to 0.96. Conclusion fNIRS could also be a promising tool for measuring mental workload in older adults with MCI despite their cognitive decline. In addition, this study demonstrated the feasibility of the classification performance of the CNNs based on fNIRS-derived signals from the prefrontal cortex. |
| first_indexed | 2024-03-08T22:37:16Z |
| format | Article |
| id | doaj.art-541bdc748fb64e5badf4815d4a1fce7a |
| institution | Directory Open Access Journal |
| issn | 1471-2377 |
| language | English |
| last_indexed | 2024-03-08T22:37:16Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Neurology |
| spelling | doaj.art-541bdc748fb64e5badf4815d4a1fce7a2023-12-17T12:21:16ZengBMCBMC Neurology1471-23772023-12-012311810.1186/s12883-023-03504-zMental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activityJin-Hyuck Park0Department of Occupational Therapy, College of Medical Science, Soonchunhyang UniversityAbstract Background Functional near-infrared spectroscopy (fNIRS) is a tool to assess brain activity during cognitive testing. Despite its usefulness, its feasibility in assessing mental workload remains unclear. This study was to investigate the potential use of convolutional neural networks (CNNs) based on functional near-infrared spectroscopy (fNIRS)-derived signals to classify mental workload in individuals with mild cognitive impairment. Methods Spatial images by constructing a statistical activation map from the prefrontal activity of 120 subjects with MCI performing three difficulty levels of the N-back task (0, 1, and 2-back) were used for CNNs. The CNNs were evaluated using a 5 and 10-fold cross-validation method. Results As the difficulty level of the N-back task increased, the accuracy decreased and prefrontal activity increased. In addition, there was a significant difference in the accuracy and prefrontal activity across the three levels (p’s < 0.05). The accuracy of the CNNs based on fNIRS-derived spatial images evaluated by 5 and 10-fold cross-validation in classifying the difficulty levels ranged from 0.83 to 0.96. Conclusion fNIRS could also be a promising tool for measuring mental workload in older adults with MCI despite their cognitive decline. In addition, this study demonstrated the feasibility of the classification performance of the CNNs based on fNIRS-derived signals from the prefrontal cortex.https://doi.org/10.1186/s12883-023-03504-zClassificationWorkloadsFunctional neuroimagingCognitive impairmentDeep learning |
| spellingShingle | Jin-Hyuck Park Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity BMC Neurology Classification Workloads Functional neuroimaging Cognitive impairment Deep learning |
| title | Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity |
| title_full | Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity |
| title_fullStr | Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity |
| title_full_unstemmed | Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity |
| title_short | Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity |
| title_sort | mental workload classification using convolutional neural networks based on fnirs derived prefrontal activity |
| topic | Classification Workloads Functional neuroimaging Cognitive impairment Deep learning |
| url | https://doi.org/10.1186/s12883-023-03504-z |
| work_keys_str_mv | AT jinhyuckpark mentalworkloadclassificationusingconvolutionalneuralnetworksbasedonfnirsderivedprefrontalactivity |