Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes
Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neur...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Neuroergonomics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnrgo.2023.1233722/full |
| _version_ | 1797492973636681728 |
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| author | Guillermo I. Gallegos Ayala David Haslacher Laurens R. Krol Laurens R. Krol Surjo R. Soekadar Thorsten O. Zander Thorsten O. Zander |
| author_facet | Guillermo I. Gallegos Ayala David Haslacher Laurens R. Krol Laurens R. Krol Surjo R. Soekadar Thorsten O. Zander Thorsten O. Zander |
| author_sort | Guillermo I. Gallegos Ayala |
| collection | DOAJ |
| description | Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons. |
| first_indexed | 2024-03-10T01:13:19Z |
| format | Article |
| id | doaj.art-90981afaede14837afbe57361e59789d |
| institution | Directory Open Access Journal |
| issn | 2673-6195 |
| language | English |
| last_indexed | 2024-03-10T01:13:19Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroergonomics |
| spelling | doaj.art-90981afaede14837afbe57361e59789d2023-11-23T14:13:05ZengFrontiers Media S.A.Frontiers in Neuroergonomics2673-61952023-11-01410.3389/fnrgo.2023.12337221233722Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudesGuillermo I. Gallegos Ayala0David Haslacher1Laurens R. Krol2Laurens R. Krol3Surjo R. Soekadar4Thorsten O. Zander5Thorsten O. Zander6Department of Psychiatry and Neurosciences, Clinical Neurotechnology Laboratory, Charité – Universitätsmedizin Berlin, Berlin, GermanyDepartment of Psychiatry and Neurosciences, Clinical Neurotechnology Laboratory, Charité – Universitätsmedizin Berlin, Berlin, GermanyNeuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Brandenburg, GermanyZander Laboratories B.V., Amsterdam, NetherlandsDepartment of Psychiatry and Neurosciences, Clinical Neurotechnology Laboratory, Charité – Universitätsmedizin Berlin, Berlin, GermanyNeuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Brandenburg, GermanyZander Laboratories B.V., Amsterdam, NetherlandsBrain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.https://www.frontiersin.org/articles/10.3389/fnrgo.2023.1233722/fullbrain-computer interfacemental workloadpassive BCIclassification of neural signalssupport vector machinecognitive task |
| spellingShingle | Guillermo I. Gallegos Ayala David Haslacher Laurens R. Krol Laurens R. Krol Surjo R. Soekadar Thorsten O. Zander Thorsten O. Zander Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes Frontiers in Neuroergonomics brain-computer interface mental workload passive BCI classification of neural signals support vector machine cognitive task |
| title | Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes |
| title_full | Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes |
| title_fullStr | Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes |
| title_full_unstemmed | Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes |
| title_short | Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes |
| title_sort | assessment of mental workload across cognitive tasks using a passive brain computer interface based on mean negative theta band amplitudes |
| topic | brain-computer interface mental workload passive BCI classification of neural signals support vector machine cognitive task |
| url | https://www.frontiersin.org/articles/10.3389/fnrgo.2023.1233722/full |
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