A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment
Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessmen...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10416860/ |
| _version_ | 1797304875673976832 |
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| author | Konstantina Kyriaki Dimitrios Koukopoulos Christos A. Fidas |
| author_facet | Konstantina Kyriaki Dimitrios Koukopoulos Christos A. Fidas |
| author_sort | Konstantina Kyriaki |
| collection | DOAJ |
| description | Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment. Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users’ cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments. |
| first_indexed | 2024-03-08T00:16:43Z |
| format | Article |
| id | doaj.art-0985ce67281347b1aad74c4d523c1719 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T00:16:43Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0985ce67281347b1aad74c4d523c17192024-02-17T00:01:18ZengIEEEIEEE Access2169-35362024-01-0112234662348910.1109/ACCESS.2024.336032810416860A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load AssessmentKonstantina Kyriaki0https://orcid.org/0009-0002-9170-9156Dimitrios Koukopoulos1https://orcid.org/0000-0001-7019-4224Christos A. Fidas2https://orcid.org/0000-0001-6111-0244Department of Electrical and Computer Engineering, University of Patras, Patras, GreeceDepartment of History and Archaeology, University of Patras, Patras, GreeceDepartment of Electrical and Computer Engineering, University of Patras, Patras, GreecePreprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment. Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users’ cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.https://ieeexplore.ieee.org/document/10416860/Cognitive loaddenoisingelectroencephalographypreprocessingreal-time human cognitive modellinguser studies |
| spellingShingle | Konstantina Kyriaki Dimitrios Koukopoulos Christos A. Fidas A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment IEEE Access Cognitive load denoising electroencephalography preprocessing real-time human cognitive modelling user studies |
| title | A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment |
| title_full | A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment |
| title_fullStr | A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment |
| title_full_unstemmed | A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment |
| title_short | A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment |
| title_sort | comprehensive survey of eeg preprocessing methods for cognitive load assessment |
| topic | Cognitive load denoising electroencephalography preprocessing real-time human cognitive modelling user studies |
| url | https://ieeexplore.ieee.org/document/10416860/ |
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