Neural Signatures Within and Between Chess Puzzle Solving and Standard Cognitive Tasks for Brain-Computer Interfaces: A Low-Cost Electroencephalography Study
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908390657097728 |
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| author | Russell, Matthew Youkeles, Samuel Xia, William Zheng, Kenny Shah, Aman Jacob, Robert J. K. |
| author_facet | Russell, Matthew Youkeles, Samuel Xia, William Zheng, Kenny Shah, Aman Jacob, Robert J. K. |
| contents | Consumer-grade electroencephalography (EEG) devices show promise for Brain-Computer Interface (BCI) applications, but their efficacy in detecting subtle cognitive states remains understudied. We developed a comprehensive study paradigm which incorporates a combination of established cognitive tasks (N-Back, Stroop, and Mental Rotation) and adds a novel ecological Chess puzzles task. We tested our paradigm with the MUSE 2, a low-cost consumer-grade EEG device. Using linear mixed-effects modeling we demonstrate successful distinctions of within-task workload levels and cross-task cognitive states based on the spectral power data derived from the MUSE 2 device. With machine learning we further show reliable predictive power to differentiate between workload levels in the N-Back task, and also achieve effective cross-task classification. These findings demonstrate that consumer-grade EEG devices like the MUSE 2 can be used to effectively differentiate between various levels of cognitive workload as well as among more nuanced task-based cognitive states, and that these tools can be leveraged for real-time adaptive BCI applications in practical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Signatures Within and Between Chess Puzzle Solving and Standard Cognitive Tasks for Brain-Computer Interfaces: A Low-Cost Electroencephalography Study Russell, Matthew Youkeles, Samuel Xia, William Zheng, Kenny Shah, Aman Jacob, Robert J. K. Human-Computer Interaction Consumer-grade electroencephalography (EEG) devices show promise for Brain-Computer Interface (BCI) applications, but their efficacy in detecting subtle cognitive states remains understudied. We developed a comprehensive study paradigm which incorporates a combination of established cognitive tasks (N-Back, Stroop, and Mental Rotation) and adds a novel ecological Chess puzzles task. We tested our paradigm with the MUSE 2, a low-cost consumer-grade EEG device. Using linear mixed-effects modeling we demonstrate successful distinctions of within-task workload levels and cross-task cognitive states based on the spectral power data derived from the MUSE 2 device. With machine learning we further show reliable predictive power to differentiate between workload levels in the N-Back task, and also achieve effective cross-task classification. These findings demonstrate that consumer-grade EEG devices like the MUSE 2 can be used to effectively differentiate between various levels of cognitive workload as well as among more nuanced task-based cognitive states, and that these tools can be leveraged for real-time adaptive BCI applications in practical settings. |
| title | Neural Signatures Within and Between Chess Puzzle Solving and Standard Cognitive Tasks for Brain-Computer Interfaces: A Low-Cost Electroencephalography Study |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.07592 |