Conference proceeding
Predicting Future Performance based on Current Brain Activity: An fNIRS and EEG Study
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), v 2019-, pp 3925-3930
Oct 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A familiar construct in everyday life is the use of past performance to predict future performance of individuals such as human operators. Here, we propose that brain activity measured during cognitive task execution can be used for prediction of future performance of the same individual on the same task. We recorded multimodal wearable neuroimaging data (Functional Near Infrared Spectroscopy and Electroencephalogram) from twenty-three volunteers performing a cognitive task on three different days. We have analyzed the relationship of brain activity and behavior for both within and across sessions. Preliminary results across sessions show that, as expected, past performance is related to future performance during other sessions to an extent. However, brain activity captured during the task is a better predictor of the future performance compared to current performance. Moreover, within session results show that medial prefrontal cortex brain activity is correlated with imminent future performance as well. These are the first multimodal neuroimaging results suggesting that brain activity has macro (across days) and micro (across seconds) level links to performance.
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Details
- Title
- Predicting Future Performance based on Current Brain Activity: An fNIRS and EEG Study
- Creators
- Hasan Ayaz - Drexel UniversityAdrian Curtin - Drexel UniversityJesse Mark - Drexel UniversityAmanda Kraft - Lockheed Martin Advanced Technology LaboratoriesMatthias Ziegler - Lockheed Martin Advanced Technology LaboratoriesIEEE
- Publication Details
- 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), v 2019-, pp 3925-3930
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000521353903152
- Scopus ID
- 2-s2.0-85076743317
- Other Identifier
- 991019170116004721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Information Systems