Abstract
We investigated the use of a multimodal functional neuroimaging system in quantifying mental workload of healthy human volunteers. We recorded behavioral performance measures as well as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously from subjects performing n-back tasks. The EEG and fNIRS signals were used in feature generation and classification offline using support vector machines. We examined the classification accuracy of three distinct systems: EEG based; fNIRS based; and Hybrid, which contained features from the first two systems as based on their interactions. The classification accuracy of the Hybrid system was observed to be greater than that of either system, indicating the synergistic role played by multimodal signals and by neurovascular coupling in quantifying mental workload.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3773-3776 |
| Number of pages | 4 |
| Volume | 2016-October |
| ISBN (Electronic) | 9781457702204 |
| DOIs | |
| State | Published - Oct 13 2016 |
| Event | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: Aug 16 2016 → Aug 20 2016 |
Other
| Other | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
|---|---|
| Country/Territory | United States |
| City | Orlando |
| Period | 8/16/16 → 8/20/16 |
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics