Journal article
Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS
Computational intelligence and neuroscience, v 2017, pp 5491296-12
04 May 2017
PMID: 28546809
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS). Four simple finger- or toe-tapping tasks (left hand, right hand, left foot, and right foot) were performed with both motor imagery and motor execution and compared to resting state. Significant activation was found during all four motor imagery tasks, indicating that they can be detected via fNIRS. Motor execution produced higher activation levels, a faster response, and a different spatial distribution compared to motor imagery, which should be taken into account when designing an imagery-based BCI. When comparing left versus right, upper limb tasks are the most clearly distinguishable, particularly during motor execution. Left and right lower limb activation patterns were found to be highly similar during both imagery and execution, indicating that higher resolution imaging, advanced signal processing, or improved subject training may be required to reliably distinguish them.
Metrics
Details
- Title
- Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS
- Creators
- Alyssa M Batula - Department of Electrical and Computer Engineering Drexel University 3141 Chestnut Street Philadelphia PA 19104 USA drexel.eduJesse A Mark - School of Biomedical Engineering Science and Health Systems Drexel University 3141 Chestnut Street Philadelphia PA 19104 USA drexel.eduYoungmoo E Kim - Department of Electrical and Computer Engineering Drexel University 3141 Chestnut Street Philadelphia PA 19104 USA drexel.eduHasan Ayaz - School of Biomedical Engineering Science and Health Systems Drexel University 3141 Chestnut Street Philadelphia PA 19104 USA drexel.edu
- Contributors
- Mikhail A Lebedev (Editor)
- Publication Details
- Computational intelligence and neuroscience, v 2017, pp 5491296-12
- Publisher
- Hindawi
- Grant note
- DGE-1002809 / National Science Foundation
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000401374100001
- Scopus ID
- 2-s2.0-85019595126
- Other Identifier
- 991014878183804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Neurosciences