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Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: An fNIRS pilot study
- Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: An fNIRS pilot study
- An, Jin Ung; Jin, Sang Hyeon; Lee, Seung Hyun; Jang, Gwang Hee; Abibullaev, Berdakh; Lee, Hyun Ju; Moon, Jeon Il
- DGIST Authors
- An, Jin Ung; Jin, Sang Hyeon; Jang, Gwang Hee; Abibullaev, Berdakh; Lee, Hyun Ju; Moon, Jeon Il
- Issue Date
- 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, 6345-6348
- Article Type
- Conference Paper
- Passive movement, action observation and motor imagery as well as motor execution have been suggested to facilitate the motor function of human brain. The purpose of this study is to investigate the cortical activation patterns of these four modes using a functional near-infrared spectroscopy (fNIRS) system. Seven healthy volunteers underwent optical brain imaging by fNIRS. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated premotor cortex, there were considerable differences between modes. The pattern of neural activation in motor execution was best resembled by passive movement, followed by motor imagery, and lastly by action observation. This result indicates that action observation may be the least preferred way to activate the sensorimotor cortices. Thus, in order to show the feasibility of motor facilitation by a brain computer interface (BCI) for an extreme case, we paradoxically adopted the observation as a control input of the BCI. An observation-FES integrated BCI activated sensorimotor system stronger than observation but slightly weaker than FES. This limitation should be overcome to utilize the observation-FES integrated BCI as an active motor training method. © 2013 IEEE.
- Institute of Electrical and Electronics Engineers
- Related Researcher
An, Jin Ung
Brain Robot Interaction Lab
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