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Stability of MEG for real-time neurofeedback

Foldes, ST and Vinjamuri, RK and Wang, W and Weber, DJ and Collinger, JL (2011) Stability of MEG for real-time neurofeedback. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 5778 - 5781. ISSN 1557-170X

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Movement-related field potentials can be extracted and processed in real-time with magnetoencephalography (MEG) and used for brain machine interfacing (BMI). However, due to its immense sensitivity to magnetic fields, MEG is prone to a low signal to noise ratio. It is therefore important to collect enough initial data to appropriately characterize motor-related activity and to ensure that decoders can be built to adequately translate brain activity into BMI-device commands. This is of particular importance for therapeutic BMI applications where less time spent collecting initial open-loop data means more time for performing neurofeedback training which could potentially promote cortical plasticity and rehabilitation. This study evaluated the amount of hand-grasp movement and rest data needed to characterize sensorimotor modulation depth and build classifier functions to decode brain states in real-time. It was determined that with only five minutes of initial open-loop MEG data, decoders can be built to classify brain activity as grasp or rest in real-time with an accuracy of 846%. © 2011 IEEE.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Foldes, STstf19@pitt.eduSTF19
Vinjamuri, RK
Wang, Wwangwei3@pitt.eduWANGWEI3
Weber, DJdougweber@pitt.eduDJW500000-0002-9782-3497
Collinger, JLcollinger@pitt.eduCOLLINGR0000-0002-4517-5395
Centers: Other Centers, Institutes, Offices, or Units > Human Engineering Research Laboratories
Date: 26 December 2011
Date Type: Publication
Journal or Publication Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Page Range: 5778 - 5781
DOI or Unique Handle: 10.1109/iembs.2011.6091430
Schools and Programs: School of Health and Rehabilitation Sciences > Rehabilitation Science and Technology
Refereed: Yes
ISSN: 1557-170X
MeSH Headings: Algorithms; Biofeedback, Psychology--methods; Biofeedback, Psychology--physiology; Computer Systems; Electroencephalography--methods; Evoked Potentials, Motor--physiology; Humans; Magnetoencephalography--methods; Motor Cortex--physiology; Movement--physiology; Reproducibility of Results; Sensitivity and Specificity
PubMed ID: 22255653
Date Deposited: 30 Jan 2013 20:58
Last Modified: 01 Sep 2023 20:55


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