@inproceedings{36c685c709dc44e09248761a7f107fbe,
title = "Stability of MEG for real-time neurofeedback",
abstract = "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%.",
author = "Foldes, {S. T.} and Vinjamuri, {R. K.} and W. Wang and Weber, {D. J.} and Collinger, {J. L.}",
year = "2011",
doi = "10.1109/IEMBS.2011.6091430",
language = "English",
isbn = "9781424441211",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
pages = "5778--5781",
booktitle = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011",
note = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 ; Conference date: 30-08-2011 Through 03-09-2011",
}