2014
DOI: 10.1016/j.mex.2014.10.008
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Head movement compensation in real-time magnetoencephalographic recordings

Abstract: Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we pr… Show more

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Cited by 9 publications
(4 citation statements)
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“…Various approaches such as real-time source estimation are described that can be taken into account in future work now we have demonstrated that patients with chronic lung disease can tolerate the procedures and that useful data are possible. 37 38 Other methods of neural activity measurement such as EEG, for which head movement would not be an issue, should also be explored in future study in this area.…”
Section: Discussionmentioning
confidence: 99%
“…Various approaches such as real-time source estimation are described that can be taken into account in future work now we have demonstrated that patients with chronic lung disease can tolerate the procedures and that useful data are possible. 37 38 Other methods of neural activity measurement such as EEG, for which head movement would not be an issue, should also be explored in future study in this area.…”
Section: Discussionmentioning
confidence: 99%
“…3 ). However, online correction for head movement could potentially eliminate the need to recalibrate and has been demonstrated as feasible [ 47 ].…”
Section: Resultsmentioning
confidence: 99%
“…For example, prolonged video-MEG/EEG increase the probabilities to record and identify seizures [51]. Movement compensation [26,52] and the projection of recorded data to a standard virtual MEG helmet [53] enable the correction of (limited) patient movement during a seizure and increases the percentage of usable MEG recordings. Analytically, the application of STFT [27,28,31] for identification of seizure onsets, and the use of distributed source localization approaches [32,34], especially frequency-based methods [31], seem to improve the localization accuracy in comparison to ECD approaches.…”
Section: Discussionmentioning
confidence: 99%