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A scalp-measurement based parameter space: Towards locating TMS coils in a clinically-friendly way.
Brain Stimul
August 2022
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China. Electronic address:
Towards Best Practices in Clinical Magnetoencephalography: Patient Preparation and Data Acquisition.
J Clin Neurophysiol
November 2020
Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, Texas, U.S.A.
A magnetoencephalography (MEG) recording for clinical purposes requires a different level of attention and detail than that for research. As contrasted with a research subject, the MEG technologist must work with a patient who may not fully cooperate with instructions. The patient is on a clinical schedule, with generally no opportunity to return due to an erroneous or poor acquisition.
View Article and Find Full Text PDFDevelopment and validation of a 3D-printed neuronavigation headset for therapeutic brain stimulation.
J Neural Eng
August 2018
Institute of Biomaterial and Biomedical Engineering, University of Toronto, Toronto, Canada.
Background: Accurate neuronavigation is essential for optimal outcomes in therapeutic brain stimulation. MRI-guided neuronavigation, the current gold standard, requires access to MRI and frameless stereotaxic equipment, which is not available in all settings. Scalp-based heuristics depend on operator skill, with variable reproducibility across operators and sessions.
View Article and Find Full Text PDFImproved artefact removal from EEG using Canonical Correlation Analysis and spectral slope.
J Neurosci Methods
March 2018
College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia.
Article Synopsis
- EEG recordings often suffer from muscle artefacts, especially affecting high-frequency signals, which complicates the interpretation of subcortical brain activity.
- A new method combining Blind Source Separation-Canonical Correlation Analysis (BSS-CCA) with spectral slope analysis effectively detects and removes muscle artefacts while preserving brain signal integrity.
- This automatic approach outperforms traditional BSS-CCA in artefact removal, matches the effectiveness of ICA methods, and operates with lower computational complexity.
Analysis of task-evoked systemic interference in fNIRS measurements: insights from fMRI.
Neuroimage
February 2014
Department of Genetics and Bioengineering, Istanbul Bilgi University, 34060 Istanbul, Turkey.
Functional near infrared spectroscopy (fNIRS) is a promising method for monitoring cerebral hemodynamics with a wide range of clinical applications. fNIRS signals are contaminated with systemic physiological interferences from both the brain and superficial tissues, resulting in a poor estimation of the task related neuronal activation. In this study, we use the anatomical resolution of functional magnetic resonance imaging (fMRI) to extract scalp and brain vascular signals separately and construct an optically weighted spatial average of the fMRI blood oxygen level-dependent (BOLD) signal for characterizing the scalp signal contribution to fNIRS measurements.
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