AI Article Synopsis
- Multivariate pattern analysis (MVPA) is effective in cognitive neuroscience and can enhance magnetoencephalography (MEG) experiments with optically-pumped magnetometers (OPM).
- The study found that using signal space separation (SSS) for noise reduction can lower classification accuracy, and that about 30 sensors are sufficient for reliable MVPA in image categorization tasks.
- It is advised to avoid SSS filters without proper regularization, as they can increase broadband noise; alternative noise reduction methods like signal-space projection and homogeneous field correction are recommended for better results.
Article Abstract
Background: Multivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography.
New Method: To optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We determined the least required number of sensors needed for robust MVPA for image categorization experiments.
Results: We found that the use of signal space separation (SSS) without a proper regularization significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied.
Comparison With Existing Methods: The power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed.
Conclusions: When designing MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters without a proper regularization to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jneumeth.2024.110279 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Spatial transcriptomics (ST) provides critical insights into the complex spatial organization of gene expression in tissues, enabling researchers to unravel the intricate relationship between cellular environments and biological function. Identifying spatial domains within tissues is essential for understanding tissue architecture and the mechanisms underlying various biological processes, including development and disease progression. Here, we present Randomized Spatial PCA (RASP), a novel spatially aware dimensionality reduction method for spatial transcriptomics (ST) data.
View Article and Find Full Text PDFAutomatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.
BMC Med Imaging
January 2025
Electronics and Communications, Arab Academy for Science, Heliopolis, Cairo, 2033, Egypt.
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images.
View Article and Find Full Text PDFDesign optimization of a 1-D array of stemless plastic scintillation detectors.
Med Phys
January 2025
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada.
Background: A stemless plastic scintillation detector (SPSD) is composed of an organic plastic scintillator coupled to an organic photodiode. Previous research has shown that SPSDs are ideally suited to challenging dosimetry measurements such as output factors and profiles in small fields. Lacking from the current literature is a systematic effort to optimize the performance of the photodiode component of the detector.
View Article and Find Full Text PDFUsing a novel smartphone app to track noise and vibration exposure during neonatal ambulance transport.
Arch Dis Child Fetal Neonatal Ed
January 2025
Centre for Perinatal Research, University of Nottingham, School of Medicine, Nottingham, UK
Objective: To assess the utility of a bespoke smartphone app to map noise and vibration exposure across neonatal road ambulance journeys.
Design And Setting: Prospective observational study of ambulance journeys across a large UK neonatal transport service. Smartphones, with an in-house developed app, were secured to incubator trolleys to collect vibration and noise data for comparison with international standards.
The power of music: impact on EEG signals.
Psychol Res
January 2025
Department of Neurology and Clinical Neurophysiology Unit, Faculty of Medicine-Cairo University, Cairo, Egypt.
Introduction: Music is known to impact attentional state without conscious awareness. Listening to music encourages the brain to secrete neurotransmitters improving cognition and emotion.
Aim Of Work: Analysis of QEEG band width while listening to two music types, identifying different cortical areas activated and which genre has a similar effect to relaxed EEG.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!