Integrating structural and functional brain connectivity image, signal, and data processing problems

Baselli, Giuseppe; Bergsland, Niels; Costantini, Isa; Dipasquale, Ottavia; Scaccianoce, Elisa; Laganà, Maria Marcella; Pelizzari, Laura; Clerici, Mario; Baglio, Francesca

doi:10.1109/aeit.2015.7415273

Cited by 1 publication

(1 citation statement)

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“…A noninvasive method called magnetoencephalography (MEG) is used to measure the magnetic field that is caused by the activity of nerve bundles. Because of its sampling rate, it can look at fast brain activity right away [ 9 ]. MEG was used in this study to get a wide range of neurophysiologically relevant frequency bands because it has a very high temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Classification of EEG Signal-Based Encephalon Magnetic Signs for Identification of Epilepsy-Based Neurological Disorder

Kaur

Gupta

Kathiravan

et al. 2022

Computational and Mathematical Methods in Medicine

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Magnetoencephalography (MEG) is now widely used in clinical examinations and medical research in many fields. Resting-state magnetoencephalography-based brain network analysis can be used to study the physiological or pathological mechanisms of the brain. Furthermore, magnetoencephalography analysis has a significant reference value for the diagnosis of epilepsy. The scope of the proposed research is that this research demonstrates how to locate spikes in the phase locking functional brain connectivity network of the Desikan-Killiany brain region division using a neural network approach. It also improves detection accuracy and reduces missed and false detection rates. The automatic classification of epilepsy encephalomagnetic signals can make timely judgments on the patient’s condition, which is of tremendous clinical significance. The existing literature’s research on the automatic type of epilepsy EEG signals is relatively sufficient, but the research on epilepsy EEG signals is relatively weak. A full-band machine learning automatic discrimination method of epilepsy brain magnetic spikes based on the brain functional connection network is proposed. The four classifiers are comprehensively compared. The classifier with the best effect is selected, and the discrimination accuracy can reach 93.8%. Therefore, this method has a good application prospect in automatically identifying and labeling epileptic spikes in magnetoencephalography.

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Section: Introductionmentioning

confidence: 99%