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Magnetic Resonance Imaging
Published in Shoogo Ueno, Bioimaging, 2020
What makes functional MRI superior to various other functional neuroimaging techniques is that it is noninvasive and has high spatial resolution. Other methods for obtaining brain function images include positron emission tomography (PET) and single photon emission computed tomography (SPECT), but these require radioactive tracer agents. In contrast, functional MRI does not need tracers or contrast agents and detects neuron activity through the body’s inherent hemoglobin magnetic properties. In terms of spatial resolution, functional MRI is superior to PET and SPECT in many cases. In addition, electroencephalography (EEG) and magnetoencephalography (MEG) are also noninvasive and detect neuronal electricity activity based on head surface potential and magnetic fields, so they have the advantage of making high temporal resolution on the order of milliseconds possible. However, an inverse problem needs to be solved to estimate neuronal current distribution in the brain from measured potential and magnetic fields on the brain surface. The issue with consistently finding a solution to this inverse problem is that it has difficult mathematical properties. In functional MRI, consistent high-resolution mapping is achievable without such difficulties. Near-infrared spectroscopy (NIRS) is another technology for detecting changes in the hemoglobin oxygenation rate with surface optical sensors, but NIRS can only measure the cerebral cortex, while functional MRI is capable of visualizing even deep brain activity.
Magnetoresistive Sensors Based on Magnetic Tunneling Junctions
Published in Evgeny Y. Tsymbal, Igor Žutić, Spintronics Handbook: Spin Transport and Magnetism, Second Edition, 2019
Nearly every physical object generates magnetic fields, some strong and some extremely weak. A human heart generates picotesla-scale (10−12 T) magnetic pulses, revealing critical cardiac information. A spinning disk inside a hard drive emits magnetic signals with frequency approaching 1 GHz, making the information age possible. The Earth’s magnetic field can be a useful navigation tool, particularly where global positioning systems (GPS) are not accessible (e.g., underground and deep sea). Magnetic sensors have been used pervasively in industrial and consumer products [3]. Ultrasensitive magnetic sensors find increasing utility in a number of emerging applications [3]. Magnetocardiography (MCG) [4] uses magnetic sensors to measure the weak electrical signals from the beating heart, allowing the diagnostics of cardiac functions. Magnetoencephalography (MEG) [5], on the other hand, is the magnetic measurement of the electrical activities in the brain. The information obtained from MEG can be used to pinpoint problem regions in the brain of a patient to minimize the invasiveness of brain surgery. Ultrasensitive magnetic sensors used in MCG and MEG are expensive superconducting quantum interference devices (SQUIDs), which require low-temperature operation.
Processing Techniques and Analysis of Brain Sensor Data Using Electroencephalography
Published in Mridu Sahu, G. R. Sinha, Brain and Behavior Computing, 2021
Magnetoencephalography (MEG) data acquisition techniques record the magnetic fields which are naturally produced by electrical currents in the brain. The sensitive magnetometers measure the perpendicularly oriented magnetic fields that are generated by electrical currents. To extract the MEG-based brain data, specially designed magnetic shielded rooms are needed due to the weak magnetic signals emitted by the brain [6]. As MEG signals are obtained directly from electrical neuronal activities, they provide both temporal characteristics and spatial localization.
Brain-machine and muscle-machine bio-sensing methods for gesture intent acquisition in upper-limb prosthesis control: a review
Published in Journal of Medical Engineering & Technology, 2021
Magnetoencephalography (MEG) is a technique used to measure the magnetic field associated with neuronal electrical activities in the brain. A number of gesture recognition and classification studies have been done using MEG. Although a range of accuracies has been obtained by authors in various studies using MEG, it is said that there is a similarity in the performance capability of both MEG and electroencephalography (EEG) when it comes to distinguishing finger-based gestures [39,80–82]. Sugata et al. [80] used MEG sensing to classify upper-limb motions and obtained accuracy in the region of 66%, while Kauhanen et al. [81] were able to classify finger-based movements from MEG signals and achieved accuracy in the range of 57–94%.
An automated magnetoencephalographic data cleaning algorithm
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2019
Antonietta Sorriso, Pierpaolo Sorrentino, Rosaria Rucco, Laura Mandolesi, Giampaolo Ferraioli, Stefano Franceschini, Michele Ambrosanio, Fabio Baselice
Magnetoencephalography (MEG) allows to record the electrical activity of the brain and to study, non-invasively, its temporal dynamics. Subject’s head is positioned within an helmet containing SQUID sensors, which measure the magnetic field produced by the brain. Such a magnetic field is extremely weak, about eight orders of magnitude lower than that of the earth, thus, the recorded signals are very sensitive to external noise.