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Review of the Human Brain and EEG Signals
Published in Teodiano Freire Bastos-Filho, Introduction to Non-Invasive EEG-Based Brain–Computer Interfaces for Assistive Technologies, 2020
Alessandro Botti Benevides, Alan Silva da Paz Floriano, Mario Sarcinelli-Filho, Teodiano Freire Bastos-Filho
Beyond the interference of technical and physiological artifacts, the EEG is also affected by the electrical activity of the brain itself. The EEG of an area of interest is a mixture of unrelated signals from cortical neighboring areas that are scattered around and attenuated by the skull and scalp. This is considered a special type of artifact, in which there are no exact solutions for unmixing it from the EEG signal. This problem is known as the inverse problem that traditionally has infinite solutions, due to the nature of its variables. A particular inverse solution uses the calculation of local field potential (LFP), which is invasive recording of the electric potential in the extracellular space in brain tissue [26]. Another solution uses the distribution of cortical extracellular currents, known as cortical current density (CCD) [27].
Flexible and Stretchable Devices for Human-Machine Interfaces
Published in Muhammad Mustafa Hussain, Nazek El-Atab, Handbook of Flexible and Stretchable Electronics, 2019
Irmandy Wicaksono, Canan Dagdeviren
Due to noise signals and attenuation caused by the skin and bone barriers, research has also been conducted to fabricate an array of electrodes that can be laminated directly on the surface of the brain to perform electrocorticography (ECoG). Conventional invasive neural recordings and stimulations have mainly relied on rigid materials, such as silicon or metals which have a mechanical, geometrical, and biological mismatch to the brain tissues (Jog et al. 2002; Kim et al. 2009). These incompatibility issues could result in hemorrhage and inflammatory response. They could damage target tissues and influence the clarity of neural reading (Polikov et al. 2005; Saxena et al. 2013). Flexible and stretchable, minimally invasive implantable electronics could solve these challenges with a greater precision, sensitivity, as well as spatial and temporal resolution. Several researchers used a polymer layer as a substrate for micro-fabricated sensors and circuits. One example is a flexible multi-channel electrode array to simultaneously monitor ECoG and local field potential (LFP) in the visual cortex of a rat (Toda et al. 2011). The device consists of gold electrodes encapsulated in a Parylene C substrate. Visual stimulus from a monitor evoked a neural response from the rat, and recording results showed reliable data throughout the 2 weeks implantation.
Medical device implants for neuromodulation
Published in Ze Zhang, Mahmoud Rouabhia, Simon E. Moulton, Conductive Polymers, 2018
A biomarker is an objectively measured indicator of normal biological processes, pathogenic processes, or modulatory responses to a therapeutic intervention. To be translational, a biomarker needs to be evaluated in experimental settings, for example, in the measurement of normal or pathological activity in animals or humans. Five examples of current or previous biomarkers for DBS include the following: (1) beta-band (13–30 Hz) power in the local field potential recorded from the subthalamic region of the basal ganglia in Parkinson’s disease (Yang et al. 2014), (2) increased positron emission tomography (PET)–derived blood flow in the subgenual cingulate cortex in treatment-resistant depression (Mayberg et al. 1997), (3) pathological high-frequency oscillations in focal epilepsy (Bragin et al. 2004), (4) theta-band (4–8 Hz) oscillations in the field potential recorded from the medial pallidum in dystonia (Liu et al. 2002), and (5) idiopathic rapid eye movement (REM) sleep behavior disorder in Parkinson’s disease (Postuma et al. 2010). In these examples, the objective of DBS therapy is to modulate the electrophysiological biomarkers of disease states.
Nanocomposites of ferroelectric liquid crystals and FeCo nanoparticles: towards a magnetic response via the application of a small electric field
Published in Liquid Crystals, 2020
Patricio N. Romero-Hasler, Lynn K. Kurihara, Lamar O. Mair, Irving N. Weinberg, E. A Soto-Bustamante, L. J. Martínez-Miranda
We observe the motion of the spherulites on the substrate to monitor the alignment of the magnetic nanoparticles with polarised optical microscopy. These spherulites were on average about 5–10 μm in diameter. Their sizes vary, as can be seen from Figures 6 and 7, depending on where in the sample they are located. For example, Figure 6d,e were taken at the border of the sample, to better compare them with Figure 6a–d which were taken at the border of the sample. Figures 6f and 7 were taken in the bulk where the measurements were taken. An applied electric field will move these spherulites in the substrate such that they form lines at the higher electric fields, as shown in Figure 6f. The quantity of spherulites that move is calculated from the pictures shown in Figure 7 and plotted in Figure 8 as a function of applied electric field. We observe from that the spherulites increase in number as the electric field is brought from 0V/cm to 500V/cmFigure 8. We observe that there is an increase of spherulites at a field as low as 5V/cm. We note that an electric field of 5V/cm is comparable to the voltages observed in many biological states, for example the local field potential in the brain.
A Functional BCI Model by the P2731 working group: Physiology
Published in Brain-Computer Interfaces, 2021
Ali Hossaini, Davide Valeriani, Chang S. Nam, Raffaele Ferrante, Mufti Mahmud
Populations of neurons can be targeted with extracellular electrodes implanted in the region of interest. In these cases, electrodes are recording local field potentials (LFPs). It is generally accepted that an LFP represents a variety of voltage fluctuations generated by target populations of neurons [76]. Despite years of study, questions of how LFPs are generated, their spatial extent, and how they relate to signals acquired by different sensing methods, e.g. fMRI vs. EEG, are ongoing subjects of investigation [77–79]. The varying structures of neurons demonstrate and often cause the challenges associated with signal acquisition.