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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
Regarding the ultrafast activity (>100 Hz), Kevan Hashemi [4] reports that the highest frequency fluctuation observed in EEG goes up to 120 Hz. He showed that the high-frequency oscillations (HFO) and very high-frequency oscillations (VHFO) reported to exist in animal and human EEG by some authors (e.g. see [20]) may be artifacts of band-pass filtering or a mismeasurement, i.e., an artifact of electromyographic activity related to other neural function, such as minute eye movements.28
Fast Rhythms In Respiratory Neural Activities
Published in Alan D. Miller, Armand L. Bianchi, Beverly P. Bishop, Neural Control of the Respiratory Muscles, 2019
Morton I. Cohen, Wu-Xin Huang, Wolf R. See, Yu Qiping, Constantinos N. Christakos
High-frequency oscillations (HFO) of activity (frequency range 50 to 120 Hz) are widespread in discharges of I nerves, in firing of medullary I neurons, and in membrane potentials of I and E neurons during the I phase. The HFOs in different signals are correlated (coherent), indicating that they arise from a common source, which may be an unspecified subpopulation of the I pattern generator. It is thought that the HFOs in I motoneuron discharges are due to excitatory inputs from medullary I neurons, and the HFOs in MPs of E neurons are due to inhibitory inputs (IPSPs) from I neurons.
Augmenting Attention with Brain–Computer Interfaces
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Mehdi Ordikhani-Seyedlar, Mikhail A. Lebedev
Analysis of oscillatory neural activity, for example, EEG rhythms sampled over different cortical areas, is a common method to extract neural features for endogenous BCIs. For example, EEG time-frequency analysis detects transient occurrences of neural oscillations, which in turn could be used to detect attentional shifts (Sanei & Chambers 2008). High-frequency oscillations (with a frequency greater than 30 Hz) indicate increased attention, as evident from EEG studies in humans (Kaiser & Lutzenberger 2005; Koelewijn et al. 2013; Musch et al. 2014) and intracranial recordings in monkeys (Fries et al. 2001).
EEG coherence and power spectra during REM sleep related to melatonin intake in mild-to-moderate Alzheimer’s disease: a pilot study
Published in International Journal of Neuroscience, 2023
Manuel Alejandro Cruz-Aguilar, Ignacio Ramírez-Salado, Marisela Hernández-González, Miguel Angel Guevara, Ana Paula Rivera-García
In other findings, high-frequency neuronal oscillations have been identified during REM sleep. Some studies have reported that high-frequency oscillations ranging from 30-80 Hz (γ frequencies) can be registered during REM sleep and are considered representative of this sleep stage. The frequencies recorded during REM sleep have been associated, hypothetically, with cognitive and perceptual processes, memory processing, and the temporal binding of dream imagery. Desynchronization of γ oscillations between anterior and posterior brain regions has also been described during REM sleep [20]. Other studies have reported a relation between γ activity and the reporting of dream memories [21, 22]. All this evidence suggests a fundamental role for τ and γ activity in memory consolidation processes during REM sleep. For this reason, the study of EEG activity during this sleep stage is relevant to Alzheimer’s disease.
The process of transferring negative impulses in capital markets – a wavelet analysis
Published in Journal of Applied Statistics, 2022
The proposed hypothesis assumes that the occurrence of contagion requires an impulse, as in the case of examining causality, hence it must be associated with the market’s lagged response. Covariance (the simultaneous response) is, on the other hand, a reflection of the financial markets’ interconnections. A successful verification of the proposed hypothesis would make it possible to depart from comparing correlations in the virtual tranquil and crisis periods and focus more attention on time lags. The other hypothesis to be verified assumes that limiting the scope of the analysis to high frequency oscillations only is an oversimplification. Responses to financial shocks may lose their intensity over a short period (up to four days), after which the markets regain their confidence relatively quickly. Delayed responses during financial crises may also be reflected by mid-term oscillations [4,5]. However, within short-term oscillations, corresponding to low scales, we can clearly see the very moment of contagion disclosure.
Focal cortical dysplasia: an update on diagnosis and treatment
Published in Expert Review of Neurotherapeutics, 2021
The relationship between HFOs and their pathological epileptogenic substrate is still matter of debate [76–78]. Jacobs and colleagues [76] found no relationship between HFO rates and distinct types of lesions (mesial temporal sclerosis, FCD, nodular heterotopia), thus suggesting that HFOs are nonspecific to a particular type of lesion, but rather reflect epileptogenicity per se. However, some evidence has emerged that certain brain lesions can generate HFOs more commonly than others. For instance, higher HFO rates were found in FCD and mesial temporal sclerosis compared to polymicrogyria and tuberous sclerosis [77]. In another study [78], high frequency oscillations rates were higher in FCD IIa and IIb than in FCD I. In these patients, HFO rates correlated with the overall seizure burden as measured over the last year rather than with the acute seizure burden during the long-term EEG recording, thus underscoring how HFOs could be used as a marker of the epileptogenic activity of the lesion.