High-Frequency Resolution EEG: Results and Opportunities

Salansky, N.; Федотчев, А. И.; Bondar, A. T.

doi:10.1080/00029238.1995.11080508

Cited by 7 publications

(5 citation statements)

References 82 publications

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“…As many studies have shown, pain stimuli evoke a very characteristic positive and negative electrical voltage-time diagram (EEG) composed of a mixture of voltage oscillations of different frequencies and potential components (half-waves or amplitudes) of the sERP, which are contained in the EEG and can be crystallized from the EEG by averaging the voltage-time diagrams in response to a series of stimuli (Bromm and Scharein, 1983 , 1990 ; Bromm et al, 1985 ; Miltner and Weiss, 1998 ). The composition of the EEG of different frequency bands can be determined by frequency-analytical mathematical models, either over fixed time intervals using Fourier analysis or time-synchronously by methods such as wavelet analysis (Salansky et al, 1995 ; Kelly et al, 1997 ; Wacker and Witte, 2013 ) and others. Depending on the time elapsed since stimulus application (latency), and the electrical orientation of the voltage-time diagram (P: positive, N: negative), in addition to the early and mid-latency components of the sERP (Schwender et al, 1997 ), so-called late components and ultra-late components have become especially interesting for studies on the effects of anesthetics and analgesic agents (for more details see below).…”

Section: Neurophysiological Correlates Of Pain Controlmentioning

confidence: 99%

Neuroscientific results of experimental studies on the control of acute pain with hypnosis and suggested analgesia

Miltner,

Franz,

Naumann

2024

Front. Psychol.

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This narrative review summarizes a representative collection of electrophysiological and imaging studies on the neural processes and brain sources underlying hypnotic trance and the effects of hypnotic suggestions on the processing of experimentally induced painful events. It complements several reviews on the effect of hypnosis on brain processes and structures of chronic pain processing. Based on a summary of previous findings on the neuronal processing of experimentally applied pain stimuli and their effects on neuronal brain structures in healthy subjects, three neurophysiological methods are then presented that examine which of these neuronal processes and structures get demonstrably altered by hypnosis and can thus be interpreted as neuronal signatures of the effect of analgesic suggestions: (A) On a more global neuronal level, these are electrical processes of the brain that can be recorded from the cranial surface of the brain with magnetoencephalography (MEG) and electroencephalography (EEG). (B) On a second level, so-called evoked (EPs) or event-related potentials (ERPs) are discussed, which represent a subset of the brain electrical parameters of the EEG. (C) Thirdly, imaging procedures are summarized that focus on brain structures involved in the processing of pain states and belong to the main imaging procedures of magnetic resonance imaging (MRI/fMRI) and positron emission tomography (PET). Finally, these different approaches are summarized in a discussion, and some research and methodological suggestions are made as to how this research could be improved in the future.

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Section: Neurophysiological Correlates Of Pain Controlmentioning

confidence: 99%

Neuroscientific results of experimental studies on the control of acute pain with hypnosis and suggested analgesia

Miltner,

Franz,

Naumann

2024

Front. Psychol.

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“…The smaller the FWHM of a PSD curve, the higher the frequency resolution. The higher the frequency resolution, the less interference can be captured by the EEG spectra [15].…”

Section: Data Processing and Analysismentioning

confidence: 99%

A Comparative Study of Alpha Frequency Analysis between Medical and Consumer-grade Electroencephalography Devices on the Measurement of Male Healthy Subjects

Suwandi,

Risyad,

Khotimah

et al. 2023

Mal. J. Fund. Appl. Sci.

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The relatively high cost of medical-grade electroencephalography (EEG) devices has pushed the production of low-cost wireless consumer-grade devices. Therefore, it is essential to assess the performance of wireless consumer devices to determine whether they are sufficient for medical purposes. This research assessed consumer-grade EEG (C-EEG) recording quality by quantitatively comparing the consumer-grade EEG with a medical-grade EEG device (M-EEG). Recording data from C-EEG and M-EEG were obtained from 20 male subjects in age 19-23 years old. Recording for both devices was done sequentially with similar methods of recording. Upon EEG recording, the subject is asked to sit in a chair facing the screen. EEG recording was performed when the subject was asked to open and close their eyes for 30 seconds each. Subsequently, subjects took a verbal memory test. This research compared the following parameters: power spectral density (PSD), full width at half max (FWHM) from PSD, and individual peak alpha frequency (IPAF) shift. P-value, standard error, mean absolute percentage error (MAPE) and mean squared error (MSE) were also obtained based on mentioned parameters. Based on the IPAF shift, it was concluded that C-EEG could read EEG signals against time well. Based on PSD and FWHM results, it was concluded that the C-EEG could not read EEG signal amplitudes well compared to the M-EEG device. The results of this research are important as a benchmark for carrying out further research using EEG, both medical and consumer-grade.

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“…Rhythmic activity comprises much ofthe resting EEG (Salansky, Fedotchev, & Bondar, 1995). This rhythmic activity is a function of the collective oscillations generated by groups of neurons firing in synchronous patterns.…”

Section: Spectral Analysis and Detection Of Concealed Informationmentioning

confidence: 99%

“…Spectral analysis is the process of taking a time epoch from the EEG waveform and breaking it down into components related to the frequency domain (Wong, 1991). Decomposition of a time epoch into spectral components is usually performed using a digital algoritlun known as the Fast Fourier Transform, or FFT (Salansky et al, 1995). Common frequency bands include alpha rhythms (8 -13 Hz) which are often associated with relaxation, beta rhythms (13 -30 Hz) that are considered to reflect active mental processing, theta (4 -8 Hz), and delta (l -4 Hz) rhythms; the latter two are associated with underarousal (Andreassi, 1989).…”

Section: Spectral Analysis and Detection Of Concealed Informationmentioning

confidence: 99%

“…Common frequency bands include alpha rhythms (8 -13 Hz) which are often associated with relaxation, beta rhythms (13 -30 Hz) that are considered to reflect active mental processing, theta (4 -8 Hz), and delta (l -4 Hz) rhythms; the latter two are associated with underarousal (Andreassi, 1989). Power spectral analysis of the EEG has been utilized in other research, including development of diagnostic criteria, pathological brain states, attention, and learning disabilities (Ackerman, Dykman, Oglesby, & Newton, 1994;Oatman, 1982;Salansky et al, 1995). Figure 1 represents a typical power spectrum plot after FFT analysis.…”

Section: Spectral Analysis and Detection Of Concealed Informationmentioning

confidence: 99%

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