Preliminary proposal for an EEG terminology by the terminology committee of the international federation for electroencephalography and clinical neurophysiology

Brazier, Mary A. B.; Cobb, W. A.; Fischgold, H; Gastaut, H; Gloor, Peter A.; Hess, RF; Jasper, Herbert H.; Loeb, C; Magnus, O; Pampiglione, G.; Rémond, A; Leeuwen, W. Storm van; Walter, W. Grey

doi:10.1016/0013-4694(61)90186-9

Cited by 59 publications

(25 citation statements)

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“…Whereas this conclusion is contrary to classical EEG taxonomies (Brazier et al, 1961; Niedermeyer, 2005; Nuwer et al, 1999), it is in line with research indicating that the theta rhythm is uncommon during quiet wakefulness in healthy adults. For instance, several very large studies have demonstrated that <1% of patients referred to clinical EEG departments over a multi-year period (Okada & Urakami, 1993; Palmer, Yarworth, & Niedermeyer, 1976; Westmoreland & Klass, 1986) and only ~8% of unselected military personnel exhibit frank midline theta rhythms at rest (Takahashi, Shinomiya, Mori, & Tachibana, 1997), although higher proportions have occasionally been reported (Bocker et al, 2009).…”

Section: Discussionsupporting

confidence: 83%

“…This convention, and those defining the other classical EEG bands, was established on the basis of expert consensus about its key distinguishing characteristics (Brazier et al, 1961). For the alpha rhythm, early work suggested that these include a mean frequency of ~10Hz ( SD = ~1Hz), peak amplitude at midline parieto-occipital electrodes, and topographically-specific suppression (i.e., desynchronization or blocking) in response to attention-demanding tasks (Niedermeyer, 2005; Shaw, 2003).…”

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confidence: 99%

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Identifying robust and sensitive frequency bands for interrogating neural oscillations

et al. 2010

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Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5Hz), alpha-low (6–9Hz), alpha-high (10–11Hz), beta (12–19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization / desynchronization) domains.

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Section: Discussionsupporting

confidence: 83%

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confidence: 99%

Identifying robust and sensitive frequency bands for interrogating neural oscillations

et al. 2010

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“…It is based on the previous proposals (Chatrian et al, 1974, Noachtar et al, 1999, Noachtar et al, 1999) and includes terms necessary to describe the EEG and to generate the EEG report. All EEG phenomena should be described as precisely as possible in terms of frequency, amplitude, phase relation, waveform, localization, quantity, and variability of these parameters (Brazier et al, 1961). The description should be independent of the recording parameters such as amplification, montages, and computer program/display.…”

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confidence: 99%

A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017

Kane¹,

Acharya

Benickzy³

et al. 2017

Clinical Neurophysiology Practice

357

305

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“…Mu oscillations, which are typical in sensory motor areas, are characterized by periodical sharp deflections rather than smooth sinusoidal oscillations (anecdotally, the name “mu” was attributed to this activity because of the similarity between the shape of the oscillatory cycle and the Greek letter μ [25], [26]). Similarly, previous reports of beta oscillations in the motor cortex (also called Rolandic beta) show a similar spiky appearance of the oscillation [27]–[29].…”

Section: Resultsmentioning

confidence: 99%

Non-Sinusoidal Activity Can Produce Cross-Frequency Coupling in Cortical Signals in the Absence of Functional Interaction between Neural Sources

et al. 2016

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The analysis of cross-frequency coupling (CFC) has become popular in studies involving intracranial and scalp EEG recordings in humans. It has been argued that some cases where CFC is mathematically present may not reflect an interaction of two distinct yet functionally coupled neural sources with different frequencies. Here we provide two empirical examples from intracranial recordings where CFC can be shown to be driven by the shape of a periodic waveform rather than by a functional interaction between distinct sources. Using simulations, we also present a generalized and realistic scenario where such coupling may arise. This scenario, which we term waveform-dependent CFC, arises when sharp waveforms (e.g., cortical potentials) occur throughout parts of the data, in particular if they occur rhythmically. Since the waveforms contain both low- and high-frequency components, these components can be inherently phase-aligned as long as the waveforms are spaced with appropriate intervals. We submit that such behavior of the data, which seems to be present in various cortical signals, cannot be interpreted as reflecting functional modulation between distinct neural sources without additional evidence. In addition, we show that even low amplitude periodic potentials that cannot be readily observed or controlled for, are sufficient for significant CFC to occur.

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Preliminary proposal for an EEG terminology by the terminology committee of the international federation for electroencephalography and clinical neurophysiology

Cited by 59 publications

References 0 publications

Identifying robust and sensitive frequency bands for interrogating neural oscillations

Identifying robust and sensitive frequency bands for interrogating neural oscillations

A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017

Non-Sinusoidal Activity Can Produce Cross-Frequency Coupling in Cortical Signals in the Absence of Functional Interaction between Neural Sources

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