Identifying robust and sensitive frequency bands for interrogating neural oscillations

Abstract

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.

Guidelines for the Recording and Evaluation of Pharmaco-EEG Data in Man: The International Pharmaco-EEG Society (IPEG)

“…These frequency bands were defined on the basis of factorial analysis of EEG records [55,56,57] and thereby constitute a very robust framework. It does not mean that other frequency ranges shall not be used for specific purposes [58]. However, to ensure that the results of a study can be compared with others published and therefore can provide useful reference material for other scientists, publications should always report the results obtained for this standard configuration (beside others if appropriate).…”

Discovering and visualizing patterns in EEG data

“…With the advent of robust time-frequency decompositions, investigating the evolution of brain activity in the frequency domain is possible [26,40]. Evidence suggesting that the spectral dynamics of brain activity is mounting [2,37]. Blanco et al introduced pioneering new methods for time-frequency analysis for EEG data [5].…”

In the blink of an eye: The contribution of microsaccadic activity to the induced gamma band response

“…In the article YuvalGreenberg et al (2008) suggest that the commonly reported high frequency, transient, scalp-recorded iGBR that peaks around 200-300 ms and covers a broad frequency range (e.g., Busch et al, 2006;Gruber et al, 2002;Tallon-Baudry et al, 1996;Zion-Golumbic and Bentin, 2007) is produced by electromyographic activity relating to microsaccades, rather than the coherent activity of neural sources. Naturally, this suggestion has triggered an ongoing debate as to exactly how much of the iGBR reported in previous studies can be attributed to these electromyogenic artefacts (Dimigen et al, 2009;Fries et al, 2008;Gruber et al, 2008b;Keren et al, 2010;Melloni et al, 2009b;Shackman et al, 2010;Tallon-Baudry, 2008).…”