BACKGROUND: Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes.
METHODS: We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG).
RESULTS: Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach.
CONCLUSIONS: Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.

UNASSIGNED: An imbalance of excitatory and inhibitory synaptic transmission in multiple sclerosis (MS) may lead to cognitive impairment, such as impaired working memory. The 1/f slope of electroencephalography/magnetoencephalography (EEG/MEG) power spectra is shown to be a non-invasive proxy of excitation/inhibition balance. A flatter slope is associated with higher excitation/lower inhibition.
UNASSIGNED: To assess the 1/f slope modulation induced by stimulus and its association with behavioral and cognitive measures.
UNASSIGNED: We analyzed MEG recordings of 38 healthy controls (HCs) and 79 people with multiple sclerosis (pwMS) while performing an n-back task including target and distractor stimuli. Target trials require an answer, while distractor trials do not. We computed the 1/f spectral slope through the fitting oscillations and one over f (FOOOF) algorithm within the time windows 1 second before and after each stimulus presentation.
UNASSIGNED: We observed a flatter 1/f slope after distractor stimuli in pwMS compared to HCs. The 1/f slope was significantly steeper after stimulus for both HCs and pwMS and was significantly correlated with reaction times. This modulation in 1/f slope was significantly correlated with visuospatial memory assessed by the BVMT-R test.
UNASSIGNED: Our results suggest possible inhibitory mechanism deficits in pwMS during a working memory task.

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.