Further, 57.8% of the total variance in EEG power was subject-specific and acknowledged by the LMM framework and its prediction. Tinnitus distress targeted the frequency bands beta3 (23.5-35 Hz) and gamma (35.5-45 Hz) in right frontal regions, whereas delta (0.5-4 Hz) exhibited significant changes in temporal-parietal sources. Results from 49 participants (38 male, mean age of 46.69 ± 12.65 years) revealed that EEG signals were not only associated with specific recording sites, but exhibited regional specific oscillations in conjunction to symptom severity.
This report provides an example of how LMM can be used for the statistical analysis of resting-state EEG data in a heterogeneous group of subjects namely, people who suffer from tinnitus (ringing in the ear/s). Using LMM, individual differences can be modelled through the assumptions of idiosyncrasy of all responses and dependency among data points (e.g., from the same subject within and across units of time) via random effects parameters. In order to encompass the potential individual source of variance in EEG data and psychometric parameters, we introduce an innovative application of linear mixed-effects models (LMM) as an alternative procedure for the analysis of resting-state EEG data. Despite some inter-individual similarities, there is reason to believe that a discernible portion of the individual brain activity is subject-specific. Samples can also contain optional other parameters added by the addInfo operator. Samples have a data array containing a single reading from a number of different EEG electrodes along with a single timestamp.
In neuroscience, neural oscillations and other features of brain activity recorded by electroencephalography (EEG) are typically statistically assessed on the basis of the study's population mean to identify possible blueprints for healthy subjects, or subjects with diagnosable neurological or psychiatric disorders. This is the simplest, core data structure for individual samples of EEG data.
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