EEG fitting: a new method for numerical analysis of EEG

A Marked Point Process Framework for Extracellular Electrical Potentials

Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP). This spatially temporal organized multi-frequency transient (phasic) activity reflects the multiscale spatiotemporal synchronization of neuronal populations in response to external stimuli or internal physiological processes. We propose a novel generative statistical model of a single EEP channel, where the collected signal is regarded as the noisy addition of reoccurring, multi-frequency phasic events over time. One of the main advantages of the proposed framework is the exceptional temporal resolution in the time location of the EEP phasic events, e.g., up to the sampling period utilized in the data collection. Therefore, this allows for the first time a description of neuromodulation in EEPs as a Marked Point Process (MPP), represented by their amplitude, center frequency, duration, and time of occurrence. The generative model for the multi-frequency phasic events exploits sparseness and involves a shift-invariant implementation of the clustering technique known as k-means. The cost function incorporates a robust estimation component based on correntropy to mitigate the outliers caused by the inherent noise in the EEP. Lastly, the background EEP activity is explicitly modeled as the non-sparse component of the collected signal to further improve the delineation of the multi-frequency phasic events in time. The framework is validated using two publicly available datasets: the DREAMS sleep spindles database and one of the Brain-Computer Interface (BCI) competition datasets. The results achieve benchmark performance and provide novel quantitative descriptions based on power, event rates and timing in order to assess behavioral correlates beyond the classical power spectrum-based analysis. This opens the possibility for a unifying point process framework of multiscale brain activity where simultaneous recordings of EEP and the underlying single neuron spike activity can be integrated and regarded as marked and simple point processes, respectively.

A method for estimating long-range power law correlations from the electroencephalogram

Recent research has found long-range electroencephalogram (EEG) power law correlations, indicating time scale invariance. However, the EEG is also rather noisy, displaying short-term decorrelation like white noise--i.e., what is scale invariant at one time period may disappear in the next. The paradoxical combination of short-range divergence, but long-range correlations, suggests that any long-range correlations detected in one sample may be spurious, since they could be related to amplitude fluctuations. To overcome this problem, this paper suggests a new technique for analysing EEG signals segmented by zero-crossings, using detrended fluctuation analysis (DFA), evaluated across two time periods (TIME) and different sites (SITE). A mean scaling exponent across all subjects and sites of alpha = 0.67 was observed. MANOVA analysis indicates no significant main effect for TIME or interaction with SITE, suggesting that the zero-crossing method may be successful in determining the fractal nature of EEG dynamics across relatively long time scales.

Orthogonal polynomial regression for the detection of response variability in event-related fMRI

Previous studies using event-related potentials (ERPs) have identified a variety of brain regions that respond to rare nontarget distractor stimuli presented in the visual oddball task. By contrast, event-related functional magnetic resonance imaging (ER-fMRI) studies using similar stimuli have found little or no response in these same regions, suggesting that ER-fMRI may be less sensitive than ERPs for detecting stimulus-evoked activity. It was hypothesized here that variations in ER-fMRI response amplitude evoked by successive stimuli may have reduced detection sensitivity in these previous studies. Multiple regression with orthogonal polynomials (OPR) was used to increase detection sensitivity by employing orthogonal polynomial equations to model such variations in response amplitude. ER-fMRI data was collected from 17 subjects during performance of the visual oddball task, which included frequent nontarget, rare nontarget distractor, and rare target block letter stimuli, to which subjects made a speeded button press response. When compared with multiple regression using main effect regressors alone, OPR identified an increased volume of significant target and distractor evoked main effect response due to reduced error variance. In addition, distractor-evoked activity was found to correlate with orthogonal polynomial regressors but not main effect regressors across a large volume of prefrontal and paralimbic brain regions including dorsal-lateral prefrontal, inferior-lateral prefrontal, and cingulate cortex. These results illustrate that considerable variability is present in ER-fMRI responses evoked by rare distractor stimuli in the oddball task. Such variability can be modeled using OPR, leading to increased detection sensitivity and better anatomical correspondence between the findings of ER-fMRI and ERP studies.

The EEG of the sleep onset period in insomnia: a discriminant analysis

A period of rapid change in the wave components of the electroencephalogram (EEG) marks the transition from wake to sleep. Twenty-six insomniac and 28 control nights were studied in a discriminant analysis to determine whether this transitional state is modified in any way in subjects diagnosed for psychophysiological insomnia. A discriminant function was derived based on 20 insomniac and 22 normal nights. All 42 nights were correctly classified by this function. The sleep onset period, extending on the average over about 3 minutes, was characterized essentially by the beta and delta components of the EEG signal and by an activity index given by the ratio beta/delta, measured at the temporal lobe sites. Other variables included the subject's age and the magnitude of the changes occurring in the difference between activities in the right and left hemispheres. The variables contributing most to the discrimination were the activity index and beta, especially at the transitions from wake to stage 1 and from stage 1 to stage 2. The contribution of delta to the discrimination was less, but extended further in time to include stage 2 sleep. A test on the remaining six insomniac and six control nights gave a 75% classification accuracy, thus validating the derived discriminant function.

Digital period analysis of sleep EEG in depression

The period-analyzed sleep electroencephalogram (EEG) was compared in a group of 9 depressed outpatients and 9 age-matched normal controls. Both groups showed rhythms in beta, delta, and theta activity with an approximately 90-min period. The phase and coherence between fast and slow frequency EEG measures, however, differed significantly in the two groups. Beta and delta rhythms were less coherent in the depressed outpatient sample. The control group showed higher coherence and a strong coupling of beta and delta activity. These preliminary data suggest that depression may be associated with some degree of ultradian rhythm disturbances though periodicity is unaffected.