Abstract
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.
Highlights
Extracellular electrical potentials (EEP) from the brain can be recorded at distinct levels—from cm2-scale non-invasive electroencephalographic signals from the scalp (EEG) to invasive mm2-scale cortical activity recorded by subdural grid electrodes (ECoG) or even at localized deeper anatomical brain regions by inserting electrodes or silicon probes into the brain (LFP) (Buzsáki et al, 2012)
These phasic events have been well documented in the literature under the concepts of induced potentials and event-related oscillations (Tallon-Baudry and Bertrand, 1999; Freeman and Quiroga, 2012), e.g., gamma oscillations in the olfactory bulb of cats and rabbits after odor presentation (Freeman, 1975), characteristic sleep-stage-related patterns in humans (Rechtschaffen et al, 1968) and sharpwave ripples in the hippocampus associated to cognition and memory processes (Buzsáki, 2015)
As suggested by Freeman (Freeman and Quiroga, 2012), our approach calls for higher-order statistical moments, instead of being applied to individual time samples, we propose its application to snippets of embedding vectors of size M (M-snippet), where M, in samples, is the selected filter length
Summary
Extracellular electrical potentials (EEP) from the brain can be recorded at distinct levels—from cm2-scale non-invasive electroencephalographic signals from the scalp (EEG) to invasive mm2-scale cortical activity recorded by subdural grid electrodes (ECoG) or even at localized deeper anatomical brain regions by inserting electrodes or silicon probes into the brain (LFP) (Buzsáki et al, 2012) This type of activity reflects the average spatiotemporal interaction of neuronal assemblies and, constitute a coarser scale (mesoscopic) measure beyond singlecell recordings (action potentials or spikes) that can play a complementary role to relate multiscale brain activity to more overt types of cognitive phenomena and psychological constructs, such as behavior, perception and learning. Another well-known type of organized activity, event-related oscillations are characterized by latency and onset variability, which demand for fine temporal resolution and special attention when it comes to processing and further interpretation
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