A Marked Point Process Framework for Extracellular Electrical Potentials

@article{Loza2017AMP,
  title={A Marked Point Process Framework for Extracellular Electrical Potentials},
  author={Carlos A. Loza and Michael Okun and Jos{\'e} Carlos Pr{\'i}ncipe},
  journal={Frontiers in Systems Neuroscience},
  year={2017},
  volume={11}
}
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… 

Figures and Tables from this paper

Local power estimation of neuromodulations using point process modeling
TLDR
A novel approach to estimate local power more precisely at a resolution as high as the sampling frequency is presented, establishing the aptness of MPP spectrogram as a finer measure of power where it is able to track local variations in power while preserving the global structure of signal power distribution.
Sparse Wave Packets Discriminate Motor Tasks in EEG–based BCIs
TLDR
A novel non–linear source separation technique for single–channel, multi–trial Electroencephalogram (EEG), which suggests a transient, temporally sparse feature of the neuromodulations that can be further exploited in applications where compression is advantageous.
Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs
TLDR
A generative model for single-channel ECoGs that is able to fully characterize reoccurring rhythm–specific neuromodulations as weighted activations of prototypical templates over time and exploits principles of Minimum Description Length (MDL) encoding to effectively yield a data-driven framework where prototypical neurommodulations can be estimated alongside the timings and features of the TMPP.
Marked point process representation of oscillatory dynamics underlying working memory
TLDR
This paper presents a marked point process (MPP) representation of bursts during working memory, a complex cognitive process involved in encoding, storing and retrieving sensory information, which has been shown to be characterized by oscillatory bursts in the beta and gamma band.
The Embedding Transform. A Novel Analysis of Non-Stationarity in the EEG
  • Carlos A. Loza, J. Príncipe
  • Computer Science
    2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2018
TLDR
A novel technique to analyze nonstationarity in single-channel Electroencephalogram (EEG) traces: the Embedding Transform, based on Walter J. Freeman's studies concerning active and rest stages and deviations from Gaussianity is introduced.
Quantitative Analysis of a Marked Point Process based Sleep Spindle Detector (MPP-SSD)
  • Shailaja Akella, J. Príncipe
  • Computer Science
    2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2018
TLDR
A transient model for automatic sleep spindle detection designed as a Marked Point Process is employed and it is concluded that the design of the bandpass filters affects the performance.
The Generalized Sleep Spindles Detector: A Generative Model Approach on Single-Channel EEGs
TLDR
A data-driven, unsupervised learning framework for one of the hallmarks of stage 2 sleep in the electroencephalogram (EEG)—sleep spindles and its learned representations provide a rich parameter space for further applications such as sparse encoding, inference, detection, diagnosis, and modeling.
Robust Estimation of Shift–Invariant Patterns Exploiting Correntropy
We propose a novel framework for robust estimation of recurring patterns in time series. Particularly, we utilize correntropy and a shift–invariant adaptation of sparse modeling techniques as the
EEG Models and Analysis

References

SHOWING 1-10 OF 106 REFERENCES
Transient Model for Neuronal Oscillations
TLDR
A transient model is proposed that poses a single EEG trace as the result of the noisy addition of reoccurring, transient events over time and frequency bands to preserve the superior temporal resolution of EEG while incorporating additional features to the model, such as amplitude, frequency, duration, and modulation-based measures related to relevant phasic events.
Fine temporal resolution of analytic phase reveals episodic synchronization by state transitions in gamma EEGs.
TLDR
The results provide support for the hypothesis that neurons in mesoscopic neighborhoods in sensory cortices self-organize their activity by synaptic interactions into wave packets that have spatial patterns of amplitude and phase modulation of their spatially coherent carrier waves in the gamma range and that form and dissolve aperiodically at rates in and below the theta range.
Unsupervised robust detection of behavioral correlates in ECoG
TLDR
This work proposes an automatic, fully data-driven method to extract relevant neuromodulation events from single-channel, single-trial traces and finds distinct behavioral correlates in the low-gamma band that encode finger flexion movements in a cued task.
The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes
TLDR
High-density recordings of field activity in animals and subdural grid recordings in humans can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase the understanding of how these processes contribute to the extracellular signal.
Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis
TLDR
The method allows, for the first time, to define a theoretical framework in terms of an order and control parameter derived from fMRI data, where the dynamical regime can be interpreted as one corresponding to a system close to the critical point of a second order phase transition.
Learning Recurrent Waveforms Within EEGs
TLDR
An algorithm to automatically learn the time-limited waveforms associated with phasic events that repeatedly appear throughout an electroencephalogram, which could then be used as features for automatic evaluation and comparison of EEG during sleep, pathology, or mentally engaging tasks.
Aperiodic phase re‐setting in scalp EEG of beta–gamma oscillations by state transitions at alpha–theta rates
TLDR
An optimal pass band to detect and measure recurring jumps in AP in the β and γ ranges was found by maximizing the α peak in the cospectrum of the correlation between unfiltered EEG and the band pass AP differences.
The labile brain. I. Neuronal transients and nonlinear coupling.
  • Karl J. Friston
  • Biology
    Philosophical transactions of the Royal Society of London. Series B, Biological sciences
  • 2000
TLDR
The nature of, and motivation for, neuronal transients is described in relation to characterizing brain dynamics, and it is shown that nonlinear (asynchronous) coupling is, in fact, more abundant and can be more significant than synchronous coupling.
Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.
TLDR
It is shown in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes.
Analysis of EEG transients by means of matching pursuit
TLDR
The MP technique makes following the temporal evolution of transients and their propagation in brains possible, and opens up new possibilities in EEG research providing a means of investigation of dynamic processes in brains in a much finer time-frequency scale than any other method available at present.
...
...