bioRxiv Science⌕ Search

bioRxiv · 10.1101/173518

Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals

Abstract

Epilepsy is a neurological condition which affects the nervous system. It is a general term used for a group of disorders in which nerve cells of the brain discharge anomalous electrical impulses from time to time, causing a temporary malfunction of the other nerve cells of the brain. EEG signal provides an important cue for diagnosis and interpretation related to prognosis of epilepsy. In this work we envisage to provide novel tool which can be used to detect the prognosis of epileptic disorder by comparing linear and nonlinear modalities of EEG analysis conventionally used Power spectral analysis and a robust non linear method, Detrended Fluctuation Analysis (DFA). Publicly available dataset is used for this work consisting of 100 normal patients EEG data as control group and 100 epileptic patients EEG data for comparison. Response for different frequency bands (alpha, theta, beta) of the EEG spectrum have been analyzed using Detrended Fluctuation Analysis (DFA) and Power Spectral Intensity (PSI). The comparison of the DFA scaling exponent with the spectral power data is calculated for all the 3 different frequency bands of EEG signal provide new and interesting results which have been discussed in detail.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ghosh, B., Sengupta, S., Nag, S., Biswas, S., Sanyal, S.. 2017-08-08. Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals. https://doi.org/10.1101/173518

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Human iEEG recordings during audiovisual speech violations support and extend predictive routing models

Rather than constantly reconstructing the world solely through sensory input, predictive processing theories posit that the brain utilizes predictions maintained as internal representations to guide sensory processing. An emerging model, predictive routing, proposes that an interplay between neuronal gamma (40-150 Hz) and alpha-beta (10-30 Hz) rhythms implements a dynamic mechanism for predictive processing. Namely, predictions enhance alpha-beta oscillations that suppress gamma activity during the processing of predicted inputs. While this model is supported by animal studies, its validity has not been fully explored within the human brain. The present study addresses this by leveraging human intracranial EEG (iEEG) recordings across superior temporal and prefrontal/premotor areas of seven presurgical epilepsy patients during an audiovisual speech paradigm. We calculated iEEG power in the theta, alpha-beta, and high gamma bands during simple auditory deviance, multisensory integration, and a condition where an incongruent visual cue (lip movements) clashed with an auditory stimulus (a syllable), creating audiovisual incongruence that can elicit the McGurk illusion. Results revealed increased high-gamma and decreased alpha-beta power in the superior temporal cortex in response to auditory deviance and the McGurk condition, while frontal areas, despite reflecting a similar decrease in alpha-beta power, did not present this high gamma prediction error signal. Instead, the theta band revealed a strong prediction error signal in the McGurk condition, consistent with theories on the role of theta in reorganizing cortical networks. These results both support and extend existing predictive routing models for human-specific forms of predictive processing.

neuroscience↗

A Unified World Model in Human Posterior Parietal Cortex

How does the brain understand the physical world, predict future events, and plan actions? Are these problems solved by a unified world model, as hypothesized by cognitive scientists and artificial intelligence researchers? Posterior parietal cortex has been implicated in all these functions, but whether the same neurons support each of them is unknown. We recorded from the superior parietal lobule (SPL) of two tetraplegic participants implanted with microelectrode arrays. SPL units preferentially engaged by intuitive physics, judging block-tower stability and viewing naturalistic physical events, also predicted whether moving objects would collide hundreds of milliseconds before contact, generalizing between different physical scenarios. These populations also encoded which hand action the participant intended to perform. These findings identify SPL as a substrate for physical world modeling.

neuroscience↗

Cortical long-range dynamics track excitation-inhibition balance across cognitive states and organizational principles

A fundamental question in systems neuroscience is how the brain's excitation-inhibition (E/I) balance shapes the temporal structure of neural activity and whether we can non-invasively recover this relationship from neural scaling properties. We show that MRS-derived E/I balance is expressed in long-range temporal dynamics and constrained by cytoarchitecture. The laminar organisation of inhibitory circuits, spanning agranular to granular cortex, determines how faithfully regional dynamics reflect E/I balance. This neurochemical signature is state-dependent: its relationship with cortical dynamics reverses during task engagement, consistent with traversal of dynamical regimes around a critical point. It extends beyond fMRI to arousal-linked dynamics, indexed by pupil diameter and microsaccades, indicating a global mode of cortical state rather than a regionally isolated property. The imprint of E/I balance on long-range dynamics follows the sensory-association axis at rest, and is reconfigured during task engagement. These findings establish E/I balance as a neurochemical quantity with structured, scalable signatures across the temporal and spatial organization of the human cortex.

neuroscience↗