Electroencephalographic Correlates of Continuous Postural Tasks of Increasing Difficulty.

Cortical involvement in postural control is well recognized, however the role of non-visual afferents remains unclear. Parietal cortical areas are strongly implicated in vestibulo-spatial functions, but topographical localization during balance tasks remains limited. Here, we use electroencephalography (EEG) during continuous balance tasks of increasing difficulty at single electrode positions. Twenty-four healthy, right-handed individuals performed four balance tasks of increasing difficulty (bipedal and unipedal) and a seated control condition with eyes closed. Subjective ratings of task difficulty were obtained. EEG was recorded from 32 electrodes; 5 overlying sensory and motor regions of interest (ROIs) were chosen for further investigation: C3, Cz, C4, P3, P4. Spectral power and coherence during balance tasks were analyzed in theta (4-8 Hz) and alpha (8-12 Hz) bands. Alpha power reduced as task difficulty increased and this reduction correlated with subjective difficulty ratings. Alpha coherence increased with task difficulty between C3-Cz-C4 electrode pairs. Differential changes in power were observed in Cz, suggestive of a distinct role at this electrode location, which captures lower limb cortical representation. Hemispheric asymmetry was observed, as reflected by greater reductions in theta and alpha power in right-sided areas. Our results demonstrate the functional importance of bilateral central and parietal cortices in continuous balance control. The hemispheric asymmetry observed implies that the non-dominant hemisphere is involved with online monitoring of postural control. Although the posterior parietal asymmetry found may relate to vestibular, somatosensory or multisensory feedback processing, we argue that the finding relates to active balance control rather than simple sensory-intake or reflex circuit activation.

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy.

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

Using recurrence quantification analysis determinism for noise removal in cardiac optical mapping.

Selecting signal processing parameters in optical imaging by utilizing the change in Determinism, a measure introduced in Recurrence Quantification Analysis, provides a novel method using the change in residual noise Determinism for improving noise quantification and removal across signals exhibiting disparate underlying tissue pathologies. The method illustrates an improved process for selecting filtering parameters and how using measured signal-to-noise ratio alone can lead to improper parameter selection.

Coherence analysis over the latent period of epileptogenesis reveal that high-frequency communication is increased across hemispheres in an animal model of limbic epilepsy.

A total of 32 microwire electrodes were implanted bilaterally into the hippocampus of Sprague-Dawley rats, which were then stimulated in the manner prescribed for the chronic limbic epilepsy model. After the initial seizure brought on by the stimulation, the animals were recorded at a high sampling rate (approximately 12 kHz) for the entire duration of the latent period. Coherence was calculated across channels in both stimulated (and later seizing) animals and non-stimulated (and thus non-seizing control) animals. Average coherence over time was greatest in intrahemispherical electrode pairs in both stimulated and non-stimulated animals. However, the 200-800 Hz band displays increased coherence interhemispherically and up to 200 Hz band displays decreased coherence interhemispherically: this occurs only in stimulated animals.

Pre-ictal entropy analysis of microwire data from an animal model of limbic epilepsy.

Epilepsy is a common neurological disorder that can have damaging effects in the brain including over 50% loss of neuronal activity in the hippocampal regions of the CA1 and CA3. The pre-ictal period was studied in an animal model of limbic epilepsy using Shannon entropy and correlation analysis. The primary aim was to uncover underlying relative changes in signals between the Dentate Gyrus and CA1 areas of the bilateral hippocampus. Preliminary entropy analysis results included dynamical changes between channels in the Dentate Gyrus and channels in the CA1 region at and around the time of the seizure.

Detection of high frequency oscillations with Teager energy in an animal model of limbic epilepsy.

High frequency oscillations (HFO) in limbic epilepsy represent a marked difference between abnormal and normal brain activity. Faced with the difficult of visually detecting HFOs in large amounts of intracranial EEG data, it is necessary to develop an automated process. This paper presents Teager Energy as a method of finding HFOs. Teager energy is an ideal measure because unlike conventional energy it takes into account the frequency component of the signal as well as signal amplitude. This greatly aids in the dissection of HFOs out of the noise and other signals contained in the EEG. Therein, Teager energy analysis is able to detect high-frequency, low-amplitude components that conventional energy measurements would miss.