scholarly journals Audiovisual incongruence differentially impacts left and right hemisphere sensorimotor oscillations: Potential applications to production

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258335
Author(s):  
David Jenson
Keyword(s):  
Time Course ◽  
Right Hemisphere ◽  
Time Frequency ◽  
Multimodal Signals ◽  
Eeg Data ◽  
Sensorimotor Activity ◽  
Potential Applications ◽  
Beta Frequency ◽  
Resolution Processes ◽  
Level Of Analysis

Speech production gives rise to distinct auditory and somatosensory feedback signals which are dynamically integrated to enable online monitoring and error correction, though it remains unclear how the sensorimotor system supports the integration of these multimodal signals. Capitalizing on the parity of sensorimotor processes supporting perception and production, the current study employed the McGurk paradigm to induce multimodal sensory congruence/incongruence. EEG data from a cohort of 39 typical speakers were decomposed with independent component analysis to identify bilateral mu rhythms; indices of sensorimotor activity. Subsequent time-frequency analyses revealed bilateral patterns of event related desynchronization (ERD) across alpha and beta frequency ranges over the time course of perceptual events. Right mu activity was characterized by reduced ERD during all cases of audiovisual incongruence, while left mu activity was attenuated and protracted in McGurk trials eliciting sensory fusion. Results were interpreted to suggest distinct hemispheric contributions, with right hemisphere mu activity supporting a coarse incongruence detection process and left hemisphere mu activity reflecting a more granular level of analysis including phonological identification and incongruence resolution. Findings are also considered in regard to incongruence detection and resolution processes during production.

scholarly journals Spectral EEG Features of a Short Psycho-physiological Relaxation

2014 ◽  
Vol 14 (4) ◽  
pp. 237-242 ◽  
Author(s):  
Michal Teplan ◽  
Anna Krakovská ◽  
Marián Špajdel
Keyword(s):  
Time Course ◽  
Total Power ◽  
Frequency Range ◽  
Eyes Closed ◽  
Beta Power ◽  
Eeg Data ◽  
Physiological Relaxation ◽  
Potential Applications ◽  
Minute Duration ◽  
Eeg Features

Abstract Short-lasting psycho-physiological relaxation was investigated through an analysis of its bipolar electroencephalographic (EEG) characteristics. In 8 subjects, 6-channel EEG data of 3-minute duration were recorded during 88 relaxation sessions. Time course of spectral EEG features was examined. Alpha powers were decreasing during resting conditions of 3-minute sessions in lying position with eyes closed. This was followed by a decrease of total power in centro-parietal cortex regions and an increase of beta power in fronto-central areas. Represented by EEG coherences the interhemispheric communication between the parieto-occipital regions was enhanced within a frequency range of 2-10 Hz. In order to discern between higher and lower levels of relaxation distinguished according to self-rated satisfaction, EEG features were assessed and discriminating parameters were identified. Successful relaxation was determined mainly by the presence of decreased delta-1 power across the cortex. Potential applications for these findings include the clinical, pharmacological, and stress management fields.

scholarly journals Exclusion of the Possibility of “False Ripples” From Ripple Band High-Frequency Oscillations Recorded From Scalp Electroencephalogram in Children With Epilepsy

2021 ◽  
Vol 15 ◽  
Author(s):  
Katsuhiro Kobayashi ◽  
Takashi Shibata ◽  
Hiroki Tsuchiya ◽  
Tomoyuki Akiyama
Keyword(s):  
High Frequency ◽  
Numerical Differentiation ◽  
Convincing Evidence ◽  
Exact Test ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Eeg Data ◽  
Group A ◽  
Spurious Oscillations ◽  
Scalp Electroencephalogram

AimRipple-band epileptic high-frequency oscillations (HFOs) can be recorded by scalp electroencephalography (EEG), and tend to be associated with epileptic spikes. However, there is a concern that the filtration of steep waveforms such as spikes may cause spurious oscillations or “false ripples.” We excluded such possibility from at least some ripples by EEG differentiation, which, in theory, enhances high-frequency signals and does not generate spurious oscillations or ringing.MethodsThe subjects were 50 pediatric patients, and ten consecutive spikes during sleep were selected for each patient. Five hundred spike data segments were initially reviewed by two experienced electroencephalographers using consensus to identify the presence or absence of ripples in the ordinary filtered EEG and an associated spectral blob in time-frequency analysis (Session A). These EEG data were subjected to numerical differentiation (the second derivative was denoted as EEG″). The EEG″ trace of each spike data segment was shown to two other electroencephalographers who judged independently whether there were clear ripple oscillations or uncertain ripple oscillations or an absence of oscillations (Session B).ResultsIn Session A, ripples were identified in 57 spike data segments (Group A-R), but not in the other 443 data segments (Group A-N). In Session B, both reviewers identified clear ripples (strict criterion) in 11 spike data segments, all of which were in Group A-R (p < 0.0001 by Fisher’s exact test). When the extended criterion that included clear and/or uncertain ripples was used in Session B, both reviewers identified 25 spike data segments that fulfilled the criterion: 24 of these were in Group A-R (p < 0.0001).DiscussionWe have demonstrated that real ripples over scalp spikes exist in a certain proportion of patients. Ripples that were visualized consistently using both ordinary filters and the EEG″ method should be true, but failure to clarify ripples using the EEG″ method does not mean that true ripples are absent.ConclusionThe numerical differentiation of EEG data provides convincing evidence that HFOs were detected in terms of the presence of such unusually fast oscillations over the scalp and the importance of this electrophysiological phenomenon.

Eigenspace Time Frequency Based Features for Accurate Seizure Detection from EEG Data

IRBM ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 122-132 ◽  
Author(s):  
M. Deriche ◽  
S. Arafat ◽  
S. Al-Insaif ◽  
M. Siddiqui
Keyword(s):  
Seizure Detection ◽  
Time Frequency ◽  
Eeg Data

Tracking markers of response inhibition in electroencephalographic data: why should we and how can we go beyond the N2 component?

2015 ◽  
Vol 26 (4) ◽  
Author(s):  
Marion Albares ◽  
Guillaume Lio ◽  
Philippe Boulinguez
Keyword(s):  
Response Inhibition ◽  
Executive Control ◽  
Time Course ◽  
Event Related Potential ◽  
Time Frequency ◽  
Inhibitory Mechanisms ◽  
Prepotent Response ◽  
Combined Use ◽  
Brain Behavior ◽  
Processing Steps

AbstractResponse inhibition is a pivotal component of executive control, which is especially difficult to assess. Indeed, it is a substantial challenge to gauge brain-behavior relationships because this function is precisely intended to suppress overt measurable behaviors. A further complication is that no single neuroimaging method has been found that can disentangle the accurate time-course of concurrent excitatory and inhibitory mechanisms. Here, we argue that this objective can be achieved with electroencephalography (EEG) on some conditions. Based on a systematic review, we emphasize that the standard event-related potential N2 (N200) is not an appropriate marker of prepotent response inhibition. We provide guidelines for assessing the cortical brain dynamics of response inhibition with EEG. This includes the combined use of inseparable data processing steps (source separation, source localization, and single-trial and time-frequency analyses) as well as the amendment of the classical experimental designs to enable the recording of different kinds of electrophysiological activity predicted by different models of response inhibition. We conclude with an illustration based on recent findings of how fruitful this approach can be.

Toward a Computational Model of Constraint-Driven Exploration and Haptic Object Identification

Perception ◽  
1993 ◽  
Vol 22 (5) ◽  
pp. 597-621 ◽  
Author(s):  
Roberta L Klatzky ◽  
Susan J Lederman
Keyword(s):  
Time Course ◽  
Incidental Learning ◽  
Object Identification ◽  
Architectural Elements ◽  
Haptic Exploration ◽  
Object Properties ◽  
Potential Applications ◽  
Haptic Object ◽  
Exploratory Procedures ◽  
Selection Of

A conceptual model of the human haptic system in relation to object identification is presented. The model encompasses major architectural elements including representations of haptically accessible object properties and exploratory procedures (EPs)—dedicated movement patterns that are specialized to extract particular properties. These architectural units are related in processing-specific ways. Properties are associated with exploratory procedures in keeping with the extent to which a given procedure delivers information about a given property. The EPs are associated with one another in keeping with their compatibility, as determined by parameters of motor execution and interactions with the object and the workspace. The resulting architecture is treated as a system of constraints which guide the exploration of an object during the course of identification. The selection of the next step in a sequence of exploration requires that constraints be optimally satisfied. A network approach to constraint satisfaction is implemented and shown to account for a number of previous empirical results concerning the time course of exploration, object classification speed, and incidental learning about object properties. This system has potential applications for robotic haptic exploration.

scholarly journals Indexing the graded allocation of visuospatial attention using anticipatory alpha oscillations

2011 ◽  
Vol 105 (3) ◽  
pp. 1318-1326 ◽  
Author(s):  
Ian C. Gould ◽  
Matthew F. Rushworth ◽  
Anna C. Nobre
Keyword(s):  
Reaction Times ◽  
Neural Mechanism ◽  
Visuospatial Attention ◽  
Event Related Potential ◽  
Response Preparation ◽  
Beta Band ◽  
Top Down ◽  
Alpha Oscillations ◽  
Time Frequency ◽  
Eeg Data

Lateralization in the desynchronization of anticipatory occipitoparietal alpha (8–12 Hz) oscillations has been implicated in the allocation of selective visuospatial attention. Previous studies have demonstrated that small changes in the lateralization of alpha-band activity are predictive of behavioral performance but have not directly investigated how flexibly alpha lateralization is linked to top-down attentional goals. To address this question, we presented participants with cues providing varying degrees of spatial certainty about the location at which a target would appear. Time-frequency analysis of EEG data demonstrated that manipulating spatial certainty led to graded changes in the extent to which alpha oscillations were lateralized over the occipitoparietal cortex during the cue-target interval. We found that individual differences in alpha desynchronization contralateral to attention predicted reaction times, event-related potential measures of perceptual processing of targets, and beta-band (15–25 Hz) activity typically associated with response preparation. These results support the hypothesis that anticipatory alpha modulation is a plausible neural mechanism underlying the allocation of visuospatial attention and is under flexible top-down control.

scholarly journals Neuronal Dynamics of Pain in Parkinson’s Disease

2021 ◽  
Vol 11 (9) ◽  
pp. 1224
Author(s):  
Kaoru Kinugawa ◽  
Tomoo Mano ◽  
Kazuma Sugie
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Symptom ◽  
Neuronal Dynamics ◽  
Whole Brain ◽  
Neuronal Synchronization ◽  
Time Frequency ◽  
Eeg Data ◽  
Significant Difference

Pain is an important non-motor symptom of Parkinson’s disease (PD). It negatively impacts the quality of life. However, the pathophysiological mechanisms underlying pain in PD remain to be elucidated. This study sought to use electroencephalographic (EEG) coherence analysis to compare neuronal synchronization in neuronal networks between patients with PD, with and without pain. Twenty-four patients with sporadic PD were evaluated for the presence of pain. Time-frequency and coherence analyses were performed on their EEG data. Whole-brain and regional coherence were calculated and compared between pain-positive and pain-negative patients. There was no significant difference in the whole-brain coherence between the pain-positive and pain-negative groups. However, temporal–temporal coherence differed significantly between the two groups (p = 0.031). Our findings indicate that aberrant synchronization of inter-temporal regions is involved in PD-related pain. This will further our understanding of the mechanisms underlying pain in PD.

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