Wavelet denoising of EEG signals and identification of evoked response potentials

1994 ◽  
Author(s):  
Rene A. Carmona ◽  
Lonnie H. Hudgins
Keyword(s):  
Wavelet Denoising ◽  
Evoked Response ◽  
Eeg Signals ◽  
Evoked Response Potentials

scholarly journals A template-matching algorithm for laminar identification of cortical recording sites from evoked response potentials

2019 ◽  
Author(s):  
Giulio Matteucci ◽  
Margherita Riggi ◽  
Davide Zoccolan
Keyword(s):  
Template Matching ◽  
Current Source ◽  
Evoked Response ◽  
Spatiotemporal Pattern ◽  
Manual Annotation ◽  
Subjective Judgment ◽  
Visual Identification ◽  
Alternative Approach ◽  
Human Operators ◽  
Evoked Response Potentials

AbstractIn recent years, the advent of the so-called silicon probes has made it possible to homogeneously sample spikes and local field potentials (LFPs) from a regular grid of cortical recording sites. In principle, this allows inferring the laminar location of the sites based on the spatiotemporal pattern of LFPs recorded along the probe, as in the well-known current source-density (CSD) analysis. This approach, however, has several limitations, since it relies on visual identification of landmark features (i.e., current sinks and sources) by human operators – features that can be absent from the CSD pattern if the probe does not span the whole cortical thickness, thus making manual labelling harder. Furthermore, as any manual annotation procedure, the typical CSD-based workflow for laminar identification of recording sites is affected by subjective judgment undermining the consistency and reproducibility of results. To overcome these limitations, we developed an alternative approach, based on finding the optimal match between the LFPs recorded along a probe in a given experiment and a template LFP profile that was computed using 18 recording sessions, in which the depth of the recording sites had been recovered through histology. We show that this method can achieve an accuracy of 79 µm in recovering the cortical depth of recording sites and a 76% accuracy in inferring their laminar location. As such, our approach provides an alternative to CSD that, being fully automated, is less prone to the idiosyncrasies of subjective judgment and works reliably also for recordings spanning a limited cortical stretch.New and noteworthyKnowing the depth and laminar location of the microelectrodes used to record neuronal activity from the cerebral cortex is crucial to properly interpret the recorded patterns of neuronal responses. Here we present an innovative approach that allows inferring such properties with high accuracy and in an automated way (i.e., without the need of visual inspection and manual annotation) from the evoked response potentials (ERPs) elicited by sensory (e.g., visual) stimuli.

P300 Evoked Response Potentials Patterns in Different Complex Concussion Phenotypes

Neurology ◽  
2021 ◽  
Vol 98 (1 Supplement 1) ◽  
pp. S4.2-S5
Author(s):  
Basil Ike ◽  
Landon Watts ◽  
David S. Oakley ◽  
Monica Pita ◽  
Mohammad Mortazavi
Keyword(s):  
Retrospective Study ◽  
Neck Pain ◽  
Mixed Type ◽  
Statistical Significance ◽  
Evoked Response ◽  
Entire Group ◽  
Post Injury ◽  
Neurologic Disorders ◽  
Symptom Scores ◽  
Evoked Response Potentials

ObjectiveDetermine the utility of P300 Evoked Response Potentials (ERP) voltage patterns in predicting phenotypical sequelae of patients with complex concussions or Persistent Post Concussive Symptoms (PPCS).BackgroundERPs have been used to aid in the diagnosis of multiple neurologic disorders. They have also been recently used in the evaluation of concussions.Design/MethodsA retrospective study of 54 patients, 10–71 year (mean age 29.6 yrs), with PPCS were tested between 6 and 12 weeks post-injury using the standard oddball audio P300 ERP protocol with measures extracted including best central parietal P300 ERP. PPCS Phenotyping was completed in each patient using a standardized post-concussive questionnaire and Rivermead method for 5 primary phenotypes and mixed type.ResultsP300 average Voltage for the entire group was 11.6 mV. Overall, these were significantly lower than age-matched non concussed controls whose average voltage was 16.3 mV (p < 0.0001). Average P300 voltages for each phenotype: Cognition- 14.1 mV, Vestibular- 8.6 mV, Headache- 11.1 mV, Mood- 13.6 mV, Neck Pain- 9.6 mV, Visual- 9.8 mV, Mixed- 6.9 mV, Mixed and Vestibular phenotypes demonstrated the lowest average voltage potentials (6.9 mV and 8.6 mV respectively) which coincided with higher average symptom scores (70.5 and 54.5 respectively). Cognition and Mood demonstrated the highest average voltage potentials (14.1 mV and 13.6 nV respectively), which coincided with lower average symptom scores (40.3 and 48.7, respectively). Mood (13.6 mV) was the lowest average symptom score in the group at 40.3 and Mixed (6.9 mV) was highest at 70.5. Comparing phenotypes against one other, mixed vs mood (p = 0.03), cognition vs vestibular (p = 0.02), and cognition vs mixed (p = 0.009) showed statistical significance.ConclusionsP300 ERPs may help identify persistent abnormal complex concussion neurophysiology. ERPs can also potentially exhibit phenotype specific patterns and be a useful tool in helping differentiate more somatic/physiologic vs mood-based phenotypes. This can ultimately lead in the aid in diagnosis, prognosis, subtyping, and targeted phenotype management.

Evoked Response Potentials and Regional Cerebral Blood Flow in Somatization Disorder

1987 ◽  
Vol 47 (3-4) ◽  
pp. 190-196 ◽  
Author(s):  
Leigh James ◽  
Andrew Singer ◽  
Yvonne Zurynski ◽  
Evian Gordon ◽  
Claudia Kraiuhin ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Regional Cerebral Blood Flow ◽  
Evoked Response ◽  
Somatization Disorder ◽  
Regional Cerebral Blood ◽  
Evoked Response Potentials

Discussion of Approach for Extracting Pure EOG Reference Signal from EEG Mixture Based on Wavelet Denoising Technique

2015 ◽  
Vol 23 ◽  
pp. 9-17 ◽  
Author(s):  
Chao Zhu Zhang ◽  
Ahmed Kareem Abdullah
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Signal Analysis ◽  
Reference Signal ◽  
Wavelet Denoising ◽  
Eeg Signals ◽  
Neural Signal ◽  
Brain Signals ◽  
Recording Process ◽  
Different Types

Several problems in EEG-brain signal analysis are not solved, such as presence of an artifact during the recording process, particularly the eye artifact (ElectroOculoGram (EOG)) which makes the analysis of EEG-brain signals very difficult. Blind source separation technique is one of the important techniques used to clean the EEG signals from different types of artifacts. Independent component analysis (ICA) techniques are widely used for this purpose, but unfortunately the ICA techniques have inherent shortcoming such as source ambiguity and unordered components. Therefore, the researchers used ICA-Reference algorithm. The main problem in ICA-Reference algorithm is to find clean reference signal to extract the wanted signal. Recently, many algorithms proposed to generate the artifact reference, but unfortunately, clean artifact signal not satisfied. In this paper wavelet denoising technique is used to solve this problem by decompose the artifact reference signal into pure artifact signal and residual neural signal. The proposed algorithm used frontal channels instead of EOG channels to extract the EOG reference signal.

Auditory evoked response potentials in somatization disorder

1986 ◽  
Vol 20 (3) ◽  
pp. 237-248 ◽  
Author(s):  
Evian Gordon ◽  
Claudia Kraiuhin ◽  
Russell Meares ◽  
Alan Howson
Keyword(s):  
Evoked Response ◽  
Somatization Disorder ◽  
Auditory Evoked Response ◽  
Evoked Response Potentials

P3-054: Evoked Response Potentials: Which Features Help Distinguish MCI from Healthy Controls?

2016 ◽  
Vol 12 ◽  
pp. P837-P837
Author(s):  
Frank Knoefel ◽  
Bruce Wallace ◽  
Rocio Adriana Lopez Zunini ◽  
Michael Breau ◽  
Lisa Sweet ◽  
...  
Keyword(s):  
Evoked Response ◽  
Healthy Controls ◽  
Evoked Response Potentials
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