scholarly journals Intra- and Inter-Rater Reliability of Manual Feature Extraction Methods in Movement Related Cortical Potential Analysis

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2427
Author(s):  
Gemma Alder ◽  
Nada Signal ◽  
Usman Rashid ◽  
Sharon Olsen ◽  
Imran Khan Niazi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Voluntary Movement ◽  
Intraclass Correlation ◽  
Event Related Potentials ◽  
Extraction Methods ◽  
Healthy People ◽  
Future Research ◽  
Rater Reliability ◽  
Related Potentials ◽  
Epoch Analysis

Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and inter-rater reliability were evaluated in five electroencephalography (EEG) experts who labelled the peak negativity of averaged movement related cortical potentials (MRCPs) derived from thirty datasets. Each dataset contained 50 MRCP epochs from healthy people performing cued voluntary or imagined movement, or people with stroke performing cued voluntary movement. Reliability was assessed using the intraclass correlation coefficient and standard error of measurement. Excellent intra- and inter-rater reliability was demonstrated in the voluntary movement conditions in healthy people and people with stroke. In comparison reliability in the imagined condition was low to moderate. Post-hoc secondary epoch analysis revealed that the morphology of the signal contributed to the consistency of epoch inclusion; potentially explaining the differences in reliability seen across conditions. Findings from this study may inform future research focused on developing automated labelling methods for ERP feature extraction and call to the wider community of researchers interested in utilizing ERPs as a measure of neurophysiological change or in the delivery of EEG-driven interventions.

scholarly journals Psychophysiology and Imaging of Visual Cortical Functions in the Blind: A Review

2008 ◽  
Vol 20 (3-4) ◽  
pp. 71-81 ◽  
Author(s):  
Stephanie L. Simon-Dack ◽  
P. Dennis Rodriguez ◽  
Wolfgang A. Teder-Sälejärvi
Keyword(s):  
Visual Cortex ◽  
Transcranial Magnetic Stimulation ◽  
Visual Information ◽  
Magnetic Stimulation ◽  
Late Onset ◽  
Event Related Potentials ◽  
Cortical Activation ◽  
Future Research ◽  
Related Potentials ◽  
Visual Cortical

Imaging, transcranial magnetic stimulation, and psychophysiological recordings of the congenitally blind have confirmed functional activation of the visual cortex but have not extensively explained the functional significance of these activation patterns in detail. This review systematically examines research on the role of the visual cortex in processing spatial and non-visual information, highlighting research on individuals with early and late onset blindness. Here, we concentrate on the methods utilized in studying visual cortical activation in early blind participants, including positron emissions tomography (PET), functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), and electrophysiological data, specifically event-related potentials (ERPs). This paper summarizes and discusses findings of these studies. We hypothesize how mechanisms of cortical plasticity are expressed in congenitally in comparison to adventitiously blind and short-term visually deprived sighted participants and discuss potential approaches for further investigation of these mechanisms in future research.

Feature Extraction Methods for Speaker Recognition: A Review

2017 ◽  
Vol 31 (12) ◽  
pp. 1750041 ◽  
Author(s):  
Gopal Chaudhary ◽  
Smriti Srivastava ◽  
Saurabh Bhardwaj
Keyword(s):  
Feature Extraction ◽  
Speech Processing ◽  
Speaker Recognition ◽  
Vocal Tract ◽  
Recognition System ◽  
Extraction Methods ◽  
Future Research ◽  
Time Variability ◽  
Noise Compensation ◽  
Clear Vision

This paper presents main paradigms of research for feature extraction methods to further augment the state of art in speaker recognition (SR) which has been recognized extensively in person identification for security and protection applications. Speaker recognition system (SRS) has become a widely researched topic for the last many decades. The basic concept of feature extraction methods is derived from the biological model of human auditory/vocal tract system. This work provides a classification-oriented review of feature extraction methods for SR over the last 55 years that are proven to be successful and have become the new stone to further research. Broadly, the review work is dichotomized into feature extraction methods with and without noise compensation techniques. Feature extraction methods without noise compensation techniques are divided into following categories: On the basis of high/low level of feature extraction; type of transform; speech production/auditory system; type of feature extraction technique; time variability; speech processing techniques. Further, feature extraction methods with noise compensation techniques are classified into noise-screened features, feature normalization methods, feature compensation methods. This classification-oriented review would endow the clear vision of readers to choose among different techniques and will be helpful in future research in this field.

scholarly journals Looking for consistency in an uncertain world: test-retest reliability of neurophysiological and behavioral readouts in autism

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shlomit Beker ◽  
John J. Foxe ◽  
John Venticinque ◽  
Juliana Bates ◽  
Elizabeth M. Ridgeway ◽  
...  
Keyword(s):  
Intraclass Correlation ◽  
Event Related Potentials ◽  
Autism Spectrum ◽  
Auditory Target ◽  
Experimental Conditions ◽  
Retest Reliability ◽  
Time Points ◽  
Electrical Brain Activity ◽  
Related Potentials ◽  
Test Retest Reliability

Abstract Background Autism spectrum disorders (ASD) are associated with altered sensory processing and perception. Scalp recordings of electrical brain activity time-locked to sensory events (event-related potentials; ERPs) provide precise information on the time-course of related altered neural activity, and can be used to model the cortical loci of the underlying neural networks. Establishing the test-retest reliability of these sensory brain responses in ASD is critical to their use as biomarkers of neural dysfunction in this population. Methods EEG and behavioral data were acquired from 33 children diagnosed with ASD aged 6–9.4 years old, while they performed a child-friendly task at two different time-points, separated by an average of 5.2 months. In two blocked conditions, participants responded to the occurrence of an auditory target that was either preceded or not by repeating visual stimuli. Intraclass correlation coefficients (ICCs) were used to assess test-retest reliability of measures of sensory (auditory and visual) ERPs and performance, for the two experimental conditions. To assess the degree of reliability of the variability of responses within individuals, this analysis was performed on the variance of the measurements, in addition to their means. This yielded a total of 24 measures for which ICCs were calculated. Results The data yielded significant good ICC values for 10 of the 24 measurements. These spanned across behavioral and ERPs data, experimental conditions, and mean as well as variance measures. Measures of the visual evoked responses accounted for a disproportionately large number of the significant ICCs; follow-up analyses suggested that the contribution of a greater number of trials to the visual compared to the auditory ERP partially accounted for this. Conclusions This analysis reveals that sensory ERPs and related behavior can be highly reliable across multiple measurement time-points in ASD. The data further suggest that the inter-trial and inter-participant variability reported in the ASD literature likely represents replicable individual participant neural processing differences. The stability of these neuronal readouts supports their use as biomarkers in clinical and translational studies on ASD. Given the minimum interval between test/retest sessions across our cohort, we also conclude that for the tested age-range of ~ 6 to 9.4 years, these reliability measures are valid for at least a 3-month interval. Limitations related to EEG task demands and study length in the context of a clinical trial are considered.

Classification of Brain Signals of Event Related Potentials using Different Methods of Feature Extraction

2017 ◽  
Vol 8 (4) ◽  
pp. 680-686
Author(s):  
Ishfaque Ahmed ◽  
Muhammad Jahangir ◽  
Syed Tanveer Iqbal ◽  
Muhammad Azhar ◽  
Imran Siddiqui
Keyword(s):  
Feature Extraction ◽  
Event Related Potentials ◽  
Brain Signals ◽  
Related Potentials

scholarly journals Analysis of independent components of cognitive event related potentials in a group of ADHD adults

PRILOZI ◽  
2016 ◽  
Vol 37 (1) ◽  
pp. 37-49
Author(s):  
Silvana Markovska-Simoska ◽  
Nada Pop-Jordanova ◽  
Jordan Pop-Jordanov
Keyword(s):  
Brain Activity ◽  
Event Related Potentials ◽  
Future Research ◽  
Adhd Group ◽  
Motor Inhibition ◽  
Independent Components ◽  
Lower Amplitude ◽  
Related Potentials ◽  
Neuropsychological Tasks ◽  
Psychological Operations

Abstract In the last decade, many studies have tried to define the neural correlates of attention deficit hyperactivity disorder (ADHD). The main aim of this study is the comparison of the ERPs independent components in the four QEEG subtypes in a group of ADHD adults as a basis for defining the corresponding endophenotypes among ADHD population. Sixty-seven adults diagnosed as ADHD according to the DSM-IV criteria and 50 age-matched control subjects participated in the study. The brain activity of the subjects was recorded by 19 channel quantitative electroencephalography (QEEG) system in two neuropsychological tasks (visual and emotional continuous performance tests). The ICA method was applied for separation of the independent ERPs components. The components were associated with distinct psychological operations, such as engagement operations (P3bP component), comparison (vcomTL and vcom TR), motor inhibition (P3supF) and monitoring (P4monCC) operations. The ERPs results point out that there is disturbance in executive functioning in investigated ADHD group obtained by the significantly lower amplitude and longer latency for the engagement (P3bP), motor inhibition (P3supF) and monitoring (P4monCC) components. Particularly, the QEEG subtype IV was with the most significant ERPs differences comparing to the other subtypes. In particular, the most prominent difference in the ERPs independent components for the QEEG subtype IV in comparison to other three subtypes, rise many questions and becomes the subject for future research. This study aims to advance and facilitate the use of neurophysiological procedures (QEEG and ERPs) in clinical practice as objective measures of ADHD for better assessment, subtyping and treatment of ADHD.

scholarly journals Electroencephalographic detection of respiratory-related cortical activity in humans: from event-related approaches to continuous connectivity evaluation

2016 ◽  
Vol 115 (4) ◽  
pp. 2214-2223 ◽  
Author(s):  
Anna L. Hudson ◽  
Xavier Navarro-Sune ◽  
Jacques Martinerie ◽  
Pierre Pouget ◽  
Mathieu Raux ◽  
...  
Keyword(s):  
Intraclass Correlation ◽  
Event Related Potentials ◽  
Cortical Activity ◽  
Event Related Potential ◽  
Computational Method ◽  
Brain State ◽  
Time Frequency ◽  
Significant Difference ◽  
Related Potentials ◽  
Respiratory State

The presence of a respiratory-related cortical activity during tidal breathing is abnormal and a hallmark of respiratory difficulties, but its detection requires superior discrimination and temporal resolution. The aim of this study was to validate a computational method using EEG covariance (or connectivity) matrices to detect a change in brain activity related to breathing. In 17 healthy subjects, EEG was recorded during resting unloaded breathing (RB), voluntary sniffs, and breathing against an inspiratory threshold load (ITL). EEG were analyzed by the specially developed covariance-based classifier, event-related potentials, and time-frequency (T-F) distributions. Nine subjects repeated the protocol. The classifier could accurately detect ITL and sniffs compared with the reference period of RB. For ITL, EEG-based detection was superior to airflow-based detection ( P < 0.05). A coincident improvement in EEG-airflow correlation in ITL compared with RB ( P < 0.05) confirmed that EEG detection relates to breathing. Premotor potential incidence was significantly higher before inspiration in sniffs and ITL compared with RB ( P < 0.05), but T-F distributions revealed a significant difference between sniffs and RB only ( P < 0.05). Intraclass correlation values ranged from poor (−0.2) to excellent (1.0). Thus, as for conventional event-related potential analysis, the covariance-based classifier can accurately predict a change in brain state related to a change in respiratory state, and given its capacity for near “real-time” detection, it is suitable to monitor the respiratory state in respiratory and critically ill patients in the development of a brain-ventilator interface.

scholarly journals Classification of event-related potentials using multivariate autoregressive modeling combined with simulated annealing

2003 ◽  
Vol 13 (1) ◽  
pp. 7-11 ◽  
Author(s):  
C.E. Vasios ◽  
O.K. Matsopoulos ◽  
K.S. Nikita ◽  
N. Uzunoglu
Keyword(s):  
Feature Extraction ◽  
Simulated Annealing ◽  
Drug Users ◽  
Back Propagation ◽  
Event Related Potentials ◽  
Back Propagation Algorithm ◽  
Related Potentials ◽  
And Control ◽  
Multivariate Autoregressive

In the present work, a new method for the classification of Event Related Potentials (ERPs) is proposed. The proposed method consists of two modules: the feature extraction module and the classification module. The feature extraction module comprises the implementation of the Multivariate Autoregressive model in conjunction with the Simulated Annealing technique, for the selection of optimum features from ERPs. The classification module is implemented with a single three-layer neural network, trained with the back-propagation algorithm and classifies the data into two classes: patients and control subjects. The method, in the form of a Decision Support System (DSS), has been thoroughly tested to a number of patient data (OCD, FES, depressives and drug users), resulting successful classification up to 100%.

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