Suggestions for Improving the Quality of EEG Recordings: A Mini-Review of Proper Technique and Instrumentation

1977 ◽  
Vol 17 (3) ◽  
pp. 147-158 ◽  
Author(s):  
Timothy A. Pedley
Keyword(s):  
Eeg Recordings

EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

2019 ◽  
Vol 29 (03) ◽  
pp. 1850049 ◽  
Author(s):  
Magdalena Zieleniewska ◽  
Anna Duszyk ◽  
Piotr Różański ◽  
Marcin Pietrzak ◽  
Marta Bogotko ◽  
...  
Keyword(s):  
Matching Pursuit ◽  
Disorders Of Consciousness ◽  
Sleep Architecture ◽  
Quality Of Sleep ◽  
Software Environment ◽  
Continuous Description ◽  
Matching Pursuit Decomposition ◽  
Parametric Description ◽  
Eeg Recordings

We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

scholarly journals Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

2021 ◽  
Author(s):  
Duong Nhu ◽  
Mubeen Janmohamed ◽  
Lubna Shakhatreh ◽  
Ofer Gonen ◽  
Patrick Kwan ◽  
...  
Keyword(s):  
Royal Melbourne Hospital ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Model Variant ◽  
Eeg Recordings ◽  
Generalized Epilepsy ◽  
Electroencephalogram Eeg ◽  
Health Hospital ◽  
Epilepsy Monitoring

Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881–0.907) when trained on Alfred’s dataset and tested on RMH’s dataset, and 0.857 (95% CI, 0.847–0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2019 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2020 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2021 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Semantic scene-object consistency modulates N300/400 EEG components, but does not automatically facilitate object representations

2021 ◽  
Author(s):  
Lixiang Chen ◽  
Radoslaw Martin Cichy ◽  
Daniel Kaiser
Keyword(s):  
Object Perception ◽  
Object Representations ◽  
Natural Vision ◽  
Index Changes ◽  
Eeg Recordings ◽  
Semantic Scene ◽  
Task Irrelevant ◽  
Inconsistent Objects ◽  
Scene Object

AbstractDuring natural vision, objects rarely appear in isolation, but often within a semantically related scene context. Previous studies reported that semantic consistency between objects and scenes facilitates object perception, and that scene-object consistency is reflected in changes in the N300 and N400 components in EEG recordings. Here, we investigate whether these N300/N400 differences are indicative of changes in the cortical representation of objects. In two experiments, we recorded EEG signals while participants viewed semantically consistent or inconsistent objects within a scene; in Experiment 1, these objects were task-irrelevant, while in Experiment 2, they were directly relevant for behavior. In both experiments, we found reliable and comparable N300/400 differences between consistent and inconsistent scene-object combinations. To probe the quality of object representations, we performed multivariate classification analyses, in which we decoded the category of the objects contained in the scene. In Experiment 1, in which the objects were not task-relevant, object category could be decoded from around 100 ms after the object presentation, but no difference in decoding performance was found between consistent and inconsistent objects. By contrast, when the objects were task-relevant in Experiment 2, we found enhanced decoding of semantically consistent, compared to semantically inconsistent, objects. These results show that differences in N300/N400 components related to scene-object consistency do not index changes in cortical object representations, but rather reflect a generic marker of semantic violations. Further, our findings suggest that facilitatory effects between objects and scenes are task-dependent rather than automatic.

scholarly journals Automagic: Standardized Preprocessing of Big EEG Data

2018 ◽  
Author(s):  
Andreas Pedroni ◽  
Amirreza Bahreini ◽  
Nicolas Langer
Keyword(s):  
Large Scale ◽  
Correction Method ◽  
Pool Data ◽  
Imaging Data ◽  
Eeg Data ◽  
Aging Populations ◽  
Artifact Rejection ◽  
Eeg Recordings ◽  
Study Sites

AbstractElectroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2020 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

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