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Somervail R, Cataldi J, Stephan AM, Siclari F, Iannetti GD. Dusk2Dawn: an EEGLAB plugin for automatic cleaning of whole-night sleep electroencephalogram using Artifact Subspace Reconstruction. Sleep 2023; 46:zsad208. [PMID: 37542730 DOI: 10.1093/sleep/zsad208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 07/20/2023] [Indexed: 08/07/2023] Open
Abstract
Whole-night sleep electroencephalogram (EEG) is plagued by several types of large-amplitude artifacts. Common approaches to remove them are fraught with issues: channel interpolation, rejection of noisy intervals, and independent component analysis are time-consuming, rely on subjective user decisions, and result in signal loss. Artifact Subspace Reconstruction (ASR) is an increasingly popular approach to rapidly and automatically clean wake EEG data. Indeed, ASR adaptively removes large-amplitude artifacts regardless of their scalp topography or consistency throughout the recording. This makes ASR, at least in theory, a highly-promising tool to clean whole-night EEG. However, ASR crucially relies on calibration against a subset of relatively clean "baseline" data. This is problematic when the baseline changes substantially over time, as in whole-night EEG data. Here we tackled this issue and, for the first time, validated ASR for cleaning sleep EEG. We demonstrate that ASR applied out-of-the-box, with the parameters recommended for wake EEG, results in the dramatic removal of slow waves. We also provide an appropriate procedure to use ASR for automatic and rapid cleaning of whole-night sleep EEG data or any long EEG recording. Our procedure is freely available in Dusk2Dawn, an open-source plugin for EEGLAB.
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Affiliation(s)
- Richard Somervail
- Neuroscience and Behaviour Laboratory, Italian Institute of Technology (IIT), Rome, Italy
- Department of Neuroscience Physiology and Pharmacology, University College London (UCL), London, UK
| | - Jacinthe Cataldi
- Centre d'Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
| | - Aurélie M Stephan
- Centre d'Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Francesca Siclari
- Centre d'Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Gian Domenico Iannetti
- Neuroscience and Behaviour Laboratory, Italian Institute of Technology (IIT), Rome, Italy
- Department of Neuroscience Physiology and Pharmacology, University College London (UCL), London, UK
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Amin U, Nascimento FA, Karakis I, Schomer D, Benbadis SR. Normal variants and artifacts: Importance in EEG interpretation. Epileptic Disord 2023; 25:591-648. [PMID: 36938895 DOI: 10.1002/epd2.20040] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 03/21/2023]
Abstract
Overinterpretation of EEG is an important contributor to the misdiagnosis of epilepsy. For the EEG to have a high diagnostic value and high specificity, it is critical to recognize waveforms that can be mistaken for abnormal patterns. This article describes artifacts, normal rhythms, and normal patterns that are prone to being misinterpreted as abnormal. Artifacts are potentials generated outside the brain. They are divided into physiologic and extraphysiologic. Physiologic artifacts arise from the body and include EMG, eyes, various movements, EKG, pulse, and sweat. Some physiologic artifacts can be useful for interpretation such as EMG and eye movements. Extraphysiologic artifacts arise from outside the body, and in turn can be divided into the environments (electrodes, equipment, and cellphones) and devices within the body (pacemakers and neurostimulators). Normal rhythms can be divided into awake patterns (alpha rhythm and its variants, mu rhythm, lambda waves, posterior slow waves of youth, HV-induced slowing, photic driving, and photomyogenic response) and sleep patterns (POSTS, vertex waves, spindles, K complexes, sleep-related hypersynchrony, and frontal arousal rhythm). Breach can affect both awake and sleep rhythms. Normal variants or variants of uncertain clinical significance include variants that may have been considered abnormal in the early days of EEG but are now considered normal. These include wicket spikes and wicket rhythms (the most common normal pattern overread as epileptiform), small sharp spikes (aka benign epileptiform transients of sleep), rhythmic midtemporal theta of drowsiness (aka psychomotor variant), Cigánek rhythm (aka midline theta), 6 Hz phantom spike-wave, 14 and 6 Hz positive spikes, subclinical rhythmic epileptiform discharges of adults (SREDA), slow-fused transients, occipital spikes of blindness, and temporal slowing of the elderly. Correctly identifying artifacts and normal patterns can help avoid overinterpretation and misdiagnosis. This is an educational review paper addressing a learning objective of the International League Against Epilepsy (ILAE) curriculum.
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Affiliation(s)
- Ushtar Amin
- University of South Florida, Department of Neurology, Tampa, Florida, USA
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ioannis Karakis
- Emory University School of Medicine - Neurology, Atlanta, Georgia, USA
| | - Donald Schomer
- Beth Israel Deaconess Medical Center, Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Selim R Benbadis
- University of South Florida, Department of Neurology, Tampa, Florida, USA
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Yarici MC, Thornton M, Mandic DP. Ear-EEG sensitivity modeling for neural sources and ocular artifacts. Front Neurosci 2023; 16:997377. [PMID: 36699519 PMCID: PMC9868963 DOI: 10.3389/fnins.2022.997377] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
The ear-EEG has emerged as a promising candidate for real-world wearable brain monitoring. While experimental studies have validated several applications of ear-EEG, the source-sensor relationship for neural sources from across the brain surface has not yet been established. In addition, modeling of the ear-EEG sensitivity to sources of artifacts is still missing. Through volume conductor modeling, the sensitivity of various configurations of ear-EEG is established for a range of neural sources, in addition to ocular artifact sources for the blink, vertical saccade, and horizontal saccade eye movements. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG in scenarios prone to ocular artifacts is also supported, through the demonstration of proportional scaling of artifacts and neural signals in various configurations of ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of sensitivity to both neural sources and ocular artifacts.
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Ouyang G, Dien J, Lorenz R. Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration. J Neural Eng 2022; 19. [PMID: 34902847 DOI: 10.1088/1741-2552/ac42b6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 12/13/2021] [Indexed: 02/02/2023]
Abstract
Objective.Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research.Approach.We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio.Main results.SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI.Significance.The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.
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Affiliation(s)
- Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Joseph Dien
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States of America
| | - Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Psychology, Stanford University, Stanford, CA, United States of America
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Sunny MSH, Hossain S, Afroze N, Hasan MK, Hossain E, Rahman MH. Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination. Biomed Phys Eng Express 2021; 8. [PMID: 34852330 DOI: 10.1088/2057-1976/ac3f17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 12/01/2021] [Indexed: 11/12/2022]
Abstract
Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.
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Affiliation(s)
- Md Samiul Haque Sunny
- Department of Computer Science, University of Wisconsin-Milwaukee, WI 53211-3029, United States of America
| | - Shifat Hossain
- Department of Electronics Engineering, Kookmin University, Seoul, Republic of Korea
| | - Nashrah Afroze
- Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna-9203, Bangladesh
| | - Md Kamrul Hasan
- Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna-9203, Bangladesh
| | - Eklas Hossain
- Department of Electrical Engineering and Renewable Energy, Oregon Institute of Technology, Klamath Falls, OR-97601, United States of America
| | - Mohammad H Rahman
- Mechanical Engineering Department, University of Wisconsin-Milwaukee, WI 53211-3029, United States of America
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Horowitz AJ, Guger C, Korostenskaja M. What External Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance? HCA Healthc J Med 2021; 2:143-162. [PMID: 37427002 PMCID: PMC10324824 DOI: 10.36518/2689-0216.1188] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Description Sensorimotor rhythm-based brain-computer interfaces (SMR-BCIs) are used for the acquisition and translation of motor imagery-related brain signals into machine control commands, bypassing the usual central nervous system output. The selection of optimal external variable configuration can maximize SMR-BCI performance in both healthy and disabled people. This performance is especially important now when the BCI is targeted for everyday use in the environment beyond strictly regulated laboratory settings. In this review article, we summarize and critically evaluate the current body of knowledge pertaining to the effect of the external variables on SMR-BCI performance. When assessing the relationship between SMR-BCI performance and external variables, we broadly characterize them as elements that are less dependent on the BCI user and originate from beyond the user. These elements include such factors as BCI type, distractors, training, visual and auditory feedback, virtual reality and magneto electric feedback, proprioceptive and haptic feedback, carefulness of electroencephalography (EEG) system assembling and positioning of EEG electrodes as well as recording-related artifacts. At the end of this review paper, future developments are proposed regarding the research into the effects of external variables on SMR-BCI performance. We believe that our critical review will be of value for academic BCI scientists and developers and clinical professionals working in the field of BCIs as well as for SMR-BCI users.
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Affiliation(s)
- Alex J. Horowitz
- Functional Brain Mapping and Brain Computer Interface Lab, Neuroscience Institute, AdventHealth Orlando, Orlando, FL,
USA
- University of Central Florida/HCA Healthcare GME Consortium, Gainesville, Florida
| | | | - Milena Korostenskaja
- Functional Brain Mapping and Brain Computer Interface Lab, Neuroscience Institute, AdventHealth Orlando, Orlando, FL,
USA
- MEG Lab, AdventHealth for Children, Orlando, FL,
USA
- Department of Psychology, College of Arts and Sciences, University of North Florida, Jacksonville, FL,
USA
- Comprehensive Epilepsy Center, AdventHealth Orlando, Orlando, FL,
USA
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Leach SC, Morales S, Bowers ME, Buzzell GA, Debnath R, Beall D, Fox NA. Adjusting ADJUST: Optimizing the ADJUST algorithm for pediatric data using geodesic nets. Psychophysiology 2020; 57:e13566. [PMID: 32185818 PMCID: PMC7402217 DOI: 10.1111/psyp.13566] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 01/03/2020] [Accepted: 02/18/2020] [Indexed: 11/27/2022]
Abstract
A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time-consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults, but to our knowledge, no such algorithms have been optimized for pediatric data. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUST's algorithm. Our "adjusted-ADJUST" algorithm was compared to the "original-ADJUST" algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after preprocessing with each algorithm. Overall, the adjusted-ADJUST algorithm performed better than the original-ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, in some measures, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations. Adjusted-ADJUST is freely available under the terms of the GNU General Public License at: https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts.
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Affiliation(s)
- Stephanie C. Leach
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Santiago Morales
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Maureen E. Bowers
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA
| | - George A. Buzzell
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Ranjan Debnath
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Daniel Beall
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Nathan A. Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
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Brigham D, Shah Y, Singh K, Pavkovic I, Karkare S, Kothare SV. Comparison of artifacts between paste and collodion method of electrode application in pediatric EEG. Clin Neurophysiol Pract 2020; 5:12-15. [PMID: 31890993 PMCID: PMC6931097 DOI: 10.1016/j.cnp.2019.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 11/29/2022] Open
Abstract
This study was done to evaluate differences in artifacts between the collodion and paste method of EEG administration. Electrode problems are common with both collodion and paste in prolonged AEEG monitoring. For studies less than 24 h, collodion may be a better alternative.
Objectives Children pose challenges to obtain quality EEG data due to excessive artifact. Collodion is used in EEG electrodes due to its water resistance and strong adhesive qualities. This study was done to evaluate differences in artifacts between the collodion and paste method. Methods 115 subjects (children age >3 years) were randomized into paste and collodion groups and artifacts evaluated at baseline and every hour over 30 s increments. Age, sleep state, and number of electrodes with artifact were also documented. T-test was performed to determine differences in the various parameters between the two groups. Results 61 subjects were in the paste group and 54 in the collodion group. Mean of total seconds of artifact from 0 to 24 h were 41.8 s in paste group versus 30.3 s in collodion group (P = 0.02). Children >11 years old had less artifact than younger children from 0 to 24 h (24.3 versus 41.2 s, P = 0.03), and from 24 to 48 h (33.1 versus 43.1 s, P = 0.03). There was a significant effect of sleep vs. awake state recordings on artifact from 0 to 24 h (30.3 versus 50.2 s, P = 0.01). Conclusion Electrode problems are common with both collodion and paste in prolonged AEEG monitoring. However, for studies less than 24 h, collodion may be a better alternative. Significance Our study provides evidence that in some cases collodion may be a better alternative to paste in terms of decreased artifacts.
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Affiliation(s)
- Danielle Brigham
- Department of Pediatrics, Cohen Children's Medical Center, NY, USA
| | - Yash Shah
- Department of Pediatrics, Cohen Children's Medical Center, NY, USA
| | - Kanwaljit Singh
- Department of Pediatrics, University of Massachusetts Medical School, MA, USA
| | - Ivan Pavkovic
- Department of Pediatrics, Cohen Children's Medical Center, NY, USA
| | - Shefali Karkare
- Department of Pediatrics, Cohen Children's Medical Center, NY, USA
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Cuesta-Frau D, Miró-Martínez P, Jordán Núñez J, Oltra-Crespo S, Molina Picó A. Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Comput Biol Med 2017; 87:141-151. [PMID: 28595129 DOI: 10.1016/j.compbiomed.2017.05.028] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 05/05/2017] [Accepted: 05/28/2017] [Indexed: 11/19/2022]
Abstract
This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.
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Affiliation(s)
- David Cuesta-Frau
- Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain.
| | - Pau Miró-Martínez
- Department of Statistics, Polytechnic University of Valencia, Alcoi Campus, Alcoi, Spain
| | - Jorge Jordán Núñez
- Department of Statistics, Polytechnic University of Valencia, Alcoi Campus, Alcoi, Spain
| | - Sandra Oltra-Crespo
- Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain
| | - Antonio Molina Picó
- Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain
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10
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Abstract
A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.
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Affiliation(s)
- Andrea Mognon
- Functional NeuroImaging Laboratory, Center for Mind/Brain Sciences, Department of Cognitive and Education Sciences, University of Trento, Trento, ItalyNILab, Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, ItalyDepartment of Information Engineering and Computer Science, University of Trento, Trento, ItalyINSERM, U992, Cognitive Neuroimaging Unit, Gif/Yvette, FranceCEA, DSV/I2BM, NeuroSpin Center, Gif/Yvette, FranceUniversité Paris-Sud, Cognitive Neuroimaging Unit, Gif/Yvette, France
| | - Jorge Jovicich
- Functional NeuroImaging Laboratory, Center for Mind/Brain Sciences, Department of Cognitive and Education Sciences, University of Trento, Trento, ItalyNILab, Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, ItalyDepartment of Information Engineering and Computer Science, University of Trento, Trento, ItalyINSERM, U992, Cognitive Neuroimaging Unit, Gif/Yvette, FranceCEA, DSV/I2BM, NeuroSpin Center, Gif/Yvette, FranceUniversité Paris-Sud, Cognitive Neuroimaging Unit, Gif/Yvette, France
| | - Lorenzo Bruzzone
- Functional NeuroImaging Laboratory, Center for Mind/Brain Sciences, Department of Cognitive and Education Sciences, University of Trento, Trento, ItalyNILab, Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, ItalyDepartment of Information Engineering and Computer Science, University of Trento, Trento, ItalyINSERM, U992, Cognitive Neuroimaging Unit, Gif/Yvette, FranceCEA, DSV/I2BM, NeuroSpin Center, Gif/Yvette, FranceUniversité Paris-Sud, Cognitive Neuroimaging Unit, Gif/Yvette, France
| | - Marco Buiatti
- Functional NeuroImaging Laboratory, Center for Mind/Brain Sciences, Department of Cognitive and Education Sciences, University of Trento, Trento, ItalyNILab, Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, ItalyDepartment of Information Engineering and Computer Science, University of Trento, Trento, ItalyINSERM, U992, Cognitive Neuroimaging Unit, Gif/Yvette, FranceCEA, DSV/I2BM, NeuroSpin Center, Gif/Yvette, FranceUniversité Paris-Sud, Cognitive Neuroimaging Unit, Gif/Yvette, France
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11
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Cassani R, Falk TH, Fraga FJ, Kanda PAM, Anghinah R. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis. Front Aging Neurosci 2014; 6:55. [PMID: 24723886 PMCID: PMC3971195 DOI: 10.3389/fnagi.2014.00055] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 03/06/2014] [Indexed: 11/13/2022] Open
Abstract
Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
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Affiliation(s)
- Raymundo Cassani
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada
| | - Tiago H Falk
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada
| | - Francisco J Fraga
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada ; Engineering, Modelling and Applied Social Sciences Center, Universidade Federal do ABC São Paulo, Brazil
| | - Paulo A M Kanda
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, Universidade de São Paulo São Paulo, Brazil
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, Universidade de São Paulo São Paulo, Brazil
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Bhattacharyya S, Biswas A, Mukherjee J, Majumdar AK, Majumdar B, Mukherjee S, Singh AK. Detection of artifacts from high energy bursts in neonatal EEG. Comput Biol Med 2013; 43:1804-14. [PMID: 24209926 DOI: 10.1016/j.compbiomed.2013.07.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Revised: 07/27/2013] [Accepted: 07/29/2013] [Indexed: 11/19/2022]
Abstract
Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.
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Affiliation(s)
- Sourya Bhattacharyya
- Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, WB 721302, India.
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