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Redwan SM, Uddin MP, Ulhaq A, Sharif MI, Krishnamoorthy G. Power spectral density-based resting-state EEG classification of first-episode psychosis. Sci Rep 2024; 14:15154. [PMID: 38956297 PMCID: PMC11219808 DOI: 10.1038/s41598-024-66110-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 06/27/2024] [Indexed: 07/04/2024] Open
Abstract
Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.
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Affiliation(s)
- Sadi Md Redwan
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, 5200, Bangladesh
- School of Information Technology, Deakin University, Geelong, VIC, 3220, Australia
| | - Anwaar Ulhaq
- School of Engineering and Technology, Central Queensland University Australia, 400 Kent Street, Sydney, NSW, 2000, Australia.
| | | | - Govind Krishnamoorthy
- School of Psychology and Wellbeing, University of Southern Queensland, Ipswich, QLD, Australia
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2
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Falter-Wagner CM, Kiefer CM, Bailey AJ, Vogeley K, Dammers J. Perceptual Grouping in Autism Spectrum Disorder: An Exploratory Magnetoencephalography Study. J Autism Dev Disord 2024; 54:1101-1112. [PMID: 36512195 PMCID: PMC10907473 DOI: 10.1007/s10803-022-05844-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/15/2022]
Abstract
Visual information is organised according to visual grouping principles. In visual grouping tasks individuals with ASD have shown equivocal performance. We explored neural correlates of Gestalt grouping in individuals with and without ASD. Neuromagnetic activity of individuals with (15) and without (18) ASD was compared during a visual grouping task testing grouping by proximity versus similarity. Individuals without ASD showed stronger evoked responses with earlier peaks in response to both grouping types indicating an earlier neuronal differentiation between grouping principles in individuals without ASD. In contrast, individuals with ASD showed particularly prolonged processing of grouping by similarity suggesting a high demand of neural resources. The neuronal processing differences found could explain less efficient grouping performance observed behaviourally in ASD.
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Affiliation(s)
| | - Christian M Kiefer
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - Anthony J Bailey
- UBC Department of Psychiatry, University of British Columbia, 2255 Westbrook Mall, Vancouver, BC, V6T 2A1, Canada
| | - Kai Vogeley
- Department of Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany
- Institute of Neurosciences and Medicine-Cognitive Neuroscience, INM-3, Forschungszentrum Jülich, Jülich, Germany
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany.
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3
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Sawalma AS, Kiefer CM, Boers F, Shah NJ, Khudeish N, Neuner I, Herzallah MM, Dammers J. The effects of trauma on feedback processing: an MEG study. Front Neurosci 2023; 17:1172549. [PMID: 38027493 PMCID: PMC10651751 DOI: 10.3389/fnins.2023.1172549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
The cognitive impact of psychological trauma can manifest as a range of post-traumatic stress symptoms that are often attributed to impairments in learning from positive and negative outcomes, aka reinforcement learning. Research on the impact of trauma on reinforcement learning has mainly been inconclusive. This study aimed to circumscribe the impact of psychological trauma on reinforcement learning in the context of neural response in time and frequency domains. Two groups of participants were tested - those who had experienced psychological trauma and a control group who had not - while they performed a probabilistic classification task that dissociates learning from positive and negative feedback during a magnetoencephalography (MEG) examination. While the exposure to trauma did not exhibit any effects on learning accuracy or response time for positive or negative feedback, MEG cortical activity was modulated in response to positive feedback. In particular, the medial and lateral orbitofrontal cortices (mOFC and lOFC) exhibited increased activity, while the insular and supramarginal cortices showed decreased activity during positive feedback presentation. Furthermore, when receiving negative feedback, the trauma group displayed higher activity in the medial portion of the superior frontal cortex. The timing of these activity changes occurred between 160 and 600 ms post feedback presentation. Analysis of the time-frequency domain revealed heightened activity in theta and alpha frequency bands (4-10 Hz) in the lOFC in the trauma group. Moreover, dividing the two groups according to their learning performance, the activity for the non-learner subgroup was found to be lower in lOFC and higher in the supramarginal cortex. These differences were found in the trauma group only. The results highlight the localization and neural dynamics of feedback processing that could be affected by exposure to psychological trauma. This approach and associated findings provide a novel framework for understanding the cognitive correlates of psychological trauma in relation to neural dynamics in the space, time, and frequency domains. Subsequent work will focus on the stratification of cognitive and neural correlates as a function of various symptoms of psychological trauma. Clinically, the study findings and approach open the possibility for neuromodulation interventions that synchronize cognitive and psychological constructs for individualized treatment.
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Affiliation(s)
- Abdulrahman S. Sawalma
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Palestinian Neuroscience Initiative, Al-Quds University, Abu Dis, Palestine
| | - Christian M. Kiefer
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - Frank Boers
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-11), Jülich Aachen Research Alliance (JARA), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA)-Brain – Translational Medicine, Aachen, Germany
- Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Nibal Khudeish
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA)-Brain – Translational Medicine, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Mohammad M. Herzallah
- Palestinian Neuroscience Initiative, Al-Quds University, Abu Dis, Palestine
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Medicine, RWTH Aachen University, Aachen, Germany
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4
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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: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [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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Zhang X, Liu W, Xu F, He W, Song Y, Li G, Zhang Y, Dai G, Xiao Q, Meng Q, Zeng X, Bai S, Zhong R. Neural signals-based respiratory motion tracking: a proof-of-concept study. Phys Med Biol 2023; 68:195015. [PMID: 37683675 DOI: 10.1088/1361-6560/acf819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective.Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking.Approach.The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion.Main results.Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87.Significance.Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.
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Affiliation(s)
- Xiangbin Zhang
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Wenjie Liu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, People's Republic of China
| | - Feng Xu
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Weizhong He
- Magstim Electrical Geodesics, Inc, Plymouth, MA, United States of America
| | - Yingpeng Song
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Guangjun Li
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Yingjie Zhang
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Guyu Dai
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Qing Xiao
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Qianqian Meng
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Xianhu Zeng
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Sen Bai
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Renming Zhong
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
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Kampel N, Kiefer CM, Shah NJ, Neuner I, Dammers J. Neural fingerprinting on MEG time series using MiniRocket. Front Neurosci 2023; 17:1229371. [PMID: 37799343 PMCID: PMC10547883 DOI: 10.3389/fnins.2023.1229371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments.
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Affiliation(s)
- Nikolas Kampel
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, Germany
| | - Christian M. Kiefer
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA) – BRAIN – Translational Medicine, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-11), Jülich Aachen Research Alliance (JARA), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, Germany
- Jülich Aachen Research Alliance (JARA) – BRAIN – Translational Medicine, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, Germany
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7
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Kao C, Zhang Y. Detecting Emotional Prosody in Real Words: Electrophysiological Evidence From a Modified Multifeature Oddball Paradigm. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2023; 66:2988-2998. [PMID: 37379567 DOI: 10.1044/2023_jslhr-22-00652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
PURPOSE Emotional voice conveys important social cues that demand listeners' attention and timely processing. This event-related potential study investigated the feasibility of a multifeature oddball paradigm to examine adult listeners' neural responses to detecting emotional prosody changes in nonrepeating naturally spoken words. METHOD Thirty-three adult listeners completed the experiment by passively listening to the words in neutral and three alternating emotions while watching a silent movie. Previous research documented preattentive change-detection electrophysiological responses (e.g., mismatch negativity [MMN], P3a) to emotions carried by fixed syllables or words. Given that the MMN and P3a have also been shown to reflect extraction of abstract regularities over repetitive acoustic patterns, this study employed a multifeature oddball paradigm to compare listeners' MMN and P3a to emotional prosody change from neutral to angry, happy, and sad emotions delivered with hundreds of nonrepeating words in a single recording session. RESULTS Both MMN and P3a were successfully elicited by the emotional prosodic change over the varying linguistic context. Angry prosody elicited the strongest MMN compared with happy and sad prosodies. Happy prosody elicited the strongest P3a in the centro-frontal electrodes, and angry prosody elicited the smallest P3a. CONCLUSIONS The results demonstrated that listeners were able to extract the acoustic patterns for each emotional prosody category over constantly changing spoken words. The findings confirm the feasibility of the multifeature oddball paradigm in investigating emotional speech processing beyond simple acoustic change detection, which may potentially be applied to pediatric and clinical populations.
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Affiliation(s)
- Chieh Kao
- Department of Speech-Language-Hearing Sciences, University of Minnesota, Twin Cities
- Center for Cognitive Sciences, University of Minnesota, Twin Cities
| | - Yang Zhang
- Department of Speech-Language-Hearing Sciences, University of Minnesota, Twin Cities
- Masonic Institute for the Developing Brain, University of Minnesota, Twin Cities
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8
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Gunasekaran H, Azizi L, van Wassenhove V, Herbst SK. Characterizing endogenous delta oscillations in human MEG. Sci Rep 2023; 13:11031. [PMID: 37419933 PMCID: PMC10328979 DOI: 10.1038/s41598-023-37514-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/22/2023] [Indexed: 07/09/2023] Open
Abstract
Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.
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Affiliation(s)
- Harish Gunasekaran
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Leila Azizi
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Virginie van Wassenhove
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France.
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Nieto Mora D, Valencia S, Trujillo N, López JD, Martínez JD. Characterizing social and cognitive EEG-ERP through multiple kernel learning. Heliyon 2023; 9:e16927. [PMID: 37484433 PMCID: PMC10361029 DOI: 10.1016/j.heliyon.2023.e16927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 07/25/2023] Open
Abstract
EEG-ERP social-cognitive studies with healthy populations commonly fail to provide significant evidence due to low-quality data and the inherent similarity between groups. We propose a multiple kernel learning-based approach to enhance classification accuracy while keeping the traceability of the features (frequency bands or regions of interest) as a linear combination of kernels. These weights determine the relevance of each source of information, which is crucial for specialists. As a case study, we classify healthy ex-combatants of the Colombian armed conflict and civilians through a cognitive valence recognition task. Although previous works have shown accuracies below 80% with these groups, our proposal achieved an F1 score of 98%, revealing the most relevant bands and brain regions, which are the base for socio-cognitive trainings. With this methodology, we aim to contribute to standardizing EEG analyses and enhancing their statistics.
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Affiliation(s)
- Daniel Nieto Mora
- Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano ITM - Medellín, Colombia
| | - Stella Valencia
- Grupo de Investigación Salud Mental, Facultad Nacional de Salud Pública, Universidad de Antioquia UDEA - Medellín, Colombia
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia UDEA - Medellín, Colombia
| | - Natalia Trujillo
- Grupo de Investigación Salud Mental, Facultad Nacional de Salud Pública, Universidad de Antioquia UDEA - Medellín, Colombia
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia UDEA - Medellín, Colombia
| | - Jose David López
- Engineering Faculty, Universidad de Antioquia UDEA - Medellín, Colombia
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10
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Edanami K, Kurosawa M, Yen HT, Kanazawa T, Abe Y, Kirimoto T, Yao Y, Matsui T, Sun G. Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107163. [PMID: 36191355 DOI: 10.1016/j.cmpb.2022.107163] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/24/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Continuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU. METHODS A non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates. RESULTS The results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland-Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms. CONCLUSION The proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings.
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Affiliation(s)
- Keisuke Edanami
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Denki Tsushin Daigaku, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
| | - Masaki Kurosawa
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Denki Tsushin Daigaku, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
| | - Hoang Thi Yen
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Denki Tsushin Daigaku, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
| | - Takeru Kanazawa
- Children's Medical Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Yoshifusa Abe
- Children's Medical Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Tetsuo Kirimoto
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Denki Tsushin Daigaku, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
| | - Yu Yao
- Bosch Center for Artificial Intelligence, Renningen, Germany
| | - Takemi Matsui
- Graduate School of System Design, Tokyo Metropolitan University, Tokyo, Japan
| | - Guanghao Sun
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Denki Tsushin Daigaku, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan.
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Hansen NC, Højlund A, Møller C, Pearce M, Vuust P. Musicians show more integrated neural processing of contextually relevant acoustic features. Front Neurosci 2022; 16:907540. [PMID: 36312026 PMCID: PMC9612920 DOI: 10.3389/fnins.2022.907540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/08/2022] [Indexed: 12/04/2022] Open
Abstract
Little is known about expertise-related plasticity of neural mechanisms for auditory feature integration. Here, we contrast two diverging hypotheses that musical expertise is associated with more independent or more integrated predictive processing of acoustic features relevant to melody perception. Mismatch negativity (MMNm) was recorded with magnetoencephalography (MEG) from 25 musicians and 25 non-musicians, exposed to interleaved blocks of a complex, melody-like multi-feature paradigm and a simple, oddball control paradigm. In addition to single deviants differing in frequency (F), intensity (I), or perceived location (L), double and triple deviants were included reflecting all possible feature combinations (FI, IL, LF, FIL). Following previous work, early neural processing overlap was approximated in terms of MMNm additivity by comparing empirical MMNms obtained with double and triple deviants to modeled MMNms corresponding to summed constituent single-deviant MMNms. Significantly greater subadditivity was found in musicians compared to non-musicians, specifically for frequency-related deviants in complex, melody-like stimuli. Despite using identical sounds, expertise effects were absent from the simple oddball paradigm. This novel finding supports the integrated processing hypothesis whereby musicians recruit overlapping neural resources facilitating more integrative representations of contextually relevant stimuli such as frequency (perceived as pitch) during melody perception. More generally, these specialized refinements in predictive processing may enable experts to optimally capitalize upon complex, domain-relevant, acoustic cues.
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Affiliation(s)
- Niels Chr. Hansen
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
- Department of Dramaturgy and Musicology, School of Communication and Culture, Aarhus University, Aarhus, Denmark
- *Correspondence: Niels Chr. Hansen,
| | - Andreas Højlund
- Department of Linguistics, Cognitive Science, and Semiotics, School of Communication and Culture, Aarhus University, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Cecilie Møller
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
- Department of Psychology and Behavioural Sciences, Aarhus University, Aarhus, Denmark
| | - Marcus Pearce
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
- School of Electronic Engineering and Computer Science, Cognitive Science Research Group and Centre for Digital Music, Queen Mary University of London, London, United Kingdom
| | - Peter Vuust
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
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12
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Yang Y, Truong ND, Eshraghian JK, Nikpour A, Kavehei O. Weak self-supervised learning for seizure forecasting: a feasibility study. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220374. [PMID: 35950196 PMCID: PMC9346358 DOI: 10.1098/rsos.220374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/12/2022] [Indexed: 05/27/2023]
Abstract
This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.
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Affiliation(s)
- Yikai Yang
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Jason K. Eshraghian
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Armin Nikpour
- Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, New South Wales 2006, Australia
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
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13
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Herbst SK, Obleser J, van Wassenhove V. Implicit Versus Explicit Timing-Separate or Shared Mechanisms? J Cogn Neurosci 2022; 34:1447-1466. [PMID: 35579985 DOI: 10.1162/jocn_a_01866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Time implicitly shapes cognition, but time is also explicitly represented, for instance, in the form of durations. Parsimoniously, the brain could use the same mechanisms for implicit and explicit timing. Yet, the evidence has been equivocal, revealing both joint versus separate signatures of timing. Here, we directly compared implicit and explicit timing using magnetoencephalography, whose temporal resolution allows investigating the different stages of the timing processes. Implicit temporal predictability was induced in an auditory paradigm by a manipulation of the foreperiod. Participants received two consecutive task instructions: discriminate pitch (indirect measure of implicit timing) or duration (direct measure of explicit timing). The results show that the human brain efficiently extracts implicit temporal statistics of sensory environments, to enhance the behavioral and neural responses to auditory stimuli, but that those temporal predictions did not improve explicit timing. In both tasks, attentional orienting in time during predictive foreperiods was indexed by an increase in alpha power over visual and parietal areas. Furthermore, pretarget induced beta power in sensorimotor and parietal areas increased during implicit compared to explicit timing, in line with the suggested role for beta oscillations in temporal prediction. Interestingly, no distinct neural dynamics emerged when participants explicitly paid attention to time, compared to implicit timing. Our work thus indicates that implicit timing shapes the behavioral and sensory response in an automatic way and is reflected in oscillatory neural dynamics, whereas the translation of implicit temporal statistics to explicit durations remains somewhat inconclusive, possibly because of the more abstract nature of this task.
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14
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Clarke MD, Larson E, Peterson ER, McCloy DR, Bosseler AN, Taulu S. Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data. Front Neurol 2022; 13:827529. [PMID: 35401424 PMCID: PMC8983818 DOI: 10.3389/fneur.2022.827529] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/31/2022] [Indexed: 11/25/2022] Open
Abstract
We discuss specific challenges and solutions in infant MEG, which is one of the most technically challenging areas of MEG studies. Our results can be generalized to a variety of challenging scenarios for MEG data acquisition, including clinical settings. We cover a wide range of steps in pre-processing, including movement compensation, suppression of magnetic interference from sources inside and outside the magnetically shielded room, suppression of specific physiological artifact components such as cardiac artifacts. In the assessment of the outcome of the pre-processing algorithms, we focus on comparing signal representation before and after pre-processing and discuss the importance of the different components of the main processing steps. We discuss the importance of taking the noise covariance structure into account in inverse modeling and present the proper treatment of the noise covariance matrix to accurately reflect the processing that was applied to the data. Using example cases, we investigate the level of source localization error before and after processing. One of our main findings is that statistical metrics of source reconstruction may erroneously indicate that the results are reliable even in cases where the data are severely distorted by head movements. As a consequence, we stress the importance of proper signal processing in infant MEG.
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Affiliation(s)
- Maggie D. Clarke
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Eric Larson
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Erica R. Peterson
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Daniel R. McCloy
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Alexis N. Bosseler
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Samu Taulu
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Physics, University of Washington, Seattle, WA, United States
- *Correspondence: Samu Taulu
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15
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Yang Y, Truong ND, Eshraghian JK, Maher C, Nikpour A, Kavehei O. A multimodal AI system for out-of-distribution generalization of seizure identification. IEEE J Biomed Health Inform 2022; 26:3529-3538. [PMID: 35263265 DOI: 10.1109/jbhi.2022.3157877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.
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16
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Kiefer CM, Ito J, Weidner R, Boers F, Shah NJ, Grün S, Dammers J. Revealing Whole-Brain Causality Networks During Guided Visual Searching. Front Neurosci 2022; 16:826083. [PMID: 35250461 PMCID: PMC8894880 DOI: 10.3389/fnins.2022.826083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 11/24/2022] Open
Abstract
In our daily lives, we use eye movements to actively sample visual information from our environment ("active vision"). However, little is known about how the underlying mechanisms are affected by goal-directed behavior. In a study of 31 participants, magnetoencephalography was combined with eye-tracking technology to investigate how interregional interactions in the brain change when engaged in two distinct forms of active vision: freely viewing natural images or performing a guided visual search. Regions of interest with significant fixation-related evoked activity (FRA) were identified with spatiotemporal cluster permutation testing. Using generalized partial directed coherence, we show that, in response to fixation onset, a bilateral cluster consisting of four regions (posterior insula, transverse temporal gyri, superior temporal gyrus, and supramarginal gyrus) formed a highly connected network during free viewing. A comparable network also emerged in the right hemisphere during the search task, with the right supramarginal gyrus acting as a central node for information exchange. The results suggest that all four regions are vital to visual processing and guiding attention. Furthermore, the right supramarginal gyrus was the only region where activity during fixations on the search target was significantly negatively correlated with search response times. Based on our findings, we hypothesize that, following a fixation, the right supramarginal gyrus supplies the right supplementary eye field (SEF) with new information to update the priority map guiding the eye movements during the search task.
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Affiliation(s)
- Christian M. Kiefer
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance (JARA)-Brain – Institute Brain Structure and Function, Institute of Neuroscience and Medicine (INM-10), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Junji Ito
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA)-Brain – Institute Brain Structure and Function, Institute of Neuroscience and Medicine (INM-10), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ralph Weidner
- Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Frank Boers
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-11), Jülich Aachen Research Alliance (JARA), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA)-Brain – Translational Medicine, Aachen, Germany
- Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA)-Brain – Institute Brain Structure and Function, Institute of Neuroscience and Medicine (INM-10), Forschungszentrum Jülich GmbH, Jülich, Germany
- Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
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17
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Brady B, Bardouille T. Periodic/Aperiodic Parameterization of Transient Oscillations (PAPTO): Implications for Healthy Ageing. Neuroimage 2022; 251:118974. [DOI: 10.1016/j.neuroimage.2022.118974] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/28/2022] [Accepted: 02/03/2022] [Indexed: 12/11/2022] Open
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18
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Caffarra S, Joo SJ, Bloom D, Kruper J, Rokem A, Yeatman JD. Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex. Hum Brain Mapp 2021; 42:5785-5797. [PMID: 34487405 PMCID: PMC8559498 DOI: 10.1002/hbm.25654] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 12/24/2022] Open
Abstract
The latency of neural responses in the visual cortex changes systematically across the lifespan. Here, we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion magnetic resonance imaging (MRI) and magnetoencephalography (MEG) sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individual's brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.
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Affiliation(s)
- Sendy Caffarra
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
- Basque Center on Cognition Brain and LanguageSan SebastianSpain
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Sung Jun Joo
- Department of PsychologyPusan National UniversityPusanRepublic of Korea
| | - David Bloom
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Jason D. Yeatman
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
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19
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Shahbakhti M, Beiramvand M, Rejer I, Augustyniak P, Broniec-Wojcik A, Wierzchon M, Marozas V. Simultaneous Eye Blink Characterization and Elimination from Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection. IEEE J Biomed Health Inform 2021; 26:1001-1012. [PMID: 34260361 DOI: 10.1109/jbhi.2021.3096984] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of drivers cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink and EEG band power features for the detection of driver drowsiness may be further boosted if a proper eye blink removal is also applied before EEG analysis. This paper proposes an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG data. METHODS Firstly, eye blink intervals (EBIs) are identified from the Fp1 EEG channel using variational mode extraction, and then blink-related features are derived. Secondly, the identified EBIs are projected to the rest of EEG channels and then filtered by a combination of principal component analysis and discrete wavelet transform. Thirdly, a support vector machine with 10-fold cross-validation is employed to classify alert and drowsy states from the derived blink and filtered EEG band power features. MAIN RESULTS When compared the synergy of eye blink and EEG features before and after filtering by the proposed algorithm, a significant improvement in the mean accuracy of driver drowsiness detection was achieved (71.2% vs. 78.1%, p<0.05). SIGNIFICANCE This paper validates a novel view of eye blinks as both a source of information and artifacts in EEG-based driver drowsiness detection.
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20
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Li M, Zhang Y. An improved MAMA-EMD for the automatic removal of EOG artifacts. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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21
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Tuckute G, Hansen ST, Kjaer TW, Hansen LK. Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback. Neural Comput 2021; 33:967-1004. [PMID: 33513324 DOI: 10.1162/neco_a_01363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/16/2020] [Indexed: 11/04/2022]
Abstract
Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance = 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction p=7.23e-4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.
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Affiliation(s)
- Greta Tuckute
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark, and Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, U.S.A.,
| | - Sofie Therese Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, 4000 Roskilde, Denmark, and Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark,
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
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22
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Ablin P, Cardoso JF, Gramfort A. Spectral Independent Component Analysis with noise modeling for M/EEG source separation. J Neurosci Methods 2021; 356:109144. [PMID: 33771653 DOI: 10.1016/j.jneumeth.2021.109144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 03/08/2021] [Accepted: 03/17/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Independent Component Analysis (ICA) is a widespread tool for exploration and denoising of electroencephalography (EEG) or magnetoencephalography (MEG) signals. In its most common formulation, ICA assumes that the signal matrix is a noiseless linear mixture of independent sources that are assumed non-Gaussian. A limitation is that it enforces to estimate as many sources as sensors or to rely on a detrimental PCA step. METHODS We present the Spectral Matching ICA (SMICA) model. Signals are modelled as a linear mixing of independent sources corrupted by additive noise, where sources and the noise are stationary Gaussian time series. Thanks to the Gaussian assumption, the negative log-likelihood has a simple expression as a sum of 'divergences' between the empirical spectral covariance matrices of the signals and those predicted by the model. The model parameters can then be estimated by the expectation-maximization (EM) algorithm. RESULTS On phantom MEG datasets with low amplitude dipole sources (20 nAm), SMICA makes a median dipole localization error of 1.5 mm while competing methods make an error ≥7 mm. Experiments on EEG datasets show that SMICA identifies a source subspace which contains sources that have less pairwise mutual information, and are better explained by the projection of a single dipole on the scalp. With 10 sources, the number of strongly dipolar sources (dipolarity >90%) is more than 80% for SMICA while competing methods do not exceed 65%. COMPARISON WITH EXISTING METHODS With the noisy model of SMICA, the number of sources to be recovered is controlled by choosing the size of the mixing matrix to be fitted rather than by a preprocessing step of dimension reduction which is required in traditional noise-free ICA methods. CONCLUSIONS SMICA is a promising alternative to other noiseless ICA models based on non-Gaussian assumptions.
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Affiliation(s)
- Pierre Ablin
- CNRS and DMA, Ecole Normale Supérieure - PSL University, Paris, France; Inria Saclay, Université Paris-Saclay, Palaiseau, France.
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23
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Shahbakhti M, Beiramvand M, Nazari M, Broniec-Wojcik A, Augustyniak P, Rodrigues AS, Wierzchon M, Marozas V. VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel. IEEE Trans Neural Syst Rehabil Eng 2021; 29:408-417. [PMID: 33497337 DOI: 10.1109/tnsre.2021.3054733] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel. METHOD The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel. RESULTS The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from -8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87). SIGNIFICANCE The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.
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24
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Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG. Brain Sci 2019; 9:brainsci9120352. [PMID: 31810263 PMCID: PMC6955982 DOI: 10.3390/brainsci9120352] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 11/27/2019] [Indexed: 12/20/2022] Open
Abstract
The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.
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Neuner I, Rajkumar R, Brambilla CR, Ramkiran S, Ruch A, Orth L, Farrher E, Mauler J, Wyss C, Kops ER, Scheins J, Tellmann L, Lang M, Ermert J, Dammers J, Neumaier B, Lerche C, Heekeren K, Kawohl W, Langen KJ, Herzog H, Shah NJ. Simultaneous PET-MR-EEG: Technology, Challenges and Application in Clinical Neuroscience. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2018.2886525] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Abstract
The interference of artefacts with evoked scalp electroencephalogram (EEG) responses is a problem in event related brain computer interface (BCI) system that reduces signal quality and interpretability of user's intentions. Many strategies have been proposed to reduce the effects of non-neural artefacts, while the activity of neural sources that do not reflect the considered stimulation has been neglected. However discerning such activities from those to be retained is important, but subtle and difficult as most of their features are the same. We propose an automated method based on a combination of a genetic algorithm (GA) and a support vector machine (SVM) to select only the sources of interest. Temporal, spectral, wavelet, autoregressive and spatial properties of independent components (ICs) of EEG are inspected. The method selects the most distinguishing subset of features among this comprehensive fused set of information and identifies the components to be preserved. EEG data were recorded from 12 healthy subjects in a visual evoked potential (VEP) based BCI paradigm and the corresponding ICs were classified by experts to train and test the algorithm. They were contaminated with different sources of artefacts, including electromyogram (EMG), electrode connection problems, blinks and electrocardiogram (ECG), together with neural contributions not related to VEPs. The accuracy of ICs classification was about 88.5% and the energetic residual error in recovering the clean signals was 3%. These performances indicate that this automated method can effectively identify and remove main artefacts derived from either neural or non-neural sources while preserving VEPs. This could have important potential applications, contributing to speed and remove subjectivity of the cleaning procedure by experts. Moreover, it could be included in a real time BCI as a pre-processing step before the identification of the user’s intention.
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Bardouille T, Bailey L. Evidence for age-related changes in sensorimotor neuromagnetic responses during cued button pressing in a large open-access dataset. Neuroimage 2019; 193:25-34. [PMID: 30849530 DOI: 10.1016/j.neuroimage.2019.02.065] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 02/24/2019] [Accepted: 02/25/2019] [Indexed: 11/27/2022] Open
Abstract
Mu, beta, and gamma rhythms increase and decrease in amplitude during movement. This event-related synchronization (ERS) and desynchronization (ERD) can be readily recorded non-invasively using magneto- and electro-encephalography (M/EEG). In addition, event-related potentials and fields (i.e., evoked responses) can be elucidated during movement. There is some evidence that the frequency, amplitude and latency of the movement-related ERS/ERD changes with ageing, however the evidence surrounding this topic comes mainly from studies in sample sizes on the order of tens of participants. The objective of this study was to examine a large open-access MEG dataset for age-related changes in movement-related ERS/ERD and evoked responses. MEG data acquired at the Cambridge Centre for Ageing and Neuroscience during cued button pressing was used from 567 participants between the ages of 18 and 88 years. The characteristics movement-related ERD/ERS and evoked responses were calculated for each individual participant. Based on linear regression analysis, significant relationships were found between participant age and some response characteristics, although the predictive value of these relationships was low. Specifically, we conclude that peak beta rebound frequency and amplitude decreased with age, peak beta suppression amplitude increased with age, movement-related gamma burst amplitude decreased with age, and peak motor-evoked response amplitude increased with age. Given our current understanding of the underlying mechanisms of these responses, our findings suggest the existence of age-related changes in the neurophysiology of thalamocortical loops and local circuitry in the primary somatosensory and motor cortices.
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Affiliation(s)
- Timothy Bardouille
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.
| | - Lyam Bailey
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada
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- Cambridge Center for Ageing and Neuroscience, University of Cambridge, Cambridge, UK
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Jas M, Larson E, Engemann DA, Leppäkangas J, Taulu S, Hämäläinen M, Gramfort A. A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices. Front Neurosci 2018; 12:530. [PMID: 30127712 PMCID: PMC6088222 DOI: 10.3389/fnins.2018.00530] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Accepted: 07/16/2018] [Indexed: 11/13/2022] Open
Abstract
Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets. While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data often require subject-specific decisions which are hard to document. This work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS vs. SSS, the use of a minimum norm inverse vs. LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.
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Affiliation(s)
- Mainak Jas
- Telecom ParisTech, Université Paris-Saclay, Paris, France
| | - Eric Larson
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Denis A Engemann
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INRIA, Université Paris-Saclay, Saclay, France
| | | | - Samu Taulu
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States.,Department of Physics, University of Washington, Seattle, WA, United States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Alexandre Gramfort
- Telecom ParisTech, Université Paris-Saclay, Paris, France.,NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INRIA, Université Paris-Saclay, Saclay, France
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Lau P, Wollbrink A, Wunderlich R, Engell A, Löhe A, Junghöfer M, Pantev C. Targeting Heterogeneous Findings in Neuronal Oscillations in Tinnitus: Analyzing MEG Novices and Mental Health Comorbidities. Front Psychol 2018; 9:235. [PMID: 29551983 PMCID: PMC5841018 DOI: 10.3389/fpsyg.2018.00235] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 02/12/2018] [Indexed: 12/12/2022] Open
Abstract
Tinnitus is a prevalent phenomenon and bothersome for people affected by it. Its occurrence and maintenance have a clear neuroscientific tie and one aspect are differences in the neuronal oscillatory pattern, especially in auditory cortical areas. As studies in this field come to different results, the aim of this study was to analyze a large number of participants to achieve more stable results. Furthermore, we expanded our analysis to two variables of potential influence, namely being a novice to neuroscientific measurements and the exclusion of psychological comorbidities. Oscillatory brain activity of 88 subjects (46 with a chronic tinnitus percept, 42 without) measured in resting state by MEG was investigated. In the analysis based on the whole group, in sensor space increased activity in the delta frequency band was found in tinnitus patients. Analyzing the subgroup of novices, a significant difference in the theta band emerged additionally to the delta band difference (sensor space). Localizing the origin of the activity, we found a difference in theta and gamma band for the auditory regions for the whole group and the same significant difference in the subgroup of novices. However, no differences in oscillatory activity were observed between tinnitus and control groups once subjects with mental health comorbidity were excluded. Against the background of previous studies, the study at hand underlines the fragility of the results in the field of neuronal cortical oscillations in tinnitus. It supports the body of research arguing for low frequency oscillations and gamma band activity as markers associated with tinnitus.
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Affiliation(s)
- Pia Lau
- Institute for Biomagnetism and Biosignalanalysis, University Hospital of Münster, Münster, Germany
| | - Andreas Wollbrink
- Institute for Biomagnetism and Biosignalanalysis, University Hospital of Münster, Münster, Germany
| | - Robert Wunderlich
- Institute for Biomagnetism and Biosignalanalysis, University Hospital of Münster, Münster, Germany
| | - Alva Engell
- Institute for Biomagnetism and Biosignalanalysis, University Hospital of Münster, Münster, Germany
| | - Alwina Löhe
- Institute for Biomagnetism and Biosignalanalysis, University Hospital of Münster, Münster, Germany
| | - Markus Junghöfer
- Institute for Biomagnetism and Biosignalanalysis, University Hospital of Münster, Münster, Germany
| | - Christo Pantev
- Institute for Biomagnetism and Biosignalanalysis, University Hospital of Münster, Münster, Germany
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30
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Andersen LM. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front Neurosci 2018; 12:6. [PMID: 29403349 PMCID: PMC5786561 DOI: 10.3389/fnins.2018.00006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/04/2018] [Indexed: 11/30/2022] Open
Abstract
An important aim of an analysis pipeline for magnetoencephalographic data is that it allows for the researcher spending maximal effort on making the statistical comparisons that will answer the questions of the researcher, while in turn spending minimal effort on the intricacies and machinery of the pipeline. I here present a set of functions and scripts that allow for setting up a clear, reproducible structure for separating raw and processed data into folders and files such that minimal effort can be spend on: (1) double-checking that the right input goes into the right functions; (2) making sure that output and intermediate steps can be accessed meaningfully; (3) applying operations efficiently across groups of subjects; (4) re-processing data if changes to any intermediate step are desirable. Applying the scripts requires only general knowledge about the Python language. The data analyses are neural responses to tactile stimulations of the right index finger in a group of 20 healthy participants acquired from an Elekta Neuromag System. Two analyses are presented: going from individual sensor space representations to, respectively, an across-group sensor space representation and an across-group source space representation. The processing steps covered for the first analysis are filtering the raw data, finding events of interest in the data, epoching data, finding and removing independent components related to eye blinks and heart beats, calculating participants' individual evoked responses by averaging over epoched data and calculating a grand average sensor space representation over participants. The second analysis starts from the participants' individual evoked responses and covers: estimating noise covariance, creating a forward model, creating an inverse operator, estimating distributed source activity on the cortical surface using a minimum norm procedure, morphing those estimates onto a common cortical template and calculating the patterns of activity that are statistically different from baseline. To estimate source activity, processing of the anatomy of subjects based on magnetic resonance imaging is necessary. The necessary steps are covered here: importing magnetic resonance images, segmenting the brain, estimating boundaries between different tissue layers, making fine-resolution scalp surfaces for facilitating co-registration, creating source spaces and creating volume conductors for each subject.
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Affiliation(s)
- Lau M Andersen
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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31
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Abstract
We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.
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Affiliation(s)
- Mainak Jas
- LTCI, Télécom ParisTech, Université Paris-Saclay, France.
| | - Denis A Engemann
- Parietal project-team, INRIA Saclay - Ile de France, France; Cognitive Neuroimaging Unit, Neurospin, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France; Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, F-75013, Paris, France
| | - Yousra Bekhti
- LTCI, Télécom ParisTech, Université Paris-Saclay, France
| | - Federico Raimondo
- Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, F-75013, Paris, France; Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación, FCEyN, Universidad de Buenos Aires, Argentina; CONICET, Argentina; Sorbonne Universités, UPMC Univ Paris 06, Faculté de Médecine Pitié-Salpêtrière, Paris, France
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Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks. SENSORS 2017; 17:s17061326. [PMID: 28594352 PMCID: PMC5492863 DOI: 10.3390/s17061326] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/25/2017] [Accepted: 05/04/2017] [Indexed: 01/31/2023]
Abstract
Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation XCorr and peak signal to noise ratio (PSNR) (ANOVA, p ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.
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Local recording of biological magnetic fields using Giant Magneto Resistance-based micro-probes. Sci Rep 2016; 6:39330. [PMID: 27991562 PMCID: PMC5171880 DOI: 10.1038/srep39330] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 11/18/2016] [Indexed: 11/08/2022] Open
Abstract
The electrical activity of brain, heart and skeletal muscles generates magnetic fields but these are recordable only macroscopically, such as in magnetoencephalography, which is used to map neuronal activity at the brain scale. At the local scale, magnetic fields recordings are still pending because of the lack of tools that can come in contact with living tissues. Here we present bio-compatible sensors based on Giant Magneto-Resistance (GMR) spin electronics. We show on a mouse muscle in vitro, using electrophysiology and computational modeling, that this technology permits simultaneous local recordings of the magnetic fields from action potentials. The sensitivity of this type of sensor is almost size independent, allowing the miniaturization and shaping required for in vivo/vitro magnetophysiology. GMR-based technology can constitute the magnetic counterpart of microelectrodes in electrophysiology, and might represent a new fundamental tool to investigate the local sources of neuronal magnetic activity.
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Sun L, Ahlfors SP, Hinrichs H. Removing Cardiac Artefacts in Magnetoencephalography with Resampled Moving Average Subtraction. Brain Topogr 2016; 29:783-790. [PMID: 27503196 DOI: 10.1007/s10548-016-0513-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 08/03/2016] [Indexed: 12/01/2022]
Abstract
Magnetoencephalography (MEG) signals are commonly contaminated by cardiac artefacts (CAs). Principle component analysis and independent component analysis have been widely used for removing CAs, but they typically require a complex procedure for the identification of CA-related components. We propose a simple and efficient method, resampled moving average subtraction (RMAS), to remove CAs from MEG data. Based on an electrocardiogram (ECG) channel, a template for each cardiac cycle was estimated by a weighted average of epochs of MEG data over consecutive cardiac cycles, combined with a resampling technique for accurate alignment of the time waveforms. The template was subtracted from the corresponding epoch of the MEG data. The resampling reduced distortions due to asynchrony between the cardiac cycle and the MEG sampling times. The RMAS method successfully suppressed CAs while preserving both event-related responses and high-frequency (>45 Hz) components in the MEG data.
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Affiliation(s)
- Limin Sun
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA. .,Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Seppo P Ahlfors
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Hermann Hinrichs
- Department of Neurology, Otto-von-Guericke University, Leipziger Straße 44, 39120, Magdeburg, Germany.,Department of Behavioural Neurology, Leibniz Institute of Neurobiology (LIN), Magdeburg, Germany.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany.,Forschungscampus STIMULATE, Magdeburg, Germany
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35
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Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016; 2016:7489108. [PMID: 27524998 PMCID: PMC4972935 DOI: 10.1155/2016/7489108] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 06/23/2016] [Indexed: 12/05/2022]
Abstract
We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal—slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low—in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.
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Abbasi O, Dammers J, Arrubla J, Warbrick T, Butz M, Neuner I, Shah NJ. Time-frequency analysis of resting state and evoked EEG data recorded at higher magnetic fields up to 9.4 T. J Neurosci Methods 2015. [PMID: 26213220 DOI: 10.1016/j.jneumeth.2015.07.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Combining both high temporal and spatial resolution by means of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is of relevance to neuroscientists. This combination, however, leads to a distortion of the EEG signal by the so-called cardio-ballistic artefacts. The aim of the present study was developing an approach to restore meaningful physiological EEG data from recordings at different magnetic fields. NEW METHODS The distortions introduced by the magnetic field were corrected using a combination of concepts from independent component analysis (ICA) and mutual information (MI). Thus, the components were classified as either related to the cardio-ballistic artefacts or to the signals of interest. EEG data from two experimental paradigms recorded at different magnetic field strengths up to 9.4 T were analyzed: (i) spontaneous activity using an eyes-open/eyes-closed alternation, and (ii) responses to auditory stimuli, i.e. auditory evoked potentials. RESULTS Even at ultra-high magnetic fields up to 9.4 T the proposed artefact rejection approach restored the physiological time-frequency information contained in the signal of interest and the data were suitable for subsequent analyses. COMPARISON WITH EXISTING METHODS Blind source separation (BSS) has been used to retrieve information from EEG data recorded inside the MR scanner in previous studies. After applying the presented method on EEG data recorded at 4 T, 7 T, and 9.4 T, we could retrieve more information than from data cleaned with the BSS method. CONCLUSIONS The present work demonstrates that EEG data recorded at ultra-high magnetic fields can be used for studying neuroscientific research question related to oscillatory activity.
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Affiliation(s)
- Omid Abbasi
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Medical Engineering, Ruhr-Universität Bochum, Bochum, Germany.
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany.
| | - Jorge Arrubla
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.
| | - Tracy Warbrick
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany.
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Irene Neuner
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany; JARA-BRAIN-Translational Medicine, RWTH Aachen University, Aachen, Germany.
| | - N Jon Shah
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany.
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Abstract
This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.
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Affiliation(s)
- Jose Antonio Urigüen
- Deustotech-Life (eVida Research Group), University of Deusto, Facultad de Ingeniería, 4a Planta Avda/Universidades 24, 48007 Bilbao, Spain
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Recasens M, Leung S, Grimm S, Nowak R, Escera C. Repetition suppression and repetition enhancement underlie auditory memory-trace formation in the human brain: an MEG study. Neuroimage 2015; 108:75-86. [PMID: 25528656 DOI: 10.1016/j.neuroimage.2014.12.031] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Revised: 11/24/2014] [Accepted: 12/12/2014] [Indexed: 10/24/2022] Open
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39
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Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals. Neuroimage 2014; 108:328-42. [PMID: 25541187 DOI: 10.1016/j.neuroimage.2014.12.040] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Revised: 11/25/2014] [Accepted: 12/04/2014] [Indexed: 11/22/2022] Open
Abstract
Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.
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Wyss C, Boers F, Kawohl W, Arrubla J, Vahedipour K, Dammers J, Neuner I, Shah N. Spatiotemporal properties of auditory intensity processing in multisensor MEG. Neuroimage 2014; 102 Pt 2:465-73. [DOI: 10.1016/j.neuroimage.2014.08.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Revised: 07/26/2014] [Accepted: 08/05/2014] [Indexed: 12/27/2022] Open
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Breuer L, Dammers J, Roberts TPL, Shah NJ. A constrained ICA approach for real-time cardiac artifact rejection in magnetoencephalography. IEEE Trans Biomed Eng 2014; 61:405-14. [PMID: 24001953 DOI: 10.1109/tbme.2013.2280143] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.
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Breuer L, Dammers J, Roberts TPL, Shah NJ. Ocular and cardiac artifact rejection for real-time analysis in MEG. J Neurosci Methods 2014; 233:105-14. [PMID: 24954539 DOI: 10.1016/j.jneumeth.2014.06.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Revised: 06/11/2014] [Accepted: 06/12/2014] [Indexed: 10/25/2022]
Abstract
BACKGROUND Recently, magnetoencephalography (MEG) based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods for neuroscience research. It is well known that artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming process. NEW METHOD The method (referred to as ocular and cardiac artifact rejection for real-time analysis, OCARTA) is based on constrained independent component analysis (cICA), where a priori information of the underlying source signals is used to optimize and accelerate signal decomposition. Thereby, prior information is incorporated by using the subject's individual cardiac and ocular activity. The algorithm automatically uses different separation strategies depending on the underlying source activity. RESULTS OCARTA was tested and applied to data from three different but most commonly used MEG systems (4D-Neuroimaging, VSM MedTech Inc. and Elekta Neuromag). Ocular and cardiac artifacts were effectively reduced within one iteration at a time delay of 1ms performed on a standard PC (Intel Core i5-2410M). COMPARISON WITH EXISTING METHODS The artifact rejection results achieved with OCARTA are in line with the results reported for offline ICA-based artifact rejection methods. CONCLUSION Due to the fast and subject-specific signal decomposition the new approach introduced here is capable of real-time ocular and cardiac artifact rejection.
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Affiliation(s)
- Lukas Breuer
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany; Jülich Aachen Research Alliance (JARA) - Translational Brain Medicine, Jülich, Germany.
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Timothy P L Roberts
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - N Jon Shah
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany; Jülich Aachen Research Alliance (JARA) - Translational Brain Medicine, Jülich, Germany
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Hamaneh MB, Chitravas N, Kaiboriboon K, Lhatoo SD, Loparo KA. Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation. IEEE Trans Biomed Eng 2014; 61:1634-41. [DOI: 10.1109/tbme.2013.2295173] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Arrubla J, Neuner I, Dammers J, Breuer L, Warbrick T, Hahn D, Poole MS, Boers F, Shah NJ. Methods for pulse artefact reduction: experiences with EEG data recorded at 9.4 T static magnetic field. J Neurosci Methods 2014; 232:110-7. [PMID: 24858798 DOI: 10.1016/j.jneumeth.2014.05.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Revised: 04/28/2014] [Accepted: 05/13/2014] [Indexed: 11/19/2022]
Abstract
BACKGROUND The feasibility of recording electroencephalography (EEG) at ultra-high static magnetic fields up to 9.4 T was recently demonstrated and is expected to be incorporated into functional magnetic resonance imaging (fMRI) studies at 9.4 T. Correction of the pulse artefact (PA) is a significant challenge since its amplitude is proportional to the strength of the magnetic field in which EEG is recorded. NEW METHOD We conducted a study in which different PA correction methods were applied to EEG data recorded inside a 9.4 T scanner in order to retrieve visual P100 and auditory P300 evoked potentials. We explored different PA reduction methods, including the optimal basis set (OBS) method as well as objective and subjective component rejection using independent component analysis (ICA). RESULTS ICA followed by objective rejection of components is optimal for retrieving visual P100 and auditory P300 from EEG data recorded inside the scanner. COMPARISON WITH EXISTING METHODS Previous studies suggest that OBS or OBS followed by ICA are optimal for retrieving evoked potentials at 3T. In our EEG data recorded at 9.4 T OBS performed alone was not fully optimal for the identification of evoked potentials. OBS followed by ICA was partially effective. CONCLUSIONS In this study ICA has been shown to be an important tool for correcting the PA in EEG data recorded at 9.4 T, particularly when automated rejection of components is performed.
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Affiliation(s)
- Jorge Arrubla
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany.
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany; JARA - BRAIN - Translational Medicine, Germany
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany
| | - Lukas Breuer
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany; Department of Neurology, RWTH Aachen University, Germany
| | - Tracy Warbrick
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany
| | - David Hahn
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany
| | - Michael S Poole
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany
| | - Frank Boers
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Germany; JARA - BRAIN - Translational Medicine, Germany; Department of Neurology, RWTH Aachen University, Germany
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Dimitriadis S, Laskaris N, Simos P, Micheloyannis S, Fletcher J, Rezaie R, Papanicolaou A. Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG. Neuroimage 2013; 83:307-17. [DOI: 10.1016/j.neuroimage.2013.06.036] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Revised: 05/01/2013] [Accepted: 06/08/2013] [Indexed: 01/25/2023] Open
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Breuer L, Axer M, Dammers J. A new constrained ICA approach for optimal signal decomposition in polarized light imaging. J Neurosci Methods 2013; 220:30-8. [DOI: 10.1016/j.jneumeth.2013.08.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 08/19/2013] [Accepted: 08/22/2013] [Indexed: 11/27/2022]
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Braeutigam S. Magnetoencephalography: fundamentals and established and emerging clinical applications in radiology. ISRN RADIOLOGY 2013; 2013:529463. [PMID: 24967282 PMCID: PMC4045536 DOI: 10.5402/2013/529463] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Accepted: 07/03/2013] [Indexed: 12/17/2022]
Abstract
Magnetoencephalography is a noninvasive, fast, and patient friendly technique for recording brain activity. It is increasingly available and is regarded as one of the most modern imaging tools available to radiologists. The dominant clinical use of this technology currently centers on two, partly overlapping areas, namely, localizing the regions from which epileptic seizures originate, and identifying regions of normal brain function in patients preparing to undergo brain surgery. As a consequence, many radiologists may not yet be familiar with this technique. This review provides an introduction to magnetoencephalography, discusses relevant analytical techniques, and presents recent developments in established and emerging clinical applications such as pervasive developmental disorders. Although the role of magnetoencephalography in diagnosis, prognosis, and patient treatment is still limited, it is argued that this technology is exquisitely capable of contributing indispensable information about brain dynamics not easily obtained with other modalities. This, it is believed, will make this technology an important clinical tool for a wide range of disorders in the future.
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Affiliation(s)
- Sven Braeutigam
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, UK
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Tal I, Abeles M. Cleaning MEG artifacts using external cues. J Neurosci Methods 2013; 217:31-8. [PMID: 23583420 DOI: 10.1016/j.jneumeth.2013.04.002] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2012] [Revised: 04/02/2013] [Accepted: 04/04/2013] [Indexed: 11/19/2022]
Abstract
Using EEG, ECoG, MEG, and microelectrodes to record brain activity is prone to multiple artifacts. The main power line (mains line), video equipment, mechanical vibrations and activities outside the brain are the most common sources of artifacts. MEG amplitudes are low, and even small artifacts distort recordings. In this study, we show how these artifacts can be efficiently removed by recording external cues during MEG recordings. These external cues are subsequently used to register the precise times or spectra of the artifacts. The results indicate that these procedures preserve both the spectra and the time domain wave-shapes of the neuromagnetic signal, while successfully reducing the contribution of the artifacts to the target signals without reducing the rank of the data.
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Affiliation(s)
- I Tal
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Israel
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Hoffmann S, Labrenz F, Themann M, Wascher E, Beste C. Crosslinking EEG time-frequency decomposition and fMRI in error monitoring. Brain Struct Funct 2013; 219:595-605. [PMID: 23443964 DOI: 10.1007/s00429-013-0521-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 02/08/2013] [Indexed: 11/27/2022]
Abstract
Recent studies implicate a common response monitoring system, being active during erroneous and correct responses. Converging evidence from time-frequency decompositions of the response-related ERP revealed that evoked theta activity at fronto-central electrode positions differentiates correct from erroneous responses in simple tasks, but also in more complex tasks. However, up to now it is unclear how different electrophysiological parameters of error processing, especially at the level of neural oscillations are related, or predictive for BOLD signal changes reflecting error processing at a functional-neuroanatomical level. The present study aims to provide crosslinks between time domain information, time-frequency information, MRI BOLD signal and behavioral parameters in a task examining error monitoring due to mistakes in a mental rotation task. The results show that BOLD signal changes reflecting error processing on a functional-neuroanatomical level are best predicted by evoked oscillations in the theta frequency band. Although the fMRI results in this study account for an involvement of the anterior cingulate cortex, middle frontal gyrus, and the Insula in error processing, the correlation of evoked oscillations and BOLD signal was restricted to a coupling of evoked theta and anterior cingulate cortex BOLD activity. The current results indicate that although there is a distributed functional-neuroanatomical network mediating error processing, only distinct parts of this network seem to modulate electrophysiological properties of error monitoring.
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Affiliation(s)
- Sven Hoffmann
- Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 64, 44139, Dortmund, Germany,
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Romo Vázquez R, Vélez-Pérez H, Ranta R, Louis Dorr V, Maquin D, Maillard L. Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling. Biomed Signal Process Control 2012. [DOI: 10.1016/j.bspc.2011.06.005] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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