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Li J, Wang L, Zhang Z, Feng Y, Huang M, Liang D. Analysis and recognition of a novel experimental paradigm for musical emotion brain-computer interface. Brain Res 2024; 1839:149039. [PMID: 38815645 DOI: 10.1016/j.brainres.2024.149039] [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: 12/18/2023] [Revised: 05/17/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024]
Abstract
Musical emotions have received increasing attention over the years. To better recognize the emotions by brain-computer interface (BCI), the random music-playing and sequential music-playing experimental paradigms are proposed and compared in this paper. Two experimental paradigms consist of three positive pieces, three neutral pieces and three negative pieces of music. Ten subjects participate in two experimental paradigms. The features of electroencephalography (EEG) signals are firstly analyzed in the time, frequency and spatial domains. To improve the effect of emotion recognition, a recognition model is proposed with the optimal channels selecting by Pearson's correlation coefficient, and the feature fusion combining differential entropy and wavelet packet energy. According to the analysis results, the features of sequential music-playing experimental paradigm are more different among three emotions. The classification results of sequential music-playing experimental paradigm are also better, and its average results of positive, neutral and negative emotions are 78.53%, 72.81% and 77.35%, respectively. The more obvious the changes of EEG induced by the emotions, the higher the classification accuracy will be. After analyzing two experimental paradigms, a better way for music to induce the emotions can be explored. Therefore, our research offers a novel perspective on affective BCIs.
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Affiliation(s)
- Jin Li
- School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
| | - Li Wang
- School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China.
| | - Zhun Zhang
- School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yujie Feng
- School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
| | - Mingyang Huang
- School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
| | - Danni Liang
- School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
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2
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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3
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Weiler R, Diachenko M, Juarez-Martinez EL, Avramiea AE, Bloem P, Linkenkaer-Hansen K. Robin's Viewer: Using deep-learning predictions to assist EEG annotation. Front Neuroinform 2023; 16:1025847. [PMID: 36844437 PMCID: PMC9951202 DOI: 10.3389/fninf.2022.1025847] [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: 08/23/2022] [Accepted: 12/20/2022] [Indexed: 02/12/2023] Open
Abstract
Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models' output and re-evaluate potential false predictions. As a first step toward addressing these challenges, we developed Robin's Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file formats to facilitate easy integration with other EEG toolboxes. RV includes common features of other EEG viewers, e.g., a view-slider, tools for marking bad channels and transient artifacts, and customizable preprocessing. Altogether, RV is an EEG viewer that combines the predictive power of deep-learning models and the knowledge of scientists and clinicians to optimize EEG annotation. With the training of new deep-learning models, RV could be developed to detect clinical patterns other than artifacts, for example sleep stages and EEG abnormalities.
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Affiliation(s)
- Robin Weiler
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Marina Diachenko
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Erika L. Juarez-Martinez
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Arthur-Ervin Avramiea
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Peter Bloem
- Department of Computer Science, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands,*Correspondence: Klaus Linkenkaer-Hansen,
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Fabietti M, Mahmud M, Lotfi A, Kaiser MS. ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals. Brain Inform 2022; 9:19. [PMID: 36048345 PMCID: PMC9437165 DOI: 10.1186/s40708-022-00167-3] [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: 04/25/2022] [Accepted: 07/22/2022] [Indexed: 11/10/2022] Open
Abstract
Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.
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Affiliation(s)
- Marcos Fabietti
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK. .,Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK. .,Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
| | - Ahmad Lotfi
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - M Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Dhaka, 1342, Savar, Bangladesh
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Abstract
There are various obstacles in the way of use of EEG. Among these, the major obstacles are the artifacts. While some artifacts are avoidable, due to the nature of the EEG techniques there are inevitable artifacts as well. Artifacts can be categorized as internal/physiological or external/non-physiological. The most common internal artifacts are ocular or muscular origins. Internal artifacts are difficult to detect and remove, because they contain signal information as well. For both resting state EEG and ERP studies, artifact handling needs to be carefully carried out in order to retain the maximal signal. Therefore, an effective management of these inevitable artifacts is critical for the EEG based researches. Many researchers from various fields studied this challenging phenomenon and came up with some solutions. However, the developed methods are not well known by the real practitioners of EEG as a tool because of their limited knowledge about these engineering approaches. They still use the traditional visual inspection of the EEG. This work aims to inform the researchers working in the field of EEG about the artifacts and artifact management options available in order to increase the awareness of the available tools such as EEG preprocessing pipelines.
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Gao J, Min X, Kang Q, Si H, Zhan H, Manyande A, Tian X, Dong Y, Zheng H, Song J. Effective connectivity in cortical networks during deception: A lie detection study using EEG. IEEE J Biomed Health Inform 2022; 26:3755-3766. [PMID: 35522638 DOI: 10.1109/jbhi.2022.3172994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Previous studies have identified activated regions associated with deceptive tasks and most of them utilized time, frequency, or temporal features to identify deceptive responses. However, when deception behaviors occur, the functional connectivity pattern and the communication between different brain areas remain largely unclear. In this study, we explored the most important information flows between different brain cortices during deception. First, we employed the guilty knowledge test protocol and recorded on 64 electrodes electroencephalogram (EEG) signals from 30 subjects (15 guilty and 15 innocent). EEG source estimation was then performed to compute the cortical activities on the 24 regions of interest (ROIs). Next, effective connectivity was calculated by partial directed coherence (PDC) analysis applied to the cortical signals. Furthermore, based on the graph-theoretical analysis, the network parameters with significant differences were extracted as features to identify two groups of subjects. In addition, the ROIs frequently involved in the above network parameters were selected, and based on the difference in the group mean of PDC values of all the edges connected with the selected ROIs, we presented the strongest information flows (MIIF) in the guilty group relative to the innocent group. Experimental results first show that the optimal classification features are mainly in-degree and out-degree measures of the ROI and the high classification accuracy for four bands demonstrated that the proposed method is suitable for lie detection. In addition, the frontoparietal network was found to be most prominent among all the MIIFs in four bands. Finally, combining the neurophysiology signification of four frequency bands, respectively, we analyzed the roles of all the important information flows to uncover the underlying cognitive processes and mechanisms used in deception.
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Prasad DS, Chanamallu SR, Prasad KS. Optimized deformable convolution network for detection and mitigation of ocular artifacts from EEG signal. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:30841-30879. [PMID: 35431612 PMCID: PMC8989407 DOI: 10.1007/s11042-022-12874-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 02/08/2022] [Accepted: 03/10/2022] [Indexed: 06/14/2023]
Abstract
Electroencephalogram (EEG) is the key component in the field of analyzing brain activity and behavior. EEG signals are affected by artifacts in the recorded electrical activity; thereby it affects the analysis of EGG. To extract the clean data from EEG signals and to improve the efficiency of detection during encephalogram recordings, a developed model is required. Although various methods have been proposed for the artifacts removal process, sill the research on this process continues. Even if, several types of artifacts from both the subject and equipment interferences are highly contaminated the EEG signals, the most common and important type of interferences is known as Ocular artifacts. Many applications like Brain-Computer Interface (BCI) need online and real-time processing of EEG signals. Hence, it is best if the removal of artifacts is performed in an online fashion. The main intention of this proposal is to accomplish the new deep learning-based ocular artifacts detection and prevention model. In the detection phase, the 5-level Discrete Wavelet Transform (DWT), and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the Principle Component Analysis (PCA) and Independent Component Analysis (ICA) are adopted as the techniques for extracting the features. With the collected features, the development of optimized Deformable Convolutional Networks (DCN) is used for the detection of ocular artifacts from the input EEG signal. Here, the optimized DCN is developed by optimizing or tuning some significant parameters by Distance Sorted-Electric Fish Optimization (DS-EFO). If the artifacts are detected, the mitigation process is performed by applying the Empirical Mean Curve Decomposition (EMCD), and then, the optimized DCN is used for denoising the signals. Finally, the clean signal is generated by applying inverse EMCD. Based on the EEG data collected from diverse subjects, the proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular-artifact reduction by the proposed method.
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Affiliation(s)
| | | | - Kodati Satya Prasad
- Department of ECE, JNTUK, University College of Engineering, Kakinada, AP India
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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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Chiu NT, Huwiler S, Ferster ML, Karlen W, Wu HT, Lustenberger C. Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Bisht A, Singh P, Kaur C, Agarwal S, Ajmani M. Progress and Challenges in Physiological Artifacts' Detection in Electroencephalographic Readings. Curr Med Imaging 2021; 18:509-531. [PMID: 34503420 DOI: 10.2174/1573405617666210908124704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/04/2021] [Accepted: 06/08/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Electroencephalographic (EEG) recordings are used to trace neural activity within the cortex to study brain functioning over time. INTRODUCTION During data acquisition, the unequivocal way to reduce artifact is to avoid artifact stimulating events. Though there are certain artifacts that make this task challenging due to their association with the internal human mechanism, in the human-computer interface, these physiological artifacts are of great assistance and act as a command signal for controlling a device or an application (communication). That is why pre-processing of electroencephalographic readings has been a progressive area of exploration, as none of the published work can be viewed as a benchmark for constructive artifact handling. METHOD This review offers a comprehensive insight into state of the art physiological artifact removal techniques listed so far. The study commences from the single-stage traditional techniques to the multistage techniques, examining the pros and cons of each discussed technique. Also, this review paper gives a general idea of various datasets available and briefs the topical trend in EEG signal processing. RESULT Comparing the state of the art techniques with hybrid ones on the basis of performance and computational complexity, it has been observed that the single-channel techniques save computational time but lack in effective artifact removal especially physiological artifacts. On the other hand, hybrid techniques merge the essential characteristics resulting in increased performance, but time consumption and complexity remain an issue. CONCLUSION Considering the high probability of the presence of multiple artifacts in EEG channels, a trade-off between performance, time and computational complexity is the only key for effective processing of artifacts in the time ahead. This paper is anticipated to facilitate upcoming researchers in enriching the contemporary artifact handling techniques to mitigate the expert's burden.
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Affiliation(s)
- Amandeep Bisht
- Department of Electronics and Communications, UIET, Sector 25, Panjab University, Chandigarh-160014. India
| | - Preeti Singh
- Department of Electronics and Communications, UIET, Sector 25, Panjab University, Chandigarh-160014. India
| | - Chamandeep Kaur
- Department of Electronics and Communications, UIET, Sector 25, Panjab University, Chandigarh-160014. India
| | - Sunil Agarwal
- Department of Electronics and Communications, UIET, Sector 25, Panjab University, Chandigarh-160014. India
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11
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Gao J, Gu L, Min X, Lin P, Li C, Zhang Q, Rao N. Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Deception Using EEG Phase Synchrony Analysis. IEEE J Biomed Health Inform 2021; 26:600-613. [PMID: 34232900 DOI: 10.1109/jbhi.2021.3095415] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG are mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.
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Ranjan R, Chandra Sahana B, Kumar Bhandari A. Ocular artifact elimination from electroencephalography signals: A systematic review. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.06.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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13
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Ranta R, Le Cam S, Chaudet B, Tyvaert L, Maillard L, Colnat-Coulbois S, Louis-Dorr V. Approximate Canonical Correlation Analysis for common/specific subspace decompositions. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Ferdous J, Ali S, Hamid E, Molla KI. Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform. INT J ADV ROBOT SYST 2021. [DOI: 10.1177/1729881421992269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.
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Affiliation(s)
- Jannatul Ferdous
- Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal-2224, Mymensingh, Bangladesh
| | - Sujan Ali
- Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal-2224, Mymensingh, Bangladesh
| | - Ekramul Hamid
- Department of Computer Science and Engineering, Signal Processing and Computational Neuroscience Laboratory, University of Rajshahi, Rajshahi, Bangladesh
| | - Khademul Islam Molla
- Department of Computer Science and Engineering, Signal Processing and Computational Neuroscience Laboratory, University of Rajshahi, Rajshahi, Bangladesh
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Yao Y, Plested J, Gedeon T. Information-preserving feature filter for short-term EEG signals. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.11.106] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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16
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Deolindo CS, Ribeiro MW, Aratanha MA, Afonso RF, Irrmischer M, Kozasa EH. A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram. Front Syst Neurosci 2020; 14:53. [PMID: 32848645 PMCID: PMC7427581 DOI: 10.3389/fnsys.2020.00053] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 07/06/2020] [Indexed: 11/13/2022] Open
Abstract
Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.
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Affiliation(s)
| | | | | | | | - Mona Irrmischer
- Department of Integrative Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU Amsterdam, Amsterdam, Netherlands
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18
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Abstract
This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performance on some specific activity classes. Through class-specific weighted majority voting, the recognition accuracy of 10 PA types has been improved from 86% to 92% compared with the non-combination approach. Furthermore, the combination method has shown to be effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition and has better performance in monitoring physical activities of varying intensities than traditional homogeneous classifiers.
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A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition. SENSORS 2019; 19:s19071631. [PMID: 30959760 PMCID: PMC6479375 DOI: 10.3390/s19071631] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 03/13/2019] [Accepted: 04/03/2019] [Indexed: 01/05/2023]
Abstract
Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.
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Haider SK, Jiang A, Jamshed MA, Pervaiz H, Mumtaz S. Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism. ACTA ACUST UNITED AC 2019. [DOI: 10.1109/lnet.2018.2883859] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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21
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22
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Gao J, Song J, Yang Y, Yao S, Guan J, Si H, Zhou H, Ge S, Lin P. Deception Decreases Brain Complexity. IEEE J Biomed Health Inform 2018; 23:164-174. [PMID: 29993592 DOI: 10.1109/jbhi.2018.2842104] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Extensive evidence suggests the feasibility of lie detection using electroencephalograms (EEGs). However, it is largely unknown whether there are any differences in the nonlinear features of EEGs between guilty and innocent subjects. In this study, we proposed a complexity-based method to distinguish lying from truth telling. A total of 35 participants were randomly divided into two groups, and their EEG signals were recorded with 14 electrodes. Averages for sequential sets of five trials were first calculated for the probe responses within each subject. Next, a common wavelet entropy (WE) measure and an improved one were used to quantify complexity from each five-trial average. The results show that for both measures, the WE values in the guilty subjects are statistically lower than those in the innocent subjects for most of the 14 electrodes. More importantly, using the improved measure, the difference in WE between the two groups of subjects significantly increases for 11 brain regions compared with the values from the common measure. Finally, the highest balanced classification accuracy, 89.64%, is achieved when using the combined WE feature vector in five brain regions from the sites of Pz, P3, C4, Cz, and C3. Our findings indicate that the lying task elicits a more ordered brain activity in some specific brain regions than the task of telling the truth. This study not only demonstrates that improved WE measurements could be a powerful quantitative index for detecting lying but also sheds light on the brain mechanisms underlying deceptive behaviors.
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Perera H, Shiratuddin MF, Wong KW. Review of EEG-based pattern classification frameworks for dyslexia. Brain Inform 2018; 5:4. [PMID: 29904812 PMCID: PMC6094381 DOI: 10.1186/s40708-018-0079-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 04/18/2018] [Indexed: 12/04/2022] Open
Abstract
Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia.
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Affiliation(s)
- Harshani Perera
- School of Engineering and Information Technology, Murdoch University, Murdoch, Australia.
| | | | - Kok Wai Wong
- School of Engineering and Information Technology, Murdoch University, Murdoch, Australia
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24
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Abstract
Electroencephalography (EEG) has a long history in neuroscience starting with its original description in humans by Hans Berger in 1929 (Berger, 1932). Investigations of EEG under anesthesia started soon after in the mid-1930s (Gibbs, 1937). No single methodology paper can credibly cover all of the issues relating to this rich field. The purpose of this chapter is to introduce some caveats that complicate and inform analysis of the EEG. Special emphasis will be given to common issues such as choice of reference electrode, filtering, artifact rejection, and spectral analysis. We will specifically emphasize high-density EEG recordings that have become the norm due to technological improvement in electrode and data acquisition design methods. In the last section we will discuss some applications of EEG analysis techniques to the study of the effects of anesthetics on the nervous system.
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Affiliation(s)
- Alex Proekt
- Perelman School of Medicine, Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, United States.
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25
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Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:5081258. [PMID: 29599950 PMCID: PMC5823426 DOI: 10.1155/2018/5081258] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 10/05/2017] [Accepted: 11/08/2017] [Indexed: 11/17/2022]
Abstract
Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
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26
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Online denoising of eye-blinks in electroencephalography. Neurophysiol Clin 2017; 47:371-391. [DOI: 10.1016/j.neucli.2017.10.059] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 10/12/2017] [Accepted: 10/12/2017] [Indexed: 11/18/2022] Open
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27
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Barua S, Ahmed MU, Ahlstrom C, Begum S, Funk P. Automated EEG Artifact Handling With Application in Driver Monitoring. IEEE J Biomed Health Inform 2017; 22:1350-1361. [PMID: 29990112 DOI: 10.1109/jbhi.2017.2773999] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments are becoming increasingly important in areas such as brain-computer interfaces and behavior science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm, which will be used as a preprocessing step in a driver monitoring application. The algorithm, named Automated aRTifacts handling in EEG (ARTE), is based on wavelets, independent component analysis, and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-min 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error, and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state-of-the-art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable, and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a preprocessing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
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28
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Dhindsa K. Filter-Bank Artifact Rejection: High performance real-time single-channel artifact detection for EEG. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.06.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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29
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DelPozo-Baños M, Weidemann CT. Localized component filtering for electroencephalogram artifact rejection. Psychophysiology 2017; 54:608-619. [PMID: 28112387 DOI: 10.1111/psyp.12810] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 11/10/2016] [Indexed: 11/30/2022]
Abstract
Blind source separation (BSS) based artifact rejection systems have been extensively studied in the electroencephalogram (EEG) literature. Although there have been advances in the development of techniques capable of dissociating neural and artifactual activity, these are still not perfect. As a result, a compromise between reduction of noise and leakage of neural activity has to be found. Here, we propose a new methodology to enhance the performance of existing BSS systems: Localized component filtering (LCF). In essence, LCF identifies the artifactual time segments within each component extracted by BSS and restricts the processing of components to these segments, therefore reducing neural leakage. We show that LCF can substantially reduce the neural leakage, increasing the true acceptance rate by 22 percentage points while worsening the false acceptance rate by less than 2 percentage points in a dataset consisting of simulated EEG data (4% improvement of the correlation between original and cleaned signals). Evaluated on real EEG data, we observed a significant increase of the signal-to-noise ratio of up to 9%.
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Affiliation(s)
- Marcos DelPozo-Baños
- Department of Psychology, Swansea University, Swansea, Wales, UK.,Swansea University Medical School, Swansea, Wales, UK
| | - Christoph T Weidemann
- Department of Psychology, Swansea University, Swansea, Wales, UK.,Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
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30
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Jafarifarmand A, Badamchizadeh MA, Khanmohammadi S, Nazari MA, Tazehkand BM. Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.08.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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31
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Minguillon J, Lopez-Gordo MA, Pelayo F. Trends in EEG-BCI for daily-life: Requirements for artifact removal. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.09.005] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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32
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Islam MK, Rastegarnia A, Yang Z. Methods for artifact detection and removal from scalp EEG: A review. Neurophysiol Clin 2016; 46:287-305. [PMID: 27751622 DOI: 10.1016/j.neucli.2016.07.002] [Citation(s) in RCA: 147] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 05/29/2016] [Accepted: 07/07/2016] [Indexed: 11/29/2022] Open
Abstract
Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.
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Affiliation(s)
- Md Kafiul Islam
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Amir Rastegarnia
- Department of Electrical Engineering, University of Malayer, Malayer, Iran.
| | - Zhi Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
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33
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Stevenson NJ, O'Toole JM, Korotchikova I, Boylan GB. Artefact detection in neonatal EEG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2014:926-9. [PMID: 25570111 DOI: 10.1109/embc.2014.6943743] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Artefact detection is an important component of any automated EEG analysis. It is of particular importance in analyses such as sleep state detection and EEG grading where there is no null state. We propose a general artefact detection system (GADS) based on the analysis of the neonatal EEG. This system aims to detect both major and minor artefacts (a distinction based primarily on amplitude). As a result, a two-stage system was constructed based on 14 features extracted from EEG epochs at multiple time scales: [2, 4, 16, 32]s. These features were combined in a support vector machine (SVM) in order to determine the presence of absence of artefact. The performance of the GADS was estimated using a leave-one-out cross-validation applied to a database of hour long recordings from 51 neonates. The median AUC was 1.00 (IQR: 0.95-1.00) for the detection of major artefacts and 0.89 (IQR: 0.83-0.95) for the detection of minor artefacts.
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34
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Islam MK, Rastegarnia A, Yang Z. A Wavelet-Based Artifact Reduction From Scalp EEG for Epileptic Seizure Detection. IEEE J Biomed Health Inform 2016; 20:1321-32. [DOI: 10.1109/jbhi.2015.2457093] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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35
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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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36
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Rogers JM, Johnstone SJ, Aminov A, Donnelly J, Wilson PH. Test-retest reliability of a single-channel, wireless EEG system. Int J Psychophysiol 2016; 106:87-96. [DOI: 10.1016/j.ijpsycho.2016.06.006] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Revised: 06/14/2016] [Accepted: 06/15/2016] [Indexed: 11/28/2022]
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37
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Longitudinal study of preterm and full-term infants: High-density EEG analyses of cortical activity in response to visual motion. Neuropsychologia 2016; 84:89-104. [DOI: 10.1016/j.neuropsychologia.2016.02.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2015] [Revised: 01/14/2016] [Accepted: 02/03/2016] [Indexed: 11/21/2022]
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38
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Chang WD, Lim JH, Im CH. An unsupervised eye blink artifact detection method for real-time electroencephalogram processing. Physiol Meas 2016; 37:401-17. [PMID: 26888113 DOI: 10.1088/0967-3334/37/3/401] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Electroencephalogram (EEG) is easily contaminated by unwanted physiological artifacts, among which electrooculogram (EOG) artifacts due to eye blinking are known to be most dominant. The eye blink artifacts are reported to affect theta and alpha rhythms of frontal EEG signals, and hard to be accurately detected in an unsupervised way due to large individual variability. In this study, we propose a new method for detecting eye blink artifacts automatically in real time without using any labeled training data. The proposed method combined our previous method for detecting eye blink artifacts based on digital filters with an automatic thresholding algorithm. The proposed method was evaluated using EEG data acquired from 24 participants. Two conventional algorithms were implemented and their performances were compared with that of the proposed method. The main contributions of this study are (1) confirming that individual thresholding is necessary for artifact detection, (2) proposing a novel algorithm structure to detect blink artifacts in a real-time environment without any a priori knowledge, and (3) demonstrating that the length of training data can be minimized through the use of a real-time adaption procedure.
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Affiliation(s)
- Won-Du Chang
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
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39
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Chang WD, Cha HS, Kim K, Im CH. Detection of eye blink artifacts from single prefrontal channel electroencephalogram. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:19-30. [PMID: 26560852 DOI: 10.1016/j.cmpb.2015.10.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 09/24/2015] [Accepted: 10/14/2015] [Indexed: 06/05/2023]
Abstract
Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain-computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.
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Affiliation(s)
- Won-Du Chang
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Ho-Seung Cha
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Kiwoong Kim
- Korea Research Institute of Standard and Science (KRISS), Daejeon, Republic of Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea.
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40
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Rakibul Mowla M, Ng SC, Zilany MS, Paramesran R. Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.06.009] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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41
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Duan F, Phothisonothai M, Kikuchi M, Yoshimura Y, Minabe Y, Watanabe K, Aihara K. Boosting specificity of MEG artifact removal by weighted support vector machine. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:6039-42. [PMID: 24111116 DOI: 10.1109/embc.2013.6610929] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
An automatic artifact removal method of magnetoencephalogram (MEG) was presented in this paper. The method proposed is based on independent components analysis (ICA) and support vector machine (SVM). However, different from the previous studies, in this paper we consider two factors which would influence the performance. First, the imbalance factor of independent components (ICs) of MEG is handled by weighted SVM. Second, instead of simply setting a fixed weight to each class, a re-weighting scheme is used for the preservation of useful MEG ICs. Experimental results on manually marked MEG dataset showed that the method proposed could correctly distinguish the artifacts from the MEG ICs. Meanwhile, 99.72% ± 0.67 of MEG ICs were preserved. The classification accuracy was 97.91% ± 1.39. In addition, it was found that this method was not sensitive to individual differences. The cross validation (leave-one-subject-out) results showed an averaged accuracy of 97.41% ± 2.14.
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42
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Gordon SM, Lawhern V, Passaro AD, McDowell K. Informed decomposition of electroencephalographic data. J Neurosci Methods 2015; 256:41-55. [PMID: 26306657 DOI: 10.1016/j.jneumeth.2015.08.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Revised: 08/15/2015] [Accepted: 08/18/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND Blind source separation techniques have become the de facto standard for decomposing electroencephalographic (EEG) data. These methods are poorly suited for incorporating prior information into the decomposition process. While alternative techniques to this problem, such as the use of constrained optimization techniques, have been proposed, these alternative techniques tend to only minimally satisfy the prior constraints. In addition, the experimenter must preset a number of parameters describing both this minimal limit as well as the size of the target subspaces. NEW METHOD We propose an informed decomposition approach that builds upon the constrained optimization approaches for independent components analysis to better model and separate distinct subspaces within EEG data. We use a likelihood function to adaptively determine the optimal model size for each target subspace. RESULTS Using our method we are able to produce ordered independent subspaces that exhibit less residual mixing than those obtained with other methods. The results show an improvement in modeling specific features of the EEG space, while also showing a simultaneous reduction in the number of components needed for each model. COMPARISON WITH EXISTING METHOD(S) We first compare our approach to common methods in the field of EEG decomposition, such as Infomax, FastICA, PCA, JADE, and SOBI for the task of modeling and removing both EOG and EMG artifacts. We then demonstrate the utility of our approach for the more complex problem of modeling neural activity. CONCLUSIONS By working in a one-size-fits-all fashion current EEG decomposition methods do not adapt to the specifics of each data set and are not well designed to incorporate additional information about the decomposition problem. However, by adding specific information about the problem to the decomposition task, we improve the identification and separation of distinct subspaces within the original data and show better preservation of the remaining data.
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Affiliation(s)
- S M Gordon
- DCS Corporation, Alexandria, VA 22310, USA.
| | - V Lawhern
- Human Research and Engineering Directorate, US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | | | - K McDowell
- Human Research and Engineering Directorate, US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
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43
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Mateo J, Torres AM, García MA, Santos JL. Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-1988-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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44
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Mateo J, Torres AM, Sanchez-Morla EM, Santos JL. Eye Movement Artefact Suppression Using Volterra Filter for Electroencephalography Signals. J Med Biol Eng 2015. [DOI: 10.1007/s40846-015-0036-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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45
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Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.09.040] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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46
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Ventouras EC, Margariti A, Chondraki P, Kalatzis I, Economou NT, Tsekou H, Paparrigopoulos T, Ktonas P. EEG-based investigation of brain connectivity changes in psychotic patients undergoing the primitive expression form of dance therapy: a methodological pilot study. Cogn Neurodyn 2014; 9:231-48. [PMID: 25852781 DOI: 10.1007/s11571-014-9319-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2014] [Revised: 10/22/2014] [Accepted: 11/05/2014] [Indexed: 11/27/2022] Open
Abstract
Primitive expression (PE) is a form of dance therapy (DT) that involves an interaction of ethologically and socially based forms which are supplied for re-enactment. There exist very few studies of DT applications including in their protocol the measurement of neurophysiological parameters. The present pilot study investigates the use of the correlation coefficient (ρ) and mutual information (MI), and of novel measures extracted from ρ and MI, on electroencephalographic (EEG) data recorded in patients with schizophrenia while they undergo PE DT, in order to expand the set of neurophysiology-based approaches for quantifying possible DT effects, using parameters that might provide insights about any potential brain connectivity changes in these patients during the PE DT process. Indication is provided for an acute potentiation effect, apparent at late-stage PE DT, on the inter-hemispheric connectivity in frontal areas, as well as for attenuation of the inter-hemispheric connectivity of left frontal and right central areas and for potentiation of the intra-hemispheric connectivity of frontal and central areas, bilaterally, in the transition from early to late-stage PE DT. This pilot study indicates that by using EEG connectivity measures based on ρ and MI, the set of useful neurophysiology-based approaches for quantifying possible DT effects is expanded. In the framework of the present study, the causes of the observed connectivity changes cannot be attributed with certainty to PE DT, but indications are provided that these measures may contribute to a detailed assessment of neurophysiological mechanisms possibly being affected by this therapeutic process.
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Affiliation(s)
- Errikos-Chaim Ventouras
- Department of Biomedical Engineering, Technological Educational Institution of Athens, Agiou Spyridonos Str., Egaleo, Athens, 12210 Greece
| | - Alexia Margariti
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece ; Department of Theater Studies, University of Peloponnese, 21, Vas. Konstantinou Str., Nafplion, 21460 Greece
| | - Paraskevi Chondraki
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Ioannis Kalatzis
- Department of Biomedical Engineering, Technological Educational Institution of Athens, Agiou Spyridonos Str., Egaleo, Athens, 12210 Greece
| | - Nicholas-Tiberio Economou
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Hara Tsekou
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Thomas Paparrigopoulos
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Periklis Ktonas
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
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47
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Gao J, Tian H, Yang Y, Yu X, Li C, Rao N. A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM. PLoS One 2014; 9:e109700. [PMID: 25365325 PMCID: PMC4218862 DOI: 10.1371/journal.pone.0109700] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Accepted: 08/13/2014] [Indexed: 11/19/2022] Open
Abstract
The investigation of lie detection methods based on P300 potentials has drawn much interest in recent years. We presented a novel algorithm to enhance signal-to-noise ratio (SNR) of P300 and applied it in lie detection to increase the classification accuracy. Thirty-four subjects were divided randomly into guilty and innocent groups, and the EEG signals on 14 electrodes were recorded. A novel spatial denoising algorithm (SDA) was proposed to reconstruct the P300 with a high SNR based on independent component analysis. The differences between the proposed method and our/other early published methods mainly lie in the extraction and feature selection method of P300. Three groups of features were extracted from the denoised waves; then, the optimal features were selected by the F-score method. Selected feature samples were finally fed into three classical classifiers to make a performance comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine (SVM) approach was adopted to combine with an F-score because this approach had the best performance. The presented model F-score_SVM reaches a significantly higher classification accuracy for P300 (specificity of 96.05%) and non-P300 (sensitivity of 96.11%) compared with the results obtained without using SDA and compared with the results obtained by other classification models. Moreover, a higher individual diagnosis rate can be obtained compared with previous methods, and the presented method requires only a small number of stimuli in the real testing application.
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Affiliation(s)
- Junfeng Gao
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Hongjun Tian
- Nanjing Fullshare Superconducting Technology Co., Ltd., Nanjing, People's Republic of China
| | - Yong Yang
- School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, People's Republic of China
| | - Xiaolin Yu
- Department of Information Engineering, Officers College of CAPF, People's Republic of China
| | - Chenhong Li
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China
| | - Nini Rao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
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48
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A preliminary study of muscular artifact cancellation in single-channel EEG. SENSORS 2014; 14:18370-89. [PMID: 25275348 PMCID: PMC4239950 DOI: 10.3390/s141018370] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2014] [Revised: 08/19/2014] [Accepted: 09/23/2014] [Indexed: 11/17/2022]
Abstract
Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.
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49
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Qinglin Zhao, Bin Hu, Yujun Shi, Yang Li, Moore P, Minghou Sun, Hong Peng. Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications. IEEE Trans Nanobioscience 2014; 13:109-17. [DOI: 10.1109/tnb.2014.2316811] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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50
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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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