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Schmierer T, Li T, Li Y. Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment. Artif Intell Med 2024; 151:102869. [PMID: 38593683 DOI: 10.1016/j.artmed.2024.102869] [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: 09/28/2023] [Revised: 01/31/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024]
Abstract
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.
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Affiliation(s)
- Thomas Schmierer
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Tianning Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
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2
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Akuthota S, K R, Ravichander J. Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN. Heliyon 2024; 10:e27198. [PMID: 38560190 PMCID: PMC10980936 DOI: 10.1016/j.heliyon.2024.e27198] [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: 01/25/2024] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.
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Affiliation(s)
- Srinath Akuthota
- Department of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, India
| | - RajKumar K
- Department of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, India
| | - Janapati Ravichander
- Department of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, India
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Zhang R, Rong R, Gan JQ, Xu Y, Wang H, Wang X. Reliable and fast automatic artifact rejection of Long-Term EEG recordings based on Isolation Forest. Med Biol Eng Comput 2024; 62:521-535. [PMID: 37943419 DOI: 10.1007/s11517-023-02961-5] [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] [Received: 05/27/2023] [Accepted: 10/28/2023] [Indexed: 11/10/2023]
Abstract
Long-term electroencephalogram (Long-Term EEG) has the capacity to monitor over a long period, making it a valuable tool in medical institutions. However, due to the large volume of patient data, selecting clean data segments from raw Long-Term EEG for further analysis is an extremely time-consuming and labor-intensive task. Furthermore, the various actions of patients during recording make it difficult to use algorithms to denoise part of the EEG data, and thus lead to the rejection of these data. Therefore, tools for the quick rejection of heavily corrupted epochs in Long-Term EEG records are highly beneficial. In this paper, a new reliable and fast automatic artifact rejection method for Long-Term EEG based on Isolation Forest (IF) is proposed. Specifically, the IF algorithm is repetitively applied to detect outliers in the EEG data, and the boundary of inliers is promptly adjusted by using a statistical indicator to make the algorithm proceed in an iterative manner. The iteration is terminated when the distance metric between clean epochs and artifact-corrupted epochs remains unchanged. Six statistical indicators (i.e., min, max, median, mean, kurtosis, and skewness) are evaluated by setting them as centroid to adjust the boundary during iteration, and the proposed method is compared with several state-of-the-art methods on a retrospectively collected dataset. The experimental results indicate that utilizing the min value of data as the centroid yields the most optimal performance, and the proposed method is highly efficacious and reliable in the automatic artifact rejection of Long-Term EEG, as it significantly improves the overall data quality. Furthermore, the proposed method surpasses compared methods on most data segments with poor data quality, demonstrating its superior capacity to enhance the data quality of the heavily corrupted data. Besides, owing to the linear time complexity of IF, the proposed method is much faster than other methods, thus providing an advantage when dealing with extensive datasets.
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Affiliation(s)
- Runkai Zhang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, Jiangsu, People's Republic of China
| | - Rong Rong
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing, 210008, Jiangsu, People's Republic of China
| | - John Q Gan
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing, 210008, Jiangsu, People's Republic of China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, Jiangsu, People's Republic of China.
| | - Xiaoyun Wang
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing, 210008, Jiangsu, People's Republic of China.
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Park D, Park H, Kim S, Choo S, Lee S, Nam CS, Jung JY. Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4504-4513. [PMID: 37934650 DOI: 10.1109/tnsre.2023.3330922] [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: 11/09/2023]
Abstract
Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed, it is still challenging to interpret the CNN models for EEG-based BCI classification effectively. In this research, we propose 3D-EEGNet as a 3D CNN model to improve both the explainability and performance of MI EEG classification. The proposed approach exhibited better performances on two MI EEG datasets than the existing EEGNet, which uses a 2D input shape. The MI classification accuracies are improved around 1.8% and 6.1% point in average on the datasets, respectively. The permutation-based XAI method is first applied for the reliable explanation of the 3D-EEGNet. Next, to find a faster XAI method for spatio-temporal explanation, we design a novel technique based on the normalized discounted cumulative gain (NDCG) for selecting the best among a few saliency-based methods due to their higher time complexity than the permutation-based method. Among the saliency-based methods, DeepLIFT was selected because the NDCG scores indicated its results are the most similar to the permutation-based results. Finally, the fast spatio-temporal explanation using DeepLIFT provides deeper understanding for the classification results of the 3D-EEGNet and the important properties in the MI EEG experiments.
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Aderinwale A, Tolossa GB, Kim AY, Jang EH, Lee YI, Jeon HJ, Kim H, Yu HY, Jeong J. Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods. Psychiatry Res Neuroimaging 2023; 332:111641. [PMID: 37054495 DOI: 10.1016/j.pscychresns.2023.111641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/27/2023] [Accepted: 04/02/2023] [Indexed: 04/15/2023]
Abstract
The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.
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Affiliation(s)
- Adedoyin Aderinwale
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea
| | - Gemechu Bekele Tolossa
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Department of Neuroscience, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Ah Young Kim
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea
| | - Eun Hye Jang
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea
| | - Yong-Il Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hyewon Kim
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Han Young Yu
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea.
| | - Jaeseung Jeong
- Department of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon 34141, South Korea.
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Fu R, Li Z, Wang S, Xu D, Huang X, Liang H. EEG-based driver states discrimination by noise fraction analysis and novel clustering algorithm. BIOMED ENG-BIOMED TE 2023:bmt-2022-0395. [PMID: 36848391 DOI: 10.1515/bmt-2022-0395] [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: 06/20/2022] [Accepted: 02/10/2023] [Indexed: 03/01/2023]
Abstract
Driver states are reported as one of the principal factors in driving safety. Distinguishing the driving driver state based on the artifact-free electroencephalogram (EEG) signal is an effective means, but redundant information and noise will inevitably reduce the signal-to-noise ratio of the EEG signal. This study proposes a method to automatically remove electrooculography (EOG) artifacts by noise fraction analysis. Specifically, multi-channel EEG recordings are collected after the driver experiences a long time driving and after a certain period of rest respectively. Noise fraction analysis is then applied to remove EOG artifacts by separating the multichannel EEG into components by optimizing the signal-to-noise quotient. The representation of data characteristics of the EEG after denoising is found in the Fisher ratio space. Additionally, a novel clustering algorithm is designed to identify denoising EEG by combining cluster ensemble and probability mixture model (CEPM). The EEG mapping plot is used to illustrate the effectiveness and efficiency of noise fraction analysis on the denoising of EEG signals. Adjusted rand index (ARI) and accuracy (ACC) are used to demonstrate clustering performance and precision. The results showed that the noise artifacts in the EEG were removed and the clustering accuracy of all participants was above 90%, resulting in a high driver fatigue recognition rate.
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Affiliation(s)
- Rongrong Fu
- Department of Electrical Engineering, Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
| | - Zheyu Li
- Department of Electrical Engineering, Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
| | - Shiwei Wang
- Jiangxi New Energy Technology Institute, Xinyu, China
| | - Dong Xu
- Department of Electrical Engineering, Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
| | - Xiaodong Huang
- Department of Electrical Engineering, Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
| | - Haifeng Liang
- Department of Electrical Engineering, Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
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Escobar-Ipuz F, Torres A, García-Jiménez M, Basar C, Cascón J, Mateo J. Prediction of patients with idiopathic generalized epilepsy from healthy controls using machine learning from scalp EEG recordings. Brain Res 2022; 1798:148131. [DOI: 10.1016/j.brainres.2022.148131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/14/2022] [Accepted: 10/23/2022] [Indexed: 11/05/2022]
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Lopes F, Leal A, Medeiros J, Pinto MF, Dourado A, Dümpelmann M, Teixeira C. EPIC: Annotated epileptic EEG independent components for artifact reduction. Sci Data 2022; 9:512. [PMID: 35987693 PMCID: PMC9392781 DOI: 10.1038/s41597-022-01524-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain’s electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers. Measurement(s) | Electroencephalogram Independent Components | Technology Type(s) | MATLAB and Python |
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Minimum Technical Requirements for Performing Ambulatory EEG. J Clin Neurophysiol 2022; 39:435-440. [DOI: 10.1097/wnp.0000000000000950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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10
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Ayoobi N, Sadeghian EB. Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4817-4820. [PMID: 36086139 DOI: 10.1109/embc48229.2022.9871906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance. The effectiveness of the suggested method is evaluated on the most widely used BCI algorithm, the common spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The results indicate that the proposed method can effectively prune MI-EEG signals and significantly enhance the performance of the CSP algorithm in terms of classification accuracy.
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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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Zhu H, Wu Y, Shen N, Fan J, Tao L, Fu C, Yu H, Wan F, Pun SH, Chen C, Chen W. The Masking Impact of Intra-artifacts in EEG on Deep Learning-based Sleep Staging Systems: A Comparative Study. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1452-1463. [PMID: 35536800 DOI: 10.1109/tnsre.2022.3173994] [Citation(s) in RCA: 4] [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
Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches. Whether intra-artifacts, originated from the eye movement, chin muscle firing, or heart beating, etc., in EEG signals would lead to a positive or a negative masking effect on deep learning-based sleep staging systems was investigated in this paper. We systematically analyzed several traditional pre-processing methods involving fast Independent Component Analysis (FastICA), Information Maximization (Infomax), and Second-order Blind Source Separation (SOBI). On top of these methods, a SOBI-WT method based on the joint use of the SOBI and Wavelet Transform (WT) is proposed. It offered an effective solution for suppressing artifact components while retaining residual informative data. To provide a comprehensive comparative analysis, these pre-processing methods were applied to eliminate the intra-artifacts and the processed signals were fed to two ready-to-use deep learning models, namely two-step hierarchical neural network (THNN) and SimpleSleepNet for automatic sleep staging. The evaluation was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDF Expanded, and a clinical dataset that was collected in Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed SOBI-WT method increased the accuracy from 79.0% to 81.3% on MASS, 83.3% to 85.7% on Sleep-EDF Expanded, and 75.5% to 77.1% on HSFU compared with the raw EEG signal, respectively. Experimental results demonstrate that the intra-artifacts bring out a masking negative impact on the deep learning-based sleep staging systems and the proposed SOBI-WT method has the best performance in diminishing this negative impact compared with other artifact elimination methods.
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Wang M, Wang J, Cui X, Wang T, Jiang T, Gao F, Cao J. Multi-dimensional Feature Optimization based Eye Blink Detection under Epileptiform Discharges. IEEE Trans Neural Syst Rehabil Eng 2022; 30:905-914. [PMID: 35363618 DOI: 10.1109/tnsre.2022.3164126] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVES Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. METHODS In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multi-dimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. RESULTS Experiments on EEGs of 16 subjects from the Children's Hospital of Zhejiang University School of Medicine (CHZU) show that our method achieves the highest average sensitivity, specificity and accuracy of 95.04, 89.52, and 93.01, respectively. That outperforms 5 recent and state-of-the-art (SOTA) eye blink detection algorithms. SIGNIFICANCE The proposed method is robust in eye blink artifact detection for EEGs containing high-frequency epileptiform discharges. It is also effective in dealing with individual differences in EEGs, which is usually ignored in conventional methods.
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Wan Z, Yang R, Huang M, Liu W, Zeng N. EEG fading data classification based on improved manifold learning with adaptive neighborhood selection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.039] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Zhang W, Yang W, Jiang X, Qin X, Yang J, Du J. Two-Stage Intelligent Multi-Type Artifact Removal for Single-Channel EEG Settings: A GRU Autoencoder based Approach. IEEE Trans Biomed Eng 2022; 69:3142-3154. [PMID: 35324430 DOI: 10.1109/tbme.2022.3161994] [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: 11/08/2022]
Abstract
OBJECTIVE The wearable and portable Electroencephalogram (EEG) sensing systems are deeply interfered by unavoidable physiological artifacts due to the limited recording resources. In this work, an intelligent artifact removal system that handles single-channel EEG signals in the presence of mixed multi-type artifacts is investigated. METHODS The basic idea is to represent the mixed artifacts in contaminated varying EEG signals with the unchanged latent pattern features, and then employ the adaptive artifact removal scheme to separate the contamination and clean EEG signals in the encoded feature domain. To minimize the risks of corrupting clean signals and keeping artifacts by mistake, the artifact removal is formulated as an identification-removal two-stage minimization problem, and an attention based adaptive feature concentration mechanism is designed to improve the removal utility and reduce the calculation consumption. RESULTS In the real implementation on open real-world dataset, this study achieves the artifact identification accuracy of 98.52% and average correlation coefficient of 0.73 for the removal of strong mixed multi-type artifacts. CONCLUSION This study can deal with single-channel EEG signals contaminated by mixed multi-type artifacts with high accuracy and low overhead, and is more effective and stable than traditional schemes with fixed criteria. SIGNIFICANCE This study can significantly improve the signal quality acquired by simplified EEG sensing systems, and may extend the application of wearable and portable EEG sensing systems to medical diagnosis, cognitive science research and other applications requiring clinical setups.
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Lopes F, Leal A, Medeiros J, Pinto MF, Dourado A, Dumpelmann M, Teixeira C. Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components. IEEE Trans Neural Syst Rehabil Eng 2022; 30:559-568. [PMID: 35213313 DOI: 10.1109/tnsre.2022.3154891] [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
OBJECTIVE Independent component analysis (ICA) is commonly used to remove noisy artifacts from multi-channel scalp electroencephalogram (EEG) signals. ICA decomposes EEG into different independent components (ICs) and then, experts remove the noisy ones. This process is highly time-consuming and experts are not always available. To surpass this drawback, research is going on to develop models to automatically conduct IC classification. Current state-of-the-art models use power spectrum densities (PSDs) and topoplots to classify ICs. The performance of these methods may be limited by disregarding the IC time-series that would contribute to fully simulate the visual inspection performed by experts. METHODS We present a novel ensemble deep neural network that combines time-series, PSDs, and topoplots to classify ICs. Moreover, we study the ability to use our model in transfer learning approaches. RESULTS Experimental results showed that using time-series improves IC classification. Results also indicated that transfer learning obtained higher performance than simply training a new model from scratch. CONCLUSION Researchers should develop IC classifiers using the three sources of information. Moreover, transfer learning approaches should be considered when producing new deep learning models. SIGNIFICANCE This work improves IC classification, enhancing the automatic removal of EEG artifacts. Additionally, since labelled ICs are generally not publicly available, the possibility of using our model in transfer learning studies may motivate other researchers to develop their own classifiers.
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Dora M, Holcman D. Adaptive single-channel EEG artifact removal for real-time clinical monitoring. IEEE Trans Neural Syst Rehabil Eng 2022; 30:286-295. [PMID: 35085086 DOI: 10.1109/tnsre.2022.3147072] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Electroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision. METHODS We propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a data-driven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques. RESULTS We assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia. CONCLUSIONS We show that our method can successfully attenuate various types of artifacts in single-channel EEG. SIGNIFICANCE Thanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.
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Maimaiti B, Meng H, Lv Y, Qiu J, Zhu Z, Xie Y, Li Y, Yu-Cheng, Zhao W, Liu J, Li M. An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field. Neuroscience 2021; 481:197-218. [PMID: 34793938 DOI: 10.1016/j.neuroscience.2021.11.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022]
Abstract
The unpredictability of epileptic seizures is one of the most problematic aspects of the field of epilepsy. Methods or devices capable of detecting seizures minutes before they occur may help prevent injury or even death and significantly improve the quality of life. Machine learning (ML) is an emerging technology that can markedly enhance algorithm performance by interpreting data. ML has gained increasing attention from medical researchers in recent years. Its epilepsy applications range from the localization of the epileptic region, predicting the medical or surgical outcome of epilepsy, and automated electroencephalography (EEG) analysis to seizure prediction. While ML has good prospects with regard to detecting epileptic seizures via EEG signals, many clinicians are still unfamiliar with this field. This work briefly summarizes the history and recent significant progress made in this field and clarifies the essential components of the automatic seizure detection system using ML methodologies for clinicians. This review also proposes how neurologists can actively contribute to ensure improvements in seizure prediction using EEG-based ML.
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Affiliation(s)
- Buajieerguli Maimaiti
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Hongmei Meng
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.
| | - Yudan Lv
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Jiqing Qiu
- Department of Neurological Surgery, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Zhanpeng Zhu
- Department of Neurological Surgery, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yinyin Xie
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yue Li
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Yu-Cheng
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Weixuan Zhao
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Jiayu Liu
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Mingyang Li
- Department of Communication Engineering, Jilin University, Changchun, Jilin, People's Republic of China.
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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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Nunez-Ibero M, Camino-Pontes B, Diez I, Erramuzpe A, Martinez-Gutierrez E, Stramaglia S, Alvarez-Cienfuegos JO, Cortes JM. A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers. IEEE J Biomed Health Inform 2021; 25:2948-2957. [PMID: 33999827 DOI: 10.1109/jbhi.2021.3080935] [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: 11/10/2022]
Abstract
OBJECTIVE To develop a new device for identifying physiological markers of pain perception by reading the brain's electrical activity and hemodynamic interactions while applying thermoalgesic stimulation. METHODS We designed a compact prototype that generates well-controlled thermal stimuli using a computer-driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females). We first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increased the temperature. Next, we defined the painful condition as the one occurring at temperature equal to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. RESULTS Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals differentiated the condition of pain. In particular, the SE for PPG was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. CONCLUSION Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. SIGNIFICANCE The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.
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Martín-Brufau R, Gómez MN, Sanchez-Sanchez-Rojas L, Nombela C. Fibromyalgia Detection Based on EEG Connectivity Patterns. J Clin Med 2021; 10:jcm10153277. [PMID: 34362061 PMCID: PMC8348913 DOI: 10.3390/jcm10153277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 01/04/2023] Open
Abstract
Objective: The identification of a complementary test to confirm the diagnosis of FM. The diagnosis of fibromyalgia (FM) is based on clinical features, but there is still no consensus, so patients and clinicians might benefit from such a test. Recent findings showed that pain lies in neuronal bases (pain matrices) and, in the long term, chronic pain modifies the activity and dynamics of brain structures. Our hypothesis is that patients with FM present lower levels of brain activity and therefore less connectivity than controls. Methods: We registered the resting state EEG of 23 patients with FM and compared them with 23 control subjects’ resting state recordings from the PhysioBank database. We measured frequency, amplitude, and functional connectivity, and conducted source localization (sLORETA). ROC analysis was performed on the resulting data. Results: We found significant differences in brain bioelectrical activity at rest in all analyzed bands between patients and controls, except for Delta. Subsequent source analysis provided connectivity values that depicted a distinct profile, with high discriminative capacity (between 91.3–100%) between the two groups. Conclusions: Patients with FM show a distinct neurophysiological pattern that fits with the clinical features of the disease.
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Affiliation(s)
- Ramón Martín-Brufau
- Unidad de Corta Estancia, Hospital Psiquiátrico Román Alberca, National Service of Health, 30120 Murcia, Spain;
| | | | - Leyre Sanchez-Sanchez-Rojas
- Regenerative Medicine and Advanced Therapies Lab., Instituto de Investigación Sanitaria San Carlos (IdIISC), Hospital Clínico San Carlos, 28040 Madrid, Spain;
| | - Cristina Nombela
- Biological and Health Psychology, Autonomous University of Madrid (UAM), 28049 Madrid, Spain
- Correspondence: ; Tel.: +34-4975921
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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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Mumtaz W, Rasheed S, Irfan A. Review of challenges associated with the EEG artifact removal methods. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102741] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Maddirala AK, Veluvolu KC. Eye-blink artifact removal from single channel EEG with k-means and SSA. Sci Rep 2021; 11:11043. [PMID: 34040062 PMCID: PMC8155082 DOI: 10.1038/s41598-021-90437-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 05/04/2021] [Indexed: 02/04/2023] Open
Abstract
In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ([Formula: see text]) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.
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Affiliation(s)
- Ajay Kumar Maddirala
- grid.258803.40000 0001 0661 1556School of Electronics Engineering, Kyungpook National University, Daegu, 41566 South Korea
| | - Kalyana C Veluvolu
- grid.258803.40000 0001 0661 1556School of Electronics Engineering, Kyungpook National University, Daegu, 41566 South Korea ,grid.258803.40000 0001 0661 1556School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566 South Korea
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Zhang S, Yan Z, Sapkota S, Zhao S, Ooi WT. Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device. SENSORS (BASEL, SWITZERLAND) 2021; 21:3419. [PMID: 34069027 PMCID: PMC8156270 DOI: 10.3390/s21103419] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/28/2021] [Accepted: 05/08/2021] [Indexed: 11/16/2022]
Abstract
While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.
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Affiliation(s)
- Shan Zhang
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
| | - Zihan Yan
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
| | - Shardul Sapkota
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
- Division of Science, Yale-NUS College, Singapore 138527, Singapore
| | - Shengdong Zhao
- NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore; (Z.Y.); (S.S.); (S.Z.)
| | - Wei Tsang Ooi
- National University of Singapore, Singapore 117417, Singapore;
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Shahbakhti M, Rodrigues AS, Augustyniak P, Broniec-Wójcik A, Sološenko A, Beiramvand M, Marozas V. SWT-kurtosis based algorithm for elimination of electrical shift and linear trend from EEG signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102373] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Phadikar S, Sinha N, Ghosh R. Automatic Eyeblink Artifact Removal From EEG Signal Using Wavelet Transform With Heuristically Optimized Threshold. IEEE J Biomed Health Inform 2021; 25:475-484. [PMID: 32750902 DOI: 10.1109/jbhi.2020.2995235] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper proposes an automatic eyeblink artifacts removal method from corrupted-EEG signals using discrete wavelet transform (DWT) and meta-heuristically optimized threshold. The novel idea of thresholding approximation-coefficients (ACs) instead of detail-coefficients (DCs) of DWT of EEG in a backward manner is proposed for the first time for the removal of eyeblink artifacts. EEG is very sensitive and easily gets affected by eyeblink artifacts. First, the eyeblink corrupted EEG signals are identified using support vector machine (SVM) as a classifier. Then the corrupted EEG signal is decomposed using DWT up to the sixth level. Both the mother wavelet and the level of decomposition are selected using appropriate techniques. Then the ACs are thresholded in backward manner using the optimum threshold values followed by inverse DWT operation to reconstruct the original EEG signal. The AC at level 6 is thresholded and is used in IDWT with DC to get back the AC at level 5. Likewise, the backward thresholding of the ACs followed by IDWT is continued till the artifact free EEG signal is reconstructed at level 1. The optimum values of the thresholds of the ACs at different levels are optimized using two meta-heuristic algorithms, particle swarm optimization (PSO) and grey wolf optimization (GWO) for comparison. The results reveal that the proposed methodology is superior to the recently reported methods in terms of average correlation coefficient (CC) which states that the proposed method is better in terms of the quality of reconstruction in addition to being fully automatic.
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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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Gajbhiye P, Mingchinda N, Chen W, Mukhopadhyay SC, Wilaiprasitporn T, Tripathy RK. Wavelet Domain Optimized Savitzky–Golay Filter for the Removal of Motion Artifacts From EEG Recordings. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021; 70:1-11. [PMID: 0 DOI: 10.1109/tim.2020.3041099] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
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Prasad DS, Chanamallu SR, Prasad KS. Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform. Comput Methods Biomech Biomed Engin 2020; 24:551-578. [PMID: 33245687 DOI: 10.1080/10255842.2020.1839893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous efforts have been taken to improve its quality by eliminating the artifacts. The EEG involves the biological artifacts (ocular artifacts, ECG and EMG artifacts), and technical artifacts (noise from the electric power source, amplitude artifacts, etc.). From these physiological artifacts, ocular activities are one of the most well-known over other noise sources. Reducing the risks of this event and avoid it is practically very difficult, even impossible, as the ocular activities are involuntary tasks. To trim down the effect of ocular artifacts overlapping with EEG signal and overwhelm the subjected flaws, few intelligent approaches have to be developed. This proposal tempts to implement a novel method for detecting and preventing ocular artifacts from the EEG signal. The developed model involves two main phases: (a) Detection of Ocular artifacts and (b) Removal of ocular artifacts. For detecting the ocular artifacts, initially, the EEG is subjected to decomposition process using 5-level Discrete Wavelet Transform (DWT), and Empirical Mean Curve Decomposition (EMCD). Next to the decomposition process, the features like kurtosis, variance, Shannon's entropy, and few first-order statistical features are extracted. These features will be helpful for the detection process in the classification side. For detecting the ocular artifacts from the decomposed signal, the extracted features are subjected to a machine learning algorithm called Neural Network (NN). As an improvement to the conventional NN, the training algorithm of ANN is improved by the improved Earth Worm optimization Algorithm (EWA) termed as Dual Positioned Elitism-based EWA (DPE-EWA), which updates the weight of NN to improve the performance. In the Removal phase, the optimized Lifting Wavelet Transform (LWT) is deployed, in which the improvement is made on optimizing the filter coefficients using the proposed DPE-EWA. Thus, the integration of optimized NN and optimized LWT suggests a potential possibility to accommodate the detection and removal of ocular artifacts that exist in the EEG signals.
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Bahador N, Erikson K, Laurila J, Koskenkari J, Ala-Kokko T, Kortelainen J. A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG. J Neural Eng 2020; 17:056018. [PMID: 33055380 DOI: 10.1088/1741-2552/abb5bd] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVE When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance. APPROACH A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact. MAIN RESULTS The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset. SIGNIFICANCE This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.
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Affiliation(s)
- Nooshin Bahador
- Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, MRC Oulu, University of Oulu, Oulu, Finland
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Bahador N, Erikson K, Laurila J, Koskenkari J, Ala-Kokko T, Kortelainen J. Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:138-141. [PMID: 33017949 DOI: 10.1109/embc44109.2020.9175711] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.
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Rashid M, Sulaiman N, P P Abdul Majeed A, Musa RM, Ab Nasir AF, Bari BS, Khatun S. Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review. Front Neurorobot 2020; 14:25. [PMID: 32581758 PMCID: PMC7283463 DOI: 10.3389/fnbot.2020.00025] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 04/08/2020] [Indexed: 12/12/2022] Open
Abstract
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
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Affiliation(s)
- Mamunur Rashid
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Norizam Sulaiman
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Anwar P P Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Malaysia
| | - Ahmad Fakhri Ab Nasir
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Bifta Sama Bari
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Sabira Khatun
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
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Amo Usanos C, Boquete L, de Santiago L, Barea Navarro R, Cavaliere C. Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis. SENSORS 2020; 20:s20061545. [PMID: 32168747 PMCID: PMC7146111 DOI: 10.3390/s20061545] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 03/03/2020] [Accepted: 03/10/2020] [Indexed: 11/16/2022]
Abstract
The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30-60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain-computer interfaces, and diagnosis of cognitive or motor pathologies.
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Mucarquer JA, Prado P, Escobar MJ, El-Deredy W, Zañartu M. Improving EEG Muscle Artifact Removal With an EMG Array. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020; 69:815-824. [PMID: 32205896 PMCID: PMC7088455 DOI: 10.1109/tim.2019.2906967] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Removal of artifacts induced by muscle activity is crucial for analysis of the electroencephalogram (EEG), and continues to be a challenge in experiments where the subject may speak, change facial expressions, or move. Ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) has been proven to be an efficient method for denoising of EEG contaminated with muscle artifacts. EEMD-CCA, likewise the majority of algorithms, does not incorporate any statistical information of the artifact, namely, electromyogram (EMG) recorded over the muscles actively contaminating the EEG. In this paper, we propose to extend EEMD-CCA in order to include an EMG array as information to aid the removal of artifacts, assessing the performance gain achieved when the number of EMG channels grow. By filtering adaptively (recursive least squares, EMG array as reference) each component resulting from CCA, we aim to ameliorate the distortion of brain signals induced by artifacts and denoising methods. We simulated several noise scenarios based on a linear contamination model, between real and synthetic EEG and EMG signals, and varied the number of EMG channels available to the filter. Our results exhibit a substantial improvement in the performance as the number of EMG electrodes increase from 2 to 16. Further increasing the number of EMG channels up to 128 did not have a significant impact on the performance. We conclude by recommending the use of EMG electrodes to filter components, as it is a computationally inexpensive enhancement that impacts significantly on performance using only a few electrodes.
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Affiliation(s)
- Juan Andrés Mucarquer
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| | - Pavel Prado
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| | - María-José Escobar
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| | - Wael El-Deredy
- Department of Biomedical Engineering, Universidad de Valparaíso, Valparaíso 2360102, Chile
| | - Matías Zañartu
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
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Multiresolution approach for artifacts removal and localization of seizure onset zone in epileptic EEG signal. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Anem J, Kumar GS, Madhu R. Cat Swarm Fractional Calculus optimization-based deep learning for artifact removal from EEG signal. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1704438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Jayalaxmi Anem
- ECE, Aditya Institute of Technology and Management, Tekkali, India
| | - G. Sateesh Kumar
- ECE, Aditya Institute of Technology and Management, Tekkali, India
| | - R. Madhu
- ECE, Jawaharlal Nehru Technological University, Kakinada, India
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Abstract
The emerging field of affective computing focuses on enhancing computers’ ability to understand and appropriately respond to people’s affective states in human-computer interactions, and has revealed significant potential for a wide spectrum of applications. Recently, the electroencephalography (EEG) based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application. The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology, psychology, and neuroscience. On the theoretical side, we suggest that researchers should be well aware of the limitations of the commonly used emotion models, and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms. On the practical side, we propose several operational recommendations for the challenges about data collection, feature extraction, model implementation, online system design, as well as the potential ethical issues. The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.
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Affiliation(s)
- Xin Hu
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
- These authors contributed equally to this work
| | - Jingjing Chen
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
- These authors contributed equally to this work
| | - Fei Wang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Dan Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
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Frassineti L, Barba C, Melani F, Piras F, Guerrini R, Manfredi C. Automatic detection and sonification of nonmotor generalized onset epileptic seizures: Preliminary results. Brain Res 2019; 1721:146341. [DOI: 10.1016/j.brainres.2019.146341] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 06/04/2019] [Accepted: 07/17/2019] [Indexed: 10/26/2022]
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Usman SM, Khalid S, Akhtar R, Bortolotto Z, Bashir Z, Qiu H. Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies. Seizure 2019; 71:258-269. [DOI: 10.1016/j.seizure.2019.08.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 08/09/2019] [Accepted: 08/14/2019] [Indexed: 12/24/2022] Open
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Naik GR, Gargiulo GD, Serrador JM, Breen PP. Groundtruth: A Matlab GUI for Artifact and Feature Identification in Physiological Signals. Front Physiol 2019; 10:850. [PMID: 31481893 PMCID: PMC6710362 DOI: 10.3389/fphys.2019.00850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 06/20/2019] [Indexed: 12/03/2022] Open
Abstract
Groundtruth is a Matlab Graphical User Interface (GUI) developed for the identification of key features and artifacts within physiological signals. The ultimate aim of this GUI is to provide a simple means of assessing the performance of new sensors. Secondary, to this is providing a means of providing marked data, enabling assessment of automated artifact rejection and feature identification algorithms. With the emergence of new wearable sensor technologies, there is an unmet need for convenient assessment of device performance, and a faster means of assessing new algorithms. The proposed GUI allows interactive marking of artifact regions as well as simultaneous interactive identification of key features, e.g., respiration peaks in respiration signals, R-peaks in Electrocardiography signals, etc. In this paper, we present the base structure of the system, together with an example of its use for two simultaneously worn respiration sensors. The respiration rates are computed for both original as well as artifact removed data and validated using Bland–Altman plots. The respiration rates computed based on the proposed GUI (after artifact removal process) demonstrated consistent results for two respiration sensors after artifact removal process. Groundtruth is customizable, and alternative processing modules are easy to add/remove. Groundtruth is intended for open-source use.
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Affiliation(s)
- Ganesh R Naik
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Gaetano D Gargiulo
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Jorge M Serrador
- Rutgers Biomedical and Health Sciences, Newark, NJ, United States.,Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers University, Newark, NJ, United States
| | - Paul P Breen
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia.,Translational Health Research Institute, Western Sydney University, Penrith, NSW, Australia
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Hu X, Chen J, Wang F, Zhang D. Ten challenges for EEG-based affective computing. BRAIN SCIENCE ADVANCES 2019. [DOI: 10.26599/bsa.2019.9050005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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Jiang X, Bian GB, Tian Z. Removal of Artifacts from EEG Signals: A Review. SENSORS (BASEL, SWITZERLAND) 2019; 19:E987. [PMID: 30813520 PMCID: PMC6427454 DOI: 10.3390/s19050987] [Citation(s) in RCA: 210] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 02/03/2019] [Accepted: 02/21/2019] [Indexed: 11/28/2022]
Abstract
Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.
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Affiliation(s)
- Xiao Jiang
- Institute of Automation, Chinese Academy of Science, Beijing 100190, China.
- School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China.
| | - Gui-Bin Bian
- Institute of Automation, Chinese Academy of Science, Beijing 100190, China.
| | - Zean Tian
- School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China.
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Grosselin F, Navarro-Sune X, Vozzi A, Pandremmenou K, De Vico Fallani F, Attal Y, Chavez M. Quality Assessment of Single-Channel EEG for Wearable Devices. SENSORS 2019; 19:s19030601. [PMID: 30709004 PMCID: PMC6387437 DOI: 10.3390/s19030601] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 01/28/2019] [Indexed: 11/16/2022]
Abstract
The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.
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Affiliation(s)
- Fanny Grosselin
- hlSorbonne Université, UPMC Univ. Paris 06, INSERM U-1127, CNRS UMR-7225, Institut du Cerveau et de la Moelle Épinière (ICM), Groupe Hospitalier Pitié Salpêtrière-Charles Foix, 75013 Paris, France.
- myBrainTechnologies, 75010 Paris, France.
| | | | | | | | - Fabrizio De Vico Fallani
- hlSorbonne Université, UPMC Univ. Paris 06, INSERM U-1127, CNRS UMR-7225, Institut du Cerveau et de la Moelle Épinière (ICM), Groupe Hospitalier Pitié Salpêtrière-Charles Foix, 75013 Paris, France.
- INRIA, Aramis Project-Team, F-75013 Paris, France.
| | | | - Mario Chavez
- CNRS UMR-7225, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, 75013 Paris, France.
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Goh SK, Abbass HA, Tan KC, Al-Mamun A, Thakor N, Bezerianos A, Li J. Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1858-1867. [DOI: 10.1109/tnsre.2018.2864119] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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