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Wang B, Deng F, Jiang P. EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network. Comput Biol Med 2024; 177:108626. [PMID: 38810475 DOI: 10.1016/j.compbiomed.2024.108626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/29/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
Electroencephalogram (EEG) signals are pivotal in clinical medicine, brain research, and neurological disorder studies. However, their susceptibility to contamination from physiological and environmental noise challenges the precision of brain activity analysis. Advances in deep learning have yielded superior EEG signal denoising techniques that eclipse traditional approaches. In this research, we deploy the Retentive Network architecture - initially crafted for large language models (LLMs) - for EEG denoising, exploiting its robust feature extraction and comprehensive modeling prowess. Furthermore, its inherent temporal structure alignment makes the Retentive Network particularly well-suited for the time-series nature of EEG signals, offering an additional rationale for its adoption. To conform the Retentive Network to the unidimensional characteristic of EEG signals, we introduce a signal embedding tactic that reshapes these signals into a two-dimensional embedding space conducive to network processing. This avant-garde method not only carves a novel trajectory in EEG denoising but also enhances our comprehension of brain functionality and the accuracy in diagnosing neurological ailments. Moreover, in response to the labor-intensive creation of deep learning datasets, we furnish a standardized, preprocessed dataset poised to streamline deep learning advancements in this domain.
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Affiliation(s)
- Bin Wang
- College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 629100, Sichuan, China.
| | - Fei Deng
- College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 629100, Sichuan, China.
| | - Peifan Jiang
- College of Geophysics, Chengdu University of Technology, Chengdu, 629100, Sichuan, China.
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2
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Şekerci Y, Kahraman MU, Özturan Ö, Çelik E, Ayan SŞ. Neurocognitive responses to spatial design behaviors and tools among interior architecture students: a pilot study. Sci Rep 2024; 14:4454. [PMID: 38396070 PMCID: PMC10891056 DOI: 10.1038/s41598-024-55182-7] [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: 06/05/2023] [Accepted: 02/21/2024] [Indexed: 02/25/2024] Open
Abstract
The impact of emotions on human behavior is substantial, and the ability to recognize people's feelings has a wide range of practical applications including education. Here, the methods and tools of education are being calibrated according to the data gained over electroencephalogram (EEG) signals. The issue of which design tools would be ideal in the future of interior architecture education, is an uncertain field. It is important to measure the students' emotional states while using manual and digital design tools to determine the different impacts. Brain-computer interfaces have made it possible to monitor emotional states in a way that is both convenient and economical. In the research of emotion recognition, EEG signals have been employed, and the resulting literature explains basic emotions as well as complicated scenarios that are created from the combination of numerous basic emotions. The objective of this study is to investigate the emotional states and degrees of attachment experienced by interior architecture students while engaging in their design processes. This includes examining the use of 2D or 3D tools, whether manual or digital, and identifying any changes in design tool usage and behaviors that may be influenced by different teaching techniques. Accordingly, the hierarchical clustering which is a technique used in data analysis to group objects into a hierarchical structure of clusters based on their similarities has been conducted.
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Affiliation(s)
- Yaren Şekerci
- Interior Architecture and Environmental Design, Antalya Bilim University, Antalya, 07190, Turkey.
| | - Mehmet Uğur Kahraman
- Interior Architecture and Environmental Design, Antalya Bilim University, Antalya, 07190, Turkey
| | - Özgü Özturan
- Akdeniz University, Interior Architecture, Antalya, 07070, Turkey
| | - Ertuğrul Çelik
- Electrical and Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey
| | - Sevgi Şengül Ayan
- Industrial Engineering, Antalya Bilim University, Antalya, 07190, Turkey
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3
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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4
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The importance of ocular artifact removal in single-trial ERP analysis: The case of the N250 in face learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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5
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Daud SNSS, Sudirman R. Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review. Ann Biomed Eng 2022; 50:1271-1291. [PMID: 35994164 DOI: 10.1007/s10439-022-03053-5] [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: 06/29/2022] [Accepted: 08/10/2022] [Indexed: 11/26/2022]
Abstract
Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time-frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time-frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.
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Affiliation(s)
| | - Rubita Sudirman
- School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Johor, Malaysia
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Ahmed MIB, Alotaibi S, Atta-ur-Rahman, Dash S, Nabil M, AlTurki AO. A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy. SN COMPUTER SCIENCE 2022; 3:437. [PMID: 35965953 PMCID: PMC9364307 DOI: 10.1007/s42979-022-01358-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/26/2022] [Indexed: 10/26/2022]
Abstract
Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of the brain which results in recurrent seizures. Though the epilepsy in general is considered as a serious disorder, its effects in children are rather dangerous. It is mainly because it reasons a slower rate of development and a failure to improve certain skills among such children. Seizures are the most common symptom of epilepsy. As a regular medical procedure, the specialists record brain activity using an electroencephalogram (EEG) to observe epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate and depend on the specialist's experience; therefore, automated detection of epileptic pediatric seizures might be an optimal solution. In this regard, several techniques have been investigated in the literature. This research aims to review the approaches to pediatric epilepsy seizures' identification especially those based on machine learning, in addition to the techniques applied on the CHB-MIT scalp EEG database of epileptic pediatric signals.
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Affiliation(s)
- Mohammed Imran Basheer Ahmed
- Department of Computer Engineering, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Shamsah Alotaibi
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Atta-ur-Rahman
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Sujata Dash
- Department of Computer Application, Maharaja Srirama Chandra Bhanj Deo University, Baripada, India
| | - Majed Nabil
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Abdullah Omar AlTurki
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
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7
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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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8
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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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9
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Wu Q, Zhang W, Wang Y, Zhang W, Liu X. Research on removal algorithm of EOG artifacts in single-channel EEG signals based on CEEMDAN-BD. Comput Methods Biomech Biomed Engin 2021; 24:1368-1379. [PMID: 33620279 DOI: 10.1080/10255842.2021.1889525] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Single-channel electroencephalography (EEG) signals are more susceptible to electro-oculography (EOG) interference, which could be attributed to the acquisition device of the single-channel. To realize EOG artifacts separation in this paper, the blind deconvolution (BD) model was investigated based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The CEEMDAN method was firstly used to decompose the EEG data contained artifacts into several intrinsic mode functions (IMF). After that, the modal component used as the observed signal was provided to the BD model, which was formed by the source signal of the EEG signal and the EOG artifacts. Consequently, we successfully realized the separation of EEG signal and EOG artifacts by the constructing cost function iteratively, and our results demonstrated that the separation effect of this method on EOG artifacts is better than previous studies. Further, the correlation coefficient of real-life data after CEEMDAN-BD algorithm processing reaches 0.81. Moreover, the modal aliasing problem was solved with most of the original EEG signal components retained. In a word, this novel method provides theory and practice references for the processing of EEG signals and other physiological signals.
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Affiliation(s)
- Quanyu Wu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, P. R. China
| | - WenQiang Zhang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, P. R. China
| | - Ye Wang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, P. R. China
| | - Wenxi Zhang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, P. R. China
| | - Xiaojie Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, P. R. China
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10
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Li Y, Wang PT, Vaidya MP, Flint RD, Liu CY, Slutzky MW, Do AH. Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)-A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG. Front Neurosci 2021; 14:597941. [PMID: 33584176 PMCID: PMC7873899 DOI: 10.3389/fnins.2020.597941] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 12/11/2020] [Indexed: 12/01/2022] Open
Abstract
Electroencephalographic (EEG) recordings are often contaminated by electromyographic (EMG) artifacts, especially when recording during movement. Existing methods to remove EMG artifacts include independent component analysis (ICA), and other high-order statistical methods. However, these methods can not effectively remove most of EMG artifacts. Here, we proposed a modified ICA model for EMG artifacts removal in the EEG, which is called EMG Removal by Adding Sources of EMG (ERASE). In this new approach, additional channels of real EMG from neck and head muscles (reference artifacts) were added as inputs to ICA in order to “force” the most power from EMG artifacts into a few independent components (ICs). The ICs containing EMG artifacts (the “artifact ICs”) were identified and rejected using an automated procedure. ERASE was validated first using both simulated and experimentally-recorded EEG and EMG. Simulation results showed ERASE removed EMG artifacts from EEG significantly more effectively than conventional ICA. Also, it had a low false positive rate and high sensitivity. Subsequently, EEG was collected from 8 healthy participants while they moved their hands to test the realistic efficacy of this approach. Results showed that ERASE successfully removed EMG artifacts (on average, about 75% of EMG artifacts were removed when using real EMGs as reference artifacts) while preserving the expected EEG features related to movement. We also tested the ERASE procedure using simulated EMGs as reference artifacts (about 63% of EMG artifacts removed). Compared to conventional ICA, ERASE removed on average 26% more EMG artifacts from EEG. These findings suggest that ERASE can achieve significant separation of EEG signal and EMG artifacts without a loss of the underlying EEG features. These results indicate that using additional real or simulated EMG sources can increase the effectiveness of ICA in removing EMG artifacts from EEG. Combined with automated artifact IC rejection, ERASE also minimizes potential user bias. Future work will focus on improving ERASE so that it can also be used in real-time applications.
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Affiliation(s)
- Yongcheng Li
- Department of Neurology, University of California, Irvine, Irvine, CA, United States
| | - Po T Wang
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Mukta P Vaidya
- Department of Neurology, Northwestern University, Chicago, IL, United States.,Department of Physiology, Northwestern University, Chicago, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Robert D Flint
- Department of Neurology, Northwestern University, Chicago, IL, United States.,Department of Physiology, Northwestern University, Chicago, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Charles Y Liu
- Department of Neurosurgery, University of Southern California, Los Angeles, CA, United States.,Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States.,Neurorestoration Center, University of Southern California, Los Angeles, CA, United States
| | - Marc W Slutzky
- Department of Neurology, Northwestern University, Chicago, IL, United States.,Department of Physiology, Northwestern University, Chicago, IL, United States.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - An H Do
- Department of Neurology, University of California, Irvine, Irvine, CA, United States
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Ergün E, Aydemir O. A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals. Cogn Neurodyn 2020; 14:609-617. [PMID: 33014176 DOI: 10.1007/s11571-020-09592-8] [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] [Received: 01/28/2020] [Revised: 03/31/2020] [Accepted: 04/11/2020] [Indexed: 10/24/2022] Open
Abstract
Brain computer interface systems decode brain activities from electroencephalogram (EEG) signals and translate the user's intentions into commands to control and/or communicate with augmentative or assistive devices without activating any muscle or peripheral nerve. In this paper, we aimed to improve the accuracy of these systems using improved EEG signal processing techniques through a novel evolutionary approach (fusion-based preprocessing method). This approach was inspired by chromosomal crossover, which is the transfer of genetic material between homologous chromosomes. In this study, the proposed fusion-based preprocessing method was applied to an open access dataset collected from 29 subjects. Then, features were extracted by the autoregressive model and classified by k-nearest neighbor classifier. We achieved classification accuracy (CA) ranging from 67.57 to 99.70% for the detection of binary mental arithmetic (MA) based EEG signals. In addition to obtaining an average CA of 88.71%, 93.10% of the subjects showed performance improvement using the fusion-based preprocessing method. Furthermore, we compared the proposed study with the common average reference (CAR) method and without applying any preprocessing method. The achieved results showed that the proposed method provided 3.91% and 2.75% better CA then the CAR and without applying any preprocessing method, respectively. The results also prove that the proposed evolutionary preprocessing approach has great potential to classify the EEG signals recorded during MA task.
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Affiliation(s)
- Ebru Ergün
- Department of Electrical-Electronics Engineering, Faculty of Engineering, Recep Tayyip Erdogan University, Rize, Turkey
| | - Onder Aydemir
- Department of Electrical-Electronics Engineering, Faculty of Engineering, Karadeniz Technical University, Trabzon, Turkey
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Sun W, Su Y, Wu X, Wu X. A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.029] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Sheela P, Puthankattil SD. A hybrid method for artifact removal of visual evoked EEG. J Neurosci Methods 2020; 336:108638. [DOI: 10.1016/j.jneumeth.2020.108638] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 01/27/2020] [Accepted: 02/18/2020] [Indexed: 10/25/2022]
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Issa MF, Juhasz Z. Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis. Brain Sci 2019; 9:brainsci9120355. [PMID: 31817120 PMCID: PMC6956025 DOI: 10.3390/brainsci9120355] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/21/2019] [Accepted: 12/02/2019] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.
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Affiliation(s)
- Mohamed F. Issa
- Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Egyetem u.10, 8200 Veszprém, Hungary;
- Department of Scientific Computing, Faculty of Computers and Informatics, Benha University, Fareed Nada, Benha 13511, Egypt
- Correspondence:
| | - Zoltan Juhasz
- Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Egyetem u.10, 8200 Veszprém, Hungary;
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15
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Gong A, Liu J, Lu L, Wu G, Jiang C, Fu Y. Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.02.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Using a multichannel Wiener filter to remove eye-blink artifacts from EEG data. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Acharyya A, Jadhav PN, Bono V, Maharatna K, Naik GR. Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:123-133. [PMID: 29544778 DOI: 10.1016/j.cmpb.2018.02.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 12/31/2017] [Accepted: 02/02/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode. METHODS In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik für Epileptologie, Universität Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform. RESULTS Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware design based on the proposed methodology consumes 75 micro-Watt power. CONCLUSIONS The automated methodology proposed in this paper, unlike the state of the art methods, can remove blink and muscular artifacts real time without the need of any extra electrode. Its reliability and robustness is also established after exhaustive simulation study and analysis on both simulated and real data. We believe the proposed methodology would be useful in next generation personalized pervasive healthcare for Brain Computer Interface and neuro-developmental disorder diagnosis and treatment.
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Affiliation(s)
- Amit Acharyya
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India.
| | - Pranit N Jadhav
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India.
| | - Valentina Bono
- School of Electronic & Computer Science, University of Southampton, Southampton, UK.
| | - Koushik Maharatna
- School of Electronic & Computer Science, University of Southampton, Southampton, UK.
| | - Ganesh R Naik
- MARCS Institute Western Sydney University Kingswood, NSW - 2747, Australia.
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18
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A contralateral channel guided model for EEG based motor imagery classification. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.10.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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19
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Lp (p ≤ 1) Norm Partial Directed Coherence for Directed Network Analysis of Scalp EEGs. Brain Topogr 2018; 31:738-752. [DOI: 10.1007/s10548-018-0624-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 01/17/2018] [Indexed: 10/18/2022]
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20
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Sreeja SR, Sahay RR, Samanta D, Mitra P. Removal of Eye Blink Artifacts From EEG Signals Using Sparsity. IEEE J Biomed Health Inform 2017; 22:1362-1372. [PMID: 29990133 DOI: 10.1109/jbhi.2017.2771783] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain-computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.
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21
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Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:2727856. [PMID: 28874909 PMCID: PMC5569879 DOI: 10.1155/2017/2727856] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 05/14/2017] [Accepted: 07/02/2017] [Indexed: 11/17/2022]
Abstract
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.
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22
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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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23
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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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24
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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: 156] [Impact Index Per Article: 19.5] [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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25
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A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2578-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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26
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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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27
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Bhardwaj S, Jadhav P, Adapa B, Acharyya A, Naik GR. Online and automated reliable system design to remove blink and muscle artefact in EEG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:6784-7. [PMID: 26737851 DOI: 10.1109/embc.2015.7319951] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalograms (EEGs) are progressively emerging as a significant measure of brain activity and are very effective tool for the diagnosis and treatment of mental and brain diseases and disorders including sleep apnea, Alzheimer's disease and Neurodevelopmental disorders. However, EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners resulting less accurate diagnosis. In this paper we propose a real-time low-complexity and reliable system design methodology to remove these artefacts and noise in an automated fashion to aid online diagnosis under the pervasive personalized healthcare set-up without the need of any reference electrode. The simulation and hardware performance of the proposed methodology are measured and compared in terms of correlation and regression statistics lying above 80% and 67% which are much improved over the state-of-the art methodologies.
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López-Gil JM, Virgili-Gomá J, Gil R, García R. Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way. Front Comput Neurosci 2016; 10:85. [PMID: 27594831 PMCID: PMC4990561 DOI: 10.3389/fncom.2016.00085] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 08/02/2016] [Indexed: 11/23/2022] Open
Abstract
Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does not allow properly determining whether a person in self-regulation its emotions while watching affective stimuli. However, adequately combining different data sources in a synchronous way to detect emotions makes it possible to overcome the limitations of single detection methods.
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Affiliation(s)
- Juan-Miguel López-Gil
- Department of Computer Languages and Systems, University of the Basque Country Vitoria-Gasteiz, Spain
| | - Jordi Virgili-Gomá
- Department of Computer Science and Engineering, Universitat de Lleida Lleida, Spain
| | - Rosa Gil
- Department of Computer Science and Engineering, Universitat de Lleida Lleida, Spain
| | - Roberto García
- Department of Computer Science and Engineering, Universitat de Lleida Lleida, Spain
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