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Ahsan Awais M, Ward T, Redmond P, Healy G. From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces. J Neural Eng 2024; 21:046011. [PMID: 38941986 DOI: 10.1088/1741-2552/ad5d17] [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: 02/12/2024] [Accepted: 06/28/2024] [Indexed: 06/30/2024]
Abstract
Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.
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Affiliation(s)
- Muhammad Ahsan Awais
- Insight SFI Research Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
| | - Tomas Ward
- Insight SFI Research Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
| | - Peter Redmond
- Insight SFI Research Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
| | - Graham Healy
- Insight SFI Research Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
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Shahbakhti M, Beiramvand M, Rejer I, Augustyniak P, Broniec-Wojcik A, Wierzchon M, Marozas V. Simultaneous Eye Blink Characterization and Elimination from Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection. IEEE J Biomed Health Inform 2021; 26:1001-1012. [PMID: 34260361 DOI: 10.1109/jbhi.2021.3096984] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of drivers cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink and EEG band power features for the detection of driver drowsiness may be further boosted if a proper eye blink removal is also applied before EEG analysis. This paper proposes an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG data. METHODS Firstly, eye blink intervals (EBIs) are identified from the Fp1 EEG channel using variational mode extraction, and then blink-related features are derived. Secondly, the identified EBIs are projected to the rest of EEG channels and then filtered by a combination of principal component analysis and discrete wavelet transform. Thirdly, a support vector machine with 10-fold cross-validation is employed to classify alert and drowsy states from the derived blink and filtered EEG band power features. MAIN RESULTS When compared the synergy of eye blink and EEG features before and after filtering by the proposed algorithm, a significant improvement in the mean accuracy of driver drowsiness detection was achieved (71.2% vs. 78.1%, p<0.05). SIGNIFICANCE This paper validates a novel view of eye blinks as both a source of information and artifacts in EEG-based driver drowsiness detection.
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Sukriti, Chakraborty M, Mitra D. A novel automated seizure detection system from EMD-MSPCA denoised EEG: Refined composite multiscale sample, fuzzy and permutation entropies based scheme. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Bullock M, Jackson GD, Abbott DF. Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage. Front Neurol 2021; 12:622719. [PMID: 33776886 PMCID: PMC7991907 DOI: 10.3389/fneur.2021.622719] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 01/29/2021] [Indexed: 11/13/2022] Open
Abstract
Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15–20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.
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Affiliation(s)
- Madeleine Bullock
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Graeme D Jackson
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
| | - David F Abbott
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
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Belkhiria C, Peysakhovich V. Electro-Encephalography and Electro-Oculography in Aeronautics: A Review Over the Last Decade (2010-2020). FRONTIERS IN NEUROERGONOMICS 2020; 1:606719. [PMID: 38234309 PMCID: PMC10790927 DOI: 10.3389/fnrgo.2020.606719] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 11/17/2020] [Indexed: 01/19/2024]
Abstract
Electro-encephalography (EEG) and electro-oculography (EOG) are methods of electrophysiological monitoring that have potentially fruitful applications in neuroscience, clinical exploration, the aeronautical industry, and other sectors. These methods are often the most straightforward way of evaluating brain oscillations and eye movements, as they use standard laboratory or mobile techniques. This review describes the potential of EEG and EOG systems and the application of these methods in aeronautics. For example, EEG and EOG signals can be used to design brain-computer interfaces (BCI) and to interpret brain activity, such as monitoring the mental state of a pilot in determining their workload. The main objectives of this review are to, (i) offer an in-depth review of literature on the basics of EEG and EOG and their application in aeronautics; (ii) to explore the methodology and trends of research in combined EEG-EOG studies over the last decade; and (iii) to provide methodological guidelines for beginners and experts when applying these methods in environments outside the laboratory, with a particular focus on human factors and aeronautics. The study used databases from scientific, clinical, and neural engineering fields. The review first introduces the characteristics and the application of both EEG and EOG in aeronautics, undertaking a large review of relevant literature, from early to more recent studies. We then built a novel taxonomy model that includes 150 combined EEG-EOG papers published in peer-reviewed scientific journals and conferences from January 2010 to March 2020. Several data elements were reviewed for each study (e.g., pre-processing, extracted features and performance metrics), which were then examined to uncover trends in aeronautics and summarize interesting methods from this important body of literature. Finally, the review considers the advantages and limitations of these methods as well as future challenges.
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Meddah K, Zairi H, Bessekri B, Cherrih H, Kedir-Talha M. FPGA implementation of Epileptic Seizure detection based on DWT, PCA and Support Vector Machine. 2020 SECOND INTERNATIONAL CONFERENCE ON EMBEDDED & DISTRIBUTED SYSTEMS (EDIS) 2020. [DOI: 10.1109/edis49545.2020.9296466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Sidikova M, Martinek R, Kawala-Sterniuk A, Ladrova M, Jaros R, Danys L, Simonik P. Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review. SENSORS 2020; 20:s20195699. [PMID: 33036313 PMCID: PMC7582509 DOI: 10.3390/s20195699] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 12/15/2022]
Abstract
This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.
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Affiliation(s)
- Michaela Sidikova
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17 Listopadu 15, 70800 Ostrava, Czech Republic; (M.L.); (R.J.); (L.D.); (P.S.)
- Correspondence: (M.S.); (R.M.)
| | - Radek Martinek
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17 Listopadu 15, 70800 Ostrava, Czech Republic; (M.L.); (R.J.); (L.D.); (P.S.)
- Correspondence: (M.S.); (R.M.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland;
| | - Martina Ladrova
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17 Listopadu 15, 70800 Ostrava, Czech Republic; (M.L.); (R.J.); (L.D.); (P.S.)
| | - Rene Jaros
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17 Listopadu 15, 70800 Ostrava, Czech Republic; (M.L.); (R.J.); (L.D.); (P.S.)
| | - Lukas Danys
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17 Listopadu 15, 70800 Ostrava, Czech Republic; (M.L.); (R.J.); (L.D.); (P.S.)
| | - Petr Simonik
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17 Listopadu 15, 70800 Ostrava, Czech Republic; (M.L.); (R.J.); (L.D.); (P.S.)
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Zhou M, Zhao H, Xia H, Zhang J, Liu Z, Liu C, Gao F. De-noising of photoacoustic sensing and imaging based on combined empirical mode decomposition and independent component analysis. JOURNAL OF BIOPHOTONICS 2019; 12:e201900042. [PMID: 30834695 DOI: 10.1002/jbio.201900042] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/02/2019] [Accepted: 03/03/2019] [Indexed: 06/09/2023]
Abstract
Photoacoustic (PA) imaging breaks the diffusion limit of conventional optical imaging by listening to the PA wave. As a new kind of functional imaging method, it has experienced tremendous growth in research community with wide range of applications. However, it is still an open and fundamental challenge that the conversion efficiency from light to sound based on PA effect is extremely low. The consequence is the poor signal-to-noise ratio (SNR) of PA signal especially in scenarios of low laser power and deep penetration. Conventional approach to enhance the SNR of PA signal in these noisy scenarios is data averaging, which is quite time-consuming. To improve the signal fidelity and imaging speed, an algorithm of using empirical mode decomposition and independent component analysis de-noising methods in PA imaging is proposed. The simulation and in vivo experimental results show obvious SNR enhancement of the PA signal and image contrast. The proposed method provides the potential to develop real-time low-cost PA imaging system with low-power laser source.
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Affiliation(s)
- Meng Zhou
- Hybrid Imaging System Laboratory, School of Information Science and Technology, ShanghaiTech University, Shanghai, China
- Chinese Academy of Sciences, Shanghai Advanced Research Institute, Shanghai, China
| | - Huangxuan Zhao
- Research Laboratory for Biomedical Optics and Molecular Imaging, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Haibo Xia
- Chinese Academy of Sciences, Shanghai Advanced Research Institute, Shanghai, China
| | - Jiayao Zhang
- Hybrid Imaging System Laboratory, School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhicheng Liu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Chengbo Liu
- Research Laboratory for Biomedical Optics and Molecular Imaging, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Fei Gao
- Hybrid Imaging System Laboratory, School of Information Science and Technology, ShanghaiTech University, Shanghai, China
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