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Borgheai SB, Zisk AH, McLinden J, Mcintyre J, Sadjadi R, Shahriari Y. Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme. Comput Biol Med 2024; 168:107658. [PMID: 37984201 DOI: 10.1016/j.compbiomed.2023.107658] [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: 03/23/2023] [Revised: 10/20/2023] [Accepted: 10/31/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance. METHOD 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies. RESULT The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies. CONCLUSION Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.
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Affiliation(s)
- Seyyed Bahram Borgheai
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States; Neurology Department, Emory University, Atlanta, GA, United States
| | - Alyssa Hillary Zisk
- Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, RI, United States
| | - John McLinden
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States
| | - James Mcintyre
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States
| | - Reza Sadjadi
- Neurology Department, Massachusetts General Hospital, Boston, MA, United States
| | - Yalda Shahriari
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States; Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, RI, United States.
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Schmoigl-Tonis M, Schranz C, Müller-Putz GR. Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review. Front Hum Neurosci 2023; 17:1251690. [PMID: 37920561 PMCID: PMC10619676 DOI: 10.3389/fnhum.2023.1251690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023] Open
Abstract
Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.
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Affiliation(s)
- Mathias Schmoigl-Tonis
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Christoph Schranz
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
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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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A hybrid method for muscle artifact removal from EEG signals. J Neurosci Methods 2021; 353:109104. [PMID: 33617916 DOI: 10.1016/j.jneumeth.2021.109104] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/10/2021] [Accepted: 02/13/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND Electroencephalogram (EEG) signals may be contaminated with muscle artifacts that are usually difficult to be removed. NEW METHOD In this article, a new hybrid method for suppressing muscle artifacts is proposed. Our method leverages variational mode decomposition (VMD) and canonical correlation analysis (CCA) algorithms. Each channel of EEG is decomposed into intrinsic mode functions (IMFs) with VMD to achieve an extended data set that contains more channels than the original data set. The potential artifact components are decomposed by CCA for further isolation. RESULTS The proposed method is evaluated with semi-simulation and real contaminated EEG signals. The results show that the performance of removing artifacts for VMD-CCA exceeds the comparison methods. COMPARISON WITH EXISTING METHODS Regardless of the number of EEG channels and the signal-to-noise ratio of the signal, the VMD-CCA approach is superior to the existing methods. As the number of EEG channels decreases, the average de-artifact effects of VMD-CCA and the comparison approaches are basically the same, but the randomness increases. CONCLUSIONS The VMD-CCA method can effectively isolate muscle artifacts in EEG in case of multiple channels or few channels.
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Mannan MMN, Kamran MA, Kang S, Choi HS, Jeong MY. A Hybrid Speller Design Using Eye Tracking and SSVEP Brain-Computer Interface. SENSORS 2020; 20:s20030891. [PMID: 32046131 PMCID: PMC7039291 DOI: 10.3390/s20030891] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/27/2020] [Accepted: 02/05/2020] [Indexed: 12/14/2022]
Abstract
Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain-computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.
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Affiliation(s)
- Malik M. Naeem Mannan
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, 63 Beon-gil, Geumjeong-gu, Busan 609-735, Korea; (M.M.N.M.); (M.A.K.); (H.S.C.)
| | - M. Ahmad Kamran
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, 63 Beon-gil, Geumjeong-gu, Busan 609-735, Korea; (M.M.N.M.); (M.A.K.); (H.S.C.)
| | - Shinil Kang
- National Center for Optically-Assisted Ultrahigh-Precision Mechanical Systems, Yonsei University, Seoul 03722, Korea;
- School of Mechanical Engineering, Yonsei University, Seoul 03722, Korea
| | - Hak Soo Choi
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, 63 Beon-gil, Geumjeong-gu, Busan 609-735, Korea; (M.M.N.M.); (M.A.K.); (H.S.C.)
- Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115, USA
| | - Myung Yung Jeong
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, 63 Beon-gil, Geumjeong-gu, Busan 609-735, Korea; (M.M.N.M.); (M.A.K.); (H.S.C.)
- Correspondence:
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Matanga Y, Djouani K, Kurien A. Analysis of User Control Attainment in SMR-based Brain Computer Interfaces. Ing Rech Biomed 2018. [DOI: 10.1016/j.irbm.2018.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Georgiadis K, Laskaris N, Nikolopoulos S, Kompatsiaris I. Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs. J Neuroeng Rehabil 2018; 15:90. [PMID: 30373619 PMCID: PMC6206934 DOI: 10.1186/s12984-018-0431-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 09/21/2018] [Indexed: 11/25/2022] Open
Abstract
Background Phase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication channel in patients suffering from neuromuscular disorders remains totally unexplored. We investigate, here, this possibility by estimating the time-resolved phase connectivity patterns induced during a motor imagery (MI) task and adopting a supervised learning scheme to recover the subject’s intention from the streaming data. Methods Electroencephalographic activity from six patients suffering from neuromuscular disease (NMD) and six healthy individuals was recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition. The metric of Phase locking value (PLV) was used to describe the functional coupling between all recording sites. The functional connectivity patterns and the associate network organization was first compared between the two cohorts. Next, working at the level of individual patients, we trained support vector machines (SVMs) to discriminate between “left” and “right” based on different instantiations of connectivity patterns (depending on the encountered brain rhythm and the temporal interval). Finally, we designed and realized a novel brain decoding scheme that could interpret the intention from streaming connectivity patterns, based on an ensemble of SVMs. Results The group-level analysis revealed increased phase synchrony and richer network organization in patients. This trend was also seen in the performance of the employed classifiers. Time-resolved connectivity led to superior performance, with distinct SVMs acting as local experts, specialized in the patterning emerged within specific temporal windows (defined with respect to the external trigger). This empirical finding was further exploited in implementing a decoding scheme that can be activated without the need of the precise timing of a trigger. Conclusion The increased phase synchrony in NMD patients can turn to a valuable tool for MI decoding. Considering the fast implementation for the PLV pattern computation in multichannel signals, we can envision the development of efficient personalized BCI systems in assistance of these patients. Electronic supplementary material The online version of this article (10.1186/s12984-018-0431-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kostas Georgiadis
- AIIA lab, Informatics Department, AUTH, Thessaloniki, Greece. .,Information Technologies Institute (ITI), Centre for Research & Technology Hellas, Thessaloniki-Thermi, Greece.
| | - Nikos Laskaris
- AIIA lab, Informatics Department, AUTH, Thessaloniki, Greece.,NeuroInformatics.GRoup, AUTH, Thessaloniki, Greece
| | - Spiros Nikolopoulos
- Information Technologies Institute (ITI), Centre for Research & Technology Hellas, Thessaloniki-Thermi, Greece
| | - Ioannis Kompatsiaris
- Information Technologies Institute (ITI), Centre for Research & Technology Hellas, Thessaloniki-Thermi, Greece
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Hong KS, Khan MJ. Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review. Front Neurorobot 2017; 11:35. [PMID: 28790910 PMCID: PMC5522881 DOI: 10.3389/fnbot.2017.00035] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 07/03/2017] [Indexed: 12/11/2022] Open
Abstract
In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.
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Affiliation(s)
- Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
| | - Muhammad Jawad Khan
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
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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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Wu Y, Li M, Wang J. Toward a hybrid brain-computer interface based on repetitive visual stimuli with missing events. J Neuroeng Rehabil 2016; 13:66. [PMID: 27460070 PMCID: PMC4962511 DOI: 10.1186/s12984-016-0179-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 07/20/2016] [Indexed: 11/12/2022] Open
Abstract
Background Steady-state visually evoked potentials (SSVEPs) can be elicited by repetitive stimuli and extracted in the frequency domain with satisfied performance. However, the temporal information of such stimulus is often ignored. In this study, we utilized repetitive visual stimuli with missing events to present a novel hybrid BCI paradigm based on SSVEP and omitted stimulus potential (OSP). Methods Four discs flickering from black to white with missing flickers served as visual stimulators to simultaneously elicit subject’s SSVEPs and OSPs. Key parameters in the new paradigm, including flicker frequency, optimal electrodes, missing flicker duration and intervals of missing events were qualitatively discussed with offline data. Two omitted flicker patterns including missing black/white disc were proposed and compared. Averaging times were optimized with Information Transfer Rate (ITR) in online experiments, where SSVEPs and OSPs were identified using Canonical Correlation Analysis in the frequency domain and Support Vector Machine (SVM)-Bayes fusion in the time domain, respectively. Results and conclusions The online accuracy and ITR (mean ± standard deviation) over nine healthy subjects were 79.29 ± 18.14 % and 19.45 ± 11.99 bits/min with missing black disc pattern, and 86.82 ± 12.91 % and 24.06 ± 10.95 bits/min with missing white disc pattern, respectively. The proposed BCI paradigm, for the first time, demonstrated that SSVEPs and OSPs can be simultaneously elicited in single visual stimulus pattern and recognized in real-time with satisfied performance. Besides the frequency features such as SSVEP elicited by repetitive stimuli, we found a new feature (OSP) in the time domain to design a novel hybrid BCI paradigm by adding missing events in repetitive stimuli.
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Affiliation(s)
- Yingying Wu
- Department of Instrument Science and Technology, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, ShaanXi, China
| | - Man Li
- Department of Instrument Science and Technology, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, ShaanXi, China
| | - Jing Wang
- Department of Instrument Science and Technology, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, ShaanXi, China.
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A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief 2016; 8:1004-6. [PMID: 27508255 PMCID: PMC4969208 DOI: 10.1016/j.dib.2016.06.032] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Revised: 06/12/2016] [Accepted: 06/21/2016] [Indexed: 11/20/2022] Open
Abstract
Artifact rejection techniques are used to recover the brain signals underlying artifactual electroencephalographic (EEG) segments. Although over the last few years many different artifact rejection techniques have been proposed (http://dx.doi.org/10.1109/JSEN.2011.2115236[1], http://dx.doi.org/10.1016/j.clinph.2006.09.003[2], http://dx.doi.org/10.3390/e16126553[3]), none has been established as a gold standard so far, because assessing their performance is difficult and subjective (http://dx.doi.org/10.1109/ITAB.2009.5394295[4], http://dx.doi.org/10.1016/j.bspc.2011.02.001[5], http://dx.doi.org/10.1007/978-3-540-89208-3_300. [6]). This limitation is mainly based on the fact that the underlying artifact-free brain signal is unknown, so there is no objective way to measure how close the retrieved signal is to the real one. This article solves the aforementioned problem by presenting a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts, using a realistic head model. The significant part of this dataset is that it contains the pre-contamination EEG signals, so the brain signals underlying the EOG artifacts are known and thus the performance of every artifact rejection technique can be objectively assessed.
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Banville H, Falk T. Recent advances and open challenges in hybrid brain-computer interfacing: a technological review of non-invasive human research. BRAIN-COMPUTER INTERFACES 2016. [DOI: 10.1080/2326263x.2015.1134958] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface. Clin Neurophysiol 2016. [DOI: 10.1016/j.clinph.2015.06.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Islam MK, Rastegarnia A, Nguyen AT, Yang Z. Artifact characterization and removal for in vivo neural recording. J Neurosci Methods 2014; 226:110-123. [DOI: 10.1016/j.jneumeth.2014.01.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Revised: 01/22/2014] [Accepted: 01/23/2014] [Indexed: 11/25/2022]
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Yong X, Fatourechi M, Ward RK, Birch GE. Adaptive classification in a self-paced hybrid brain-computer interface system. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:3274-3279. [PMID: 23366625 DOI: 10.1109/embc.2012.6346664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
As the characteristics of EEG signals change over time, updating the classifier of a brain computer interface, BCI, (over time) would improve the performance of the system. Developing an adaptive classifier for a self-paced BCI however is not easy because the user's intention (and therefore the true labels of the EEG signals) are not known during the operation of the system. For certain applications, it may be possible to predict the labels of some of the EEG segments using some information about the user's state (e.g., the error potentials or gaze information). This study proposes a method that adaptively updates the classifier of a self-paced BCI in a supervised or semi-supervised manner, using those EEG segments whose labels can be predicted. We employ the eye position information obtained from an eye-tracker to predict the EEG labels. This eye-tracker is also used along with a self-paced BCI to form a hybrid BCI system. The results obtained from seven individuals show that the proposed algorithm outperforms the non-adaptive and other unsupervised adaptive classifiers. It achieves a true positive rate of 49.7% and lowers the number of false positives significantly to only 2.2 FPs/minute.
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Affiliation(s)
- Xinyi Yong
- Department of Electrical and Computer Engineering, University of British Columbia, 2356 Main Mall, Vancouver, BC, Canada.
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