Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential
Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually...
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| Published in | Journal of neural engineering Vol. 20; no. 1; pp. 16029 - 16045 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
IOP Publishing
01.02.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1741-2560 1741-2552 1741-2552 |
| DOI | 10.1088/1741-2552/acb102 |
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| Abstract | Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain–computer interface (BCI). Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments. Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments. Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use. |
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| AbstractList | . Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).
. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.
. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments.
. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use. Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments.Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments.Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use. Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain–computer interface (BCI). Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments. Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments. Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use. |
| Author | Zhang, Qiuxiang Yu, Yunhui Zheng, Xiaowei Jia, Yagang Tao, Tangfei Xu, Guanghua Chen, Longting Liang, Renghao Gao, Yuxiang Chen, Ruiquan |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36608339$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/IJCNN.2008.4634130 10.1016/j.cogbrainres.2005.08.014 10.1109/ICPR.2010.904 10.1109/TBME.2014.2312397 10.3389/fneng.2014.00019 10.1007/s11571-021-09721-x 10.1111/j.1460-9568.2008.06271.x 10.1016/j.clinph.2013.03.006 10.1109/TNSRE.2010.2053387 10.3389/fnhum.2021.741709 10.1109/TNSRE.2017.2755018 10.1109/ACCESS.2019.2939623 10.3390/s19061423 10.1109/EMBC.2013.6610736 10.1016/j.neunet.2011.05.006 10.1016/j.neulet.2009.06.045 10.1109/TAI.2021.3097307 10.1016/j.neures.2017.10.002 10.1016/j.eswa.2020.114031 10.1016/j.clinph.2008.11.015 10.1049/el.2020.2509 10.1109/JAS.2017.7510616 10.1016/S1364-6613(99)01312-1 10.1109/tim.2020.3020682 10.1007/s10548-014-0382-6 10.1109/TCDS.2020.3040438 10.1016/S0301-0511(99)00031-9 10.3389/fnins.2014.00208 10.1016/j.artmed.2013.07.004 10.1080/2326263X.2017.1303253 10.1109/LRA.2021.3114418 10.1109/TBME.2004.827072 10.1038/s41598-017-17682-7 10.1145/3126686.3129334 10.1109/ACCESS.2021.3049469 10.1109/CBMS.2017.113 10.1088/1741-2552/aae069 10.1016/j.neuroimage.2010.03.022 10.1109/ACCESS.2019.2944067 10.1016/j.neuropsychologia.2018.04.016 10.1016/j.humov.2014.08.014 10.1371/journal.pone.0047048 10.1002/ana.23879 10.1016/j.cmpb.2016.02.020 10.1109/TBME.2007.908083 10.1016/S1388-2457(00)00457-0 10.1109/EMBC.2019.8857192 10.1007/s10916-012-9893-4 10.1109/86.895946 10.1109/TBME.2010.2082540 10.1016/j.bspc.2021.103342 10.3389/fnhum.2016.00692 10.1016/j.neuropsychologia.2019.107206 10.1109/ICARCV.2018.8581088 10.1016/j.jneumeth.2009.01.015 10.1016/j.clinph.2011.01.050 10.3390/s20185283 10.1109/TBME.2005.851521 |
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| Keywords | brain–computer interface neural rehabilitation online adaptive classification error related potential motor imagery training |
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| References | Kumar (jneacb102bib38) 2019; 7 Ono (jneacb102bib22) 2013; 124 Lu (jneacb102bib53) 2010; 57 Yang (jneacb102bib52) 2016; 129 Schalk (jneacb102bib25) 2000; 111 Omedes (jneacb102bib46) 2013 Ramos-Murguialday (jneacb102bib17) 2012; 7 Lotte (jneacb102bib51) 2010 Llera (jneacb102bib47) 2011; 24 Yu (jneacb102bib59) 2021; 70 Ono (jneacb102bib15) 2018; 114 Rohm (jneacb102bib6) 2013; 59 Ang (jneacb102bib50) 2008 Liang (jneacb102bib36) 2021; 9 Ramoser (jneacb102bib48) 2000; 8 Blankertz (jneacb102bib12) 2010; 51 Sadiq (jneacb102bib58) 2020; 56 Ono (jneacb102bib8) 2014; 7 Yoxon (jneacb102bib55) 2019; 134 Sadiq (jneacb102bib5) 2021; 164 Schalk (jneacb102bib1) 2004; 51 Ehrlich (jneacb102bib27) 2018; 15 Guger (jneacb102bib11) 2009; 462 Lu (jneacb102bib54) 2011; 122 Ramos-Murguialday (jneacb102bib7) 2013; 74 Friesen (jneacb102bib16) 2016; 10 Kondo (jneacb102bib21) 2015; 43 Tani (jneacb102bib9) 2018; 133 Yuan (jneacb102bib2) 2014; 61 Bhattacharyya (jneacb102bib30) 2017; 4 Kim (jneacb102bib28) 2017; 7 Kalaganis (jneacb102bib33) 2017 Neuper (jneacb102bib18) 2009; 120 Ono (jneacb102bib23) 2015; 28 Hwang (jneacb102bib19) 2009; 179 di Rienzo (jneacb102bib14) 2021; 15 Cruz (jneacb102bib32) 2018; 26 Sadiq (jneacb102bib34) 2019; 7 Padfield (jneacb102bib42) 2019; 19 Neuper (jneacb102bib4) 2005; 25 Chavarriaga (jneacb102bib45) 2014; 8 Ferrez (jneacb102bib26) 2008; 55 Falkenstein (jneacb102bib37) 2000; 51 Xu (jneacb102bib57) 2022; 16 Chavarriaga (jneacb102bib39) 2010; 18 Lemm (jneacb102bib49) 2005; 52 Li (jneacb102bib56) 2022; 72 Sadiq (jneacb102bib20) 2020; 20 Orgs (jneacb102bib13) 2008; 27 Sadiq (jneacb102bib24) 2022; 14 Decety (jneacb102bib10) 1999; 3 Sadiq (jneacb102bib3) 2021; 2 Liang (jneacb102bib35) 2021; 7 Ferrez (jneacb102bib40) 2008 Rodriguez-Bermudez (jneacb102bib44) 2012; 36 Zhang (jneacb102bib29) 2018 Schiatti (jneacb102bib43) 2019 Mousavi (jneacb102bib31) 2017; 4 Oikonomou (jneacb102bib41) 2017 |
| References_xml | – start-page: 2390 year: 2008 ident: jneacb102bib50 article-title: Filter bank common spatial pattern (FBCSP) in brain-computer interface doi: 10.1109/IJCNN.2008.4634130 – volume: 25 start-page: 668 year: 2005 ident: jneacb102bib4 article-title: Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG publication-title: Brain Res. Cogn. Brain Res. doi: 10.1016/j.cogbrainres.2005.08.014 – start-page: 3712 year: 2010 ident: jneacb102bib51 article-title: Spatially regularized common spatial patterns for EEG classification doi: 10.1109/ICPR.2010.904 – volume: 61 start-page: 1425 year: 2014 ident: jneacb102bib2 article-title: Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2312397 – volume: 7 start-page: 19 year: 2014 ident: jneacb102bib8 article-title: Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke publication-title: Front. Neuroeng. doi: 10.3389/fneng.2014.00019 – volume: 16 start-page: 379 year: 2022 ident: jneacb102bib57 article-title: A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network publication-title: Cogn. Neurodyn. doi: 10.1007/s11571-021-09721-x – volume: 27 start-page: 3380 year: 2008 ident: jneacb102bib13 article-title: Expertise in dance modulates alpha/beta event-related desynchronization during action observation publication-title: Eur. J. Neurosci. doi: 10.1111/j.1460-9568.2008.06271.x – volume: 124 start-page: 1779 year: 2013 ident: jneacb102bib22 article-title: Daily training with realistic visual feedback improves reproducibility of event-related desynchronisation following hand motor imagery publication-title: Clin. Neurophysiol. doi: 10.1016/j.clinph.2013.03.006 – volume: 18 start-page: 381 year: 2010 ident: jneacb102bib39 article-title: Learning from EEG error-related potentials in noninvasive brain-computer interfaces publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2010.2053387 – volume: 15 year: 2021 ident: jneacb102bib14 article-title: Stabilometric correlates of motor and motor imagery expertise publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2021.741709 – volume: 26 start-page: 26 year: 2018 ident: jneacb102bib32 article-title: Double ErrP detection for automatic error correction in an ERP-based BCI speller publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2017.2755018 – volume: 7 start-page: 127678 year: 2019 ident: jneacb102bib34 article-title: Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2939623 – volume: 19 start-page: 1423 year: 2019 ident: jneacb102bib42 article-title: EEG-based brain-computer interfaces using motor-imagery: techniques and challenges publication-title: Sensors doi: 10.3390/s19061423 – start-page: 5263 year: 2013 ident: jneacb102bib46 article-title: Using frequency-domain features for the generalization of EEG error-related potentials among different tasks doi: 10.1109/EMBC.2013.6610736 – volume: 24 start-page: 1120 year: 2011 ident: jneacb102bib47 article-title: On the use of interaction error potentials for adaptive brain computer interfaces publication-title: Neural Netw. doi: 10.1016/j.neunet.2011.05.006 – volume: 462 start-page: 94 year: 2009 ident: jneacb102bib11 article-title: How many people are able to control a P300-based brain-computer interface (BCI)? publication-title: Neurosci. Lett. doi: 10.1016/j.neulet.2009.06.045 – volume: 2 start-page: 314 year: 2021 ident: jneacb102bib3 article-title: Toward the development of versatile brain–computer interfaces publication-title: IEEE Trans. Artif. Intell. doi: 10.1109/TAI.2021.3097307 – volume: 133 start-page: 7 year: 2018 ident: jneacb102bib9 article-title: Action observation facilitates motor cortical activity in patients with stroke and hemiplegia publication-title: Neurosci. Res. doi: 10.1016/j.neures.2017.10.002 – volume: 164 year: 2021 ident: jneacb102bib5 article-title: Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.114031 – volume: 120 start-page: 239 year: 2009 ident: jneacb102bib18 article-title: Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface publication-title: Clin. Neurophysiol. doi: 10.1016/j.clinph.2008.11.015 – volume: 56 start-page: 1367 year: 2020 ident: jneacb102bib58 article-title: Motor imagery BCI classification based on novel two‐dimensional modelling in empirical wavelet transform publication-title: Electron. Lett. doi: 10.1049/el.2020.2509 – volume: 4 start-page: 639 year: 2017 ident: jneacb102bib30 article-title: Motor imagery and error related potential induced position control of a robotic arm publication-title: IEEE/CAA J. Autom. Sin. doi: 10.1109/JAS.2017.7510616 – volume: 3 start-page: 172 year: 1999 ident: jneacb102bib10 article-title: Neural mechanisms subserving the perception of human actions publication-title: Trends Cogn. Sci. doi: 10.1016/S1364-6613(99)01312-1 – volume: 70 start-page: 1 year: 2021 ident: jneacb102bib59 article-title: A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/tim.2020.3020682 – volume: 28 start-page: 340 year: 2015 ident: jneacb102bib23 article-title: Multimodal sensory feedback associated with motor attempts alters BOLD responses to paralyzed hand movement in chronic stroke patients publication-title: Brain Topogr. doi: 10.1007/s10548-014-0382-6 – volume: 14 start-page: 375 year: 2022 ident: jneacb102bib24 article-title: A matrix determinant feature extraction approach for decoding motor and mental imagery EEG in subject-specific tasks publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/TCDS.2020.3040438 – volume: 51 start-page: 87 year: 2000 ident: jneacb102bib37 article-title: ERP components on reaction errors and their functional significance: a tutorial publication-title: Biol. Psychol. doi: 10.1016/S0301-0511(99)00031-9 – volume: 8 start-page: 208 year: 2014 ident: jneacb102bib45 article-title: Errare machinale est: the use of error-related potentials in brain-machine interfaces publication-title: Front. Neurosci. doi: 10.3389/fnins.2014.00208 – start-page: 197 year: 2008 ident: jneacb102bib40 article-title: Simultaneous real-time detection of motor imagery and error-related potentials for improved BCI accuracy – volume: 59 start-page: 133 year: 2013 ident: jneacb102bib6 article-title: Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2013.07.004 – volume: 4 start-page: 74 year: 2017 ident: jneacb102bib31 article-title: Improving motor imagery BCI with user response to feedback publication-title: Brain-Comput. Interfaces doi: 10.1080/2326263X.2017.1303253 – volume: 7 start-page: 1721 year: 2021 ident: jneacb102bib35 article-title: Fusing topology optimization and pseudo-rigid-body method for the development of a finger exoskeleton publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2021.3114418 – volume: 51 start-page: 1034 year: 2004 ident: jneacb102bib1 article-title: BCI2000: a general-purpose brain-computer interface (BCI) system publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2004.827072 – volume: 7 year: 2017 ident: jneacb102bib28 article-title: Intrinsic interactive reinforcement learning—using error-related potentials for real world human-robot interaction publication-title: Sci. Rep. doi: 10.1038/s41598-017-17682-7 – start-page: 262 year: 2017 ident: jneacb102bib33 article-title: A collaborative representation approach to detecting error-related potentials in SSVEP-BCIs doi: 10.1145/3126686.3129334 – volume: 9 start-page: 7814 year: 2021 ident: jneacb102bib36 article-title: A general arthropod joint model and its applications in modeling human robotic joints publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3049469 – start-page: 781 year: 2017 ident: jneacb102bib41 article-title: A comparison study on EEG signal processing techniques using motor imagery EEG data doi: 10.1109/CBMS.2017.113 – volume: 15 year: 2018 ident: jneacb102bib27 article-title: Human-agent co-adaptation using error-related potentials publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aae069 – volume: 51 start-page: 1303 year: 2010 ident: jneacb102bib12 article-title: Neurophysiological predictor of SMR-based BCI performance publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.03.022 – volume: 7 start-page: 142451 year: 2019 ident: jneacb102bib38 article-title: A review of error-related potential-based brain–computer interfaces for motor impaired people publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2944067 – volume: 114 start-page: 134 year: 2018 ident: jneacb102bib15 article-title: Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback publication-title: Neuropsychologia doi: 10.1016/j.neuropsychologia.2018.04.016 – volume: 43 start-page: 239 year: 2015 ident: jneacb102bib21 article-title: Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain-computer interface publication-title: Hum. Mov. Sci. doi: 10.1016/j.humov.2014.08.014 – volume: 7 year: 2012 ident: jneacb102bib17 article-title: Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses publication-title: PLoS One doi: 10.1371/journal.pone.0047048 – volume: 74 start-page: 100 year: 2013 ident: jneacb102bib7 article-title: Brain-machine interface in chronic stroke rehabilitation: a controlled study publication-title: Ann. Neurol. doi: 10.1002/ana.23879 – volume: 129 start-page: 21 year: 2016 ident: jneacb102bib52 article-title: Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2016.02.020 – volume: 55 start-page: 923 year: 2008 ident: jneacb102bib26 article-title: Error-related EEG potentials generated during simulated brain-computer interaction publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2007.908083 – volume: 111 start-page: 2138 year: 2000 ident: jneacb102bib25 article-title: EEG-based communication: presence of an error potential publication-title: Clin. Neurophysiol. doi: 10.1016/S1388-2457(00)00457-0 – start-page: 6750 year: 2019 ident: jneacb102bib43 article-title: The effect of vibrotactile feedback on ErrP-based adaptive classification of motor imagery doi: 10.1109/EMBC.2019.8857192 – volume: 36 start-page: S51 year: 2012 ident: jneacb102bib44 article-title: Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces publication-title: J. Med. Syst. doi: 10.1007/s10916-012-9893-4 – volume: 8 start-page: 441 year: 2000 ident: jneacb102bib48 article-title: Optimal spatial filtering of single trial EEG during imagined hand movement publication-title: IEEE Trans. Rehabil. Eng. doi: 10.1109/86.895946 – volume: 57 start-page: 2936 year: 2010 ident: jneacb102bib53 article-title: Regularized common spatial pattern with aggregation for EEG classification in small-sample setting publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2010.2082540 – volume: 72 year: 2022 ident: jneacb102bib56 article-title: Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2021.103342 – volume: 10 start-page: 692 year: 2016 ident: jneacb102bib16 article-title: Combined action observation and motor imagery neurofeedback for modulation of brain activity publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2016.00692 – volume: 134 year: 2019 ident: jneacb102bib55 article-title: Rapid motor cortical plasticity can be induced by motor imagery training publication-title: Neuropsychologia doi: 10.1016/j.neuropsychologia.2019.107206 – start-page: 1923 year: 2018 ident: jneacb102bib29 article-title: Research on command confirmation unit based on motor imagery EEG signal decoding feedback in brain-computer interface doi: 10.1109/ICARCV.2018.8581088 – volume: 179 start-page: 150 year: 2009 ident: jneacb102bib19 article-title: Neurofeedback-based motor imagery training for brain-computer interface (BCI) publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2009.01.015 – volume: 122 start-page: 1569 year: 2011 ident: jneacb102bib54 article-title: Reorganization of functional connectivity during the motor task using EEG time–frequency cross mutual information analysis publication-title: Clin. Neurophysiol. doi: 10.1016/j.clinph.2011.01.050 – volume: 20 start-page: 5283 year: 2020 ident: jneacb102bib20 article-title: Identification of motor and mental imagery EEG in two and multiclass subject-dependent tasks using successive decomposition index publication-title: Sensors doi: 10.3390/s20185283 – volume: 52 start-page: 1541 year: 2005 ident: jneacb102bib49 article-title: Spatio-spectral filters for improving the classification of single trial EEG publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2005.851521 |
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| Snippet | Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the... Objective . Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the... . Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity... |
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| SubjectTerms | Brain Brain-Computer Interfaces brain–computer interface Electroencephalography - methods error related potential Humans Imagery, Psychotherapy Imagination Motor Cortex motor imagery training neural rehabilitation online adaptive classification |
| Title | Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential |
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