Neural activities classification of left and right finger gestures during motor execution and motor imagery

References

• Zhang R, Yao D, Valdés-Sosa PA, et al. Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng. 2015;12(6):066024.
   PubMed Web of Science ®Google Scholar
• Qiang G, Lixiang D, A N B, et al. Noninvasive Electroencephalogram based control of a robotic arm for writing task using hybrid BCI system. Biomed Res Int. 2017;2017:1–8.
   Web of Science ®Google Scholar
• A N B, Nishio S, Suzuki T, et al. Neuromagnetic decoding of simultaneous bilateral hand movements for multidimensional brain-machine interfaces. IEEE Trans Neural Syst Rehabil Eng. 2018;26(6):1301–1310.
   PubMed Web of Science ®Google Scholar
• Belkacem AN, Ishiguro H, Nishio S, et al., “Real-Time MEG-based brain-geminoid control using single-trial SVM classification,” 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), Singapore, 2018, pp. 679–684. doi: https://doi.org/10.1109/ICARM.2018.8610776
   Google Scholar
• Belkacem AN, Nishio S, Suzuki T, et al., “Neuromagnetic Geminoid control by BCI Based on four bilateral hand movements,” 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, pp. 524–527. doi: https://doi.org/10.1109/SMC.2018.00099
   Google Scholar
• Chen C, Shin D, Watanabe H, et al. Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex. Neurosci Res. 2014;83:1–7.
   PubMed Web of Science ®Google Scholar
• Yasuhiko N, Takufumi Y, Duk S, et al. Decoding fingertip trajectory from electrocorticographic signals in humans. Neurosci Res. 2014;85:20–27.
   PubMed Web of Science ®Google Scholar
• Yunfa F, Xiong X, Jiang C, et al. Imagined hand clenching force and speed modulate brain activity and are classified by NIRS combined with EEG. IEEE Trans Neural Syst Rehabil Eng. 2017;25(9):1641–1652.
   PubMed Web of Science ®Google Scholar
• Wen D, Jia P, Hsu S-H, et al. Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual information. Neural Networks. 2019;110:159–169.
   PubMed Web of Science ®Google Scholar
• LaFleur K, Cassady K, Doud A, et al. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface. J Neural Eng. 2013;10(4):046003.
   PubMed Web of Science ®Google Scholar
• Vuckovic A, Osuagwu BA. Using a motor imagery questionnaire to estimate the performance of a brain–computer interface based on object oriented motor imagery. Clin Neurophysiol. 2013;124(8):1586–1595.
   PubMed Web of Science ®Google Scholar
• Nagao M, Maki A, Hayashi H, et al. High cognitive function of an ALS patient in the totally locked-in state. Neurosci Lett. 2008;435(2):85–89.
   PubMed Web of Science ®Google Scholar
• Yi-Hung L, Shiuan H, Yi-De H. Motor Imagery EEG classification for patients with amyotrophic lateral sclerosis using fractal dimension and Fisher’s criterion-based channel selection. Sensors. 2017;17(7):1557.
   Web of Science ®Google Scholar
• Bajaj S, Butler AJ, Drake D, et al. Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation. NeuroImage Clin. 2015;8:572–582.
   PubMed Web of Science ®Google Scholar
• Gomez-Pilar J, Corralejo R, Nicolas-Alonso LF, et al. Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly. Med Biol Eng Comput. 2016;54(11):1655–1666.
   PubMed Web of Science ®Google Scholar
• Chen C, Zhang J, Belkacem AN, et al. G-Causality brain connectivity differences of finger movements between motor execution and motor imagery. J Healthc Eng. 2019(3):1–12.
   Web of Science ®Google Scholar
• Leeb R, Friedman D, Muller-Putz GR, et al. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci. 2007(2007):1–8.
   Google Scholar
• Mousavi M, A S K, Zhang Q, et al. Improving motor imagery BCI with user response to feedback. Brain-Comput Interfaces. 2017, 4(1–2):74–86.
   Web of Science ®Google Scholar
• Ahn M, Jun SC. Performance variation in motor imagery brain–computer interface: a brief review. J Neurosci Methods. 2015, 243:103–110.
   Web of Science ®Google Scholar
• Weibo Y, Qiu S, Hongzhi Q, et al. EEG feature comparison and classification of simple and compound limb motor imagery. J Neuroeng Rehabil. 2013 October 12;10(1):106.
   PubMedGoogle Scholar
• Xu M, Han J, Wang Y, et al.Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features. IEEE Trans Biomed Eng. 2020;1. Early Access Article. doi:https://doi.org/10.1109/TBME.2020.2975614.
   Google Scholar
• Xu M, Xiao X, Wang Y, et al. A brain-computer interface based on miniature event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng. 2018;65(5):1166–1175.
   PubMed Web of Science ®Google Scholar
• Sumi K, Yabuki K, Tiam-Lee TJ, et al. A cooperative game using the P300 EEG-Based Brain-Computer interface. IntechOpen. 2019 April 18th; DOI:https://doi.org/10.5772/intechopen.84621.
   Google Scholar
• J N M, Arbel Y, J W M, Bianchi L and Erdogmus D. et al. Optimizing the P300-based brain-computer interface: current status, limitations and future directions. J Neural Eng. 2011;8(2):025003.
   PubMed Web of Science ®Google Scholar
• A N B, Saetia S, Zintus-art K, et al. Real-time control of a video game using eye movements and two temporal EEG sensors. Comput Intell Neurosci. 2015;2015:1–10.
   Web of Science ®Google Scholar
• Bauer R, Fels M, Vukelić M, et al. Bridging the gap between motor imagery and motor execution with a brain-robot interface. Neuroimage. 2015;108:319–327.
   PubMed Web of Science ®Google Scholar
• Hanakawa T, M A D, Motor Planning HM. Imagery, and execution in the distributed motor network: a time-course study with functional MRI. Cereb Cortex. 2008;18(12):2775–2788.
   PubMed Web of Science ®Google Scholar
• SL S, Hlustik P, EE C, et al. Fine modulation in network activation during motor execution and motor imagery. Cereb Cortex. 2004;14(11):1246–1255.
   PubMed Web of Science ®Google Scholar
• Dimyan MA, Hanakawa T, Hallett M. Motor planning, imagery, and execution in the distributed motor network: a time-course study with functional MRI. Cereb Cortex. 2008;18(12):2775–2788.
   PubMed Web of Science ®Google Scholar
• Yunfa F, Chen J, Xiong X. Calculation and analysis of microstate related to variation in executed and imagined movement of force of hand clenching. Comput Intell Neurosci. 2018;9270685. DOI:https://doi.org/10.1155/2018/9270685
   Google Scholar
• Hayashi T, Yokoyama H, Nambu I, et al.,Prediction of individual finger movements for motor execution and imagery: an EEG study. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, 2017, pp. 3020–3023.
   Google Scholar
• Szameitat AJ, Shen S, Conforto A, et al. Cortical activation during executed, imagined, observed, and passive wrist movements in healthy volunteers and stroke patients. NeuroImage. 2012;62(1):266–280.
   PubMed Web of Science ®Google Scholar
• Toriyama H, Ushiba J, Ushiyama J. Subjective vividness of kinesthetic motor imagery is associated with the similarity in magnitude of sensorimotor event-related desynchronization between motor execution and motor imagery. Front Hum Neurosci. 2018;12:295.
   PubMed Web of Science ®Google Scholar
• Galdo Alvarez S, Carrillo de la Pena MT, Lastra Barreira C. Limb (hand vs. foot) and response conflict have similar effects on event-related potentials (ERPs) recorded during motor imagery and overt execution. Eur J Neurosci. 2006;24(2):635–643.
   PubMed Web of Science ®Google Scholar
• Penaloza C, Nishio S. BMI control of a third arm for multitasking. Sci Rob. 2018;3(20):eaat1228.
   PubMed Web of Science ®Google Scholar
• Shota Y, Takashi K, Tomoyoshi S, et al. Interactive holographic display based on finger gestures. Sci Rep. 2018, 8(1):2010.
   Web of Science ®Google Scholar
• Jarrasse N, Nicol C, Touillet A, et al. Classification of phantom finger, hand, wrist, and elbow voluntary gestures in transhumeral amputees with sEMG. IEEE Trans Neural Syst Rehabil Eng. 2017;25(1):68–77.
   PubMed Web of Science ®Google Scholar
• Dong E, Li C, Li L, et al. Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain–computer interfaces. Med Biol Eng Comput. 2017;55(10):1809–1818.
   PubMed Web of Science ®Google Scholar
• Chen C, Xiaomin L, Belkacem AN, et al. “The mixed kernel function SVM-based point cloud classification”. Int J Precis Eng Manuf. 2019;20(5):737–747.
   Web of Science ®Google Scholar
• Chen C, Zhang J, Belkacem AN, et al. G-Causality brain connectivity differences of finger movements between motor execution and motor imagery. J Healthcal Eng. .
   Web of Science ®Google Scholar
• Ang KK, Chin ZY, Zhang H, et al. Filter bank common spatial pattern (FBCSP) in brain–computer interface. IEEE international joint conference on neural networks, Hong Kong, China, 2008 pp 2390–2397
   Google Scholar
• Schl¨ogl A, Lee F, Bischof H, et al. Characterization of four-class motor imagery EEG data for the BCIcompetition 2005. J Neural Eng. 2005;2(4):14–22.
   PubMed Web of Science ®Google Scholar
• Lehtonen J, Jylänki P, Kauhanen L, et al. Online classification of single EEG trials during finger movements. IEEE Trans Biomed Eng. 2018;55(2 Pt 1):713–720.
   Google Scholar
• Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–791.
   PubMed Web of Science ®Google Scholar
• Frisch S, Förstl S, Legler A, et al. The interleaving of actions in everyday life multitasking demands. J Neuropsychol. 2012;6(2):257–269.
   PubMedGoogle Scholar
• Shao L, Zhang L, Belkacem AN, et al. EEG-Controlled wall-crawling cleaning robot using SSVEP-based brain-computer interface. J Healthc Eng. 2020;2020:1–11.
   Web of Science ®Google Scholar
• Chen C, Zhou P, Belkacem AN, et al. Quadcopter robot control based on hybrid brain–computer interface system. Sens Mater. 2020;32(3):991–1004.
   Web of Science ®Google Scholar
• Wang K, Xu M, Wang Y, et al. Enhance decoding of pre-movement EEG patterns for brain–computer interfaces. J Neural Eng. 2020;17(1):016033.
   PubMed Web of Science ®Google Scholar
• Xiao X, Xu M, Jin J, et al.Discriminative canonical pattern matching for single-trial classification of ERP components. IEEE Trans Biomed Eng. 2019;1. Early Access Article. doi:https://doi.org/10.1109/TBME.2019.2958641.
   Google Scholar