His main research concerns Brain–computer interface, Speech recognition, Artificial intelligence, Pattern recognition and Feature extraction. In his research, Bayesian probability is intimately related to Overfitting, which falls under the overarching field of Brain–computer interface. His Speech recognition research incorporates elements of Oddball paradigm, Event-related potential, Electroencephalography and Canonical correlation.
His Artificial intelligence study integrates concerns from other disciplines, such as Control system and Column. His work on Statistical classification and Naive Bayes classifier as part of general Pattern recognition research is frequently linked to Interference and Flash, thereby connecting diverse disciplines of science. The Feature extraction study combines topics in areas such as Linear discriminant analysis and Motor imagery.
His primary scientific interests are in Brain–computer interface, Artificial intelligence, Pattern recognition, Speech recognition and Electroencephalography. His work on Motor imagery as part of general Brain–computer interface research is frequently linked to Information transfer, bridging the gap between disciplines. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Computer vision.
His work is dedicated to discovering how Pattern recognition, Bayesian probability are connected with Regularization and other disciplines. His biological study deals with issues like N400, which deal with fields such as Mismatch negativity. His work in Electroencephalography covers topics such as Human–computer interaction which are related to areas like Tactile stimuli and Control system.
His primary areas of investigation include Brain–computer interface, Artificial intelligence, Pattern recognition, Electroencephalography and Feature extraction. His work in the fields of Motor imagery overlaps with other areas such as Information transfer. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Reduction and Distributed parameter system.
In the field of Pattern recognition, his study on Support vector machine overlaps with subjects such as Bonferroni correction, Colored, Communication channel and Signal processing. Jing Jin has included themes like Evoked potential and Convolutional neural network in his Electroencephalography study. His Feature extraction research incorporates themes from Dempster–Shafer theory, Feature, Outlier, Feature vector and Feature selection.
Brain–computer interface, Artificial intelligence, Electroencephalography, Pattern recognition and Speech recognition are his primary areas of study. His Brain–computer interface study combines topics from a wide range of disciplines, such as Motor behavior and Mental representation. His study in Feature extraction and Linear discriminant analysis is carried out as part of his Artificial intelligence studies.
The study incorporates disciplines such as Dempster–Shafer theory, Feature, Outlier, Feature vector and Feature selection in addition to Feature extraction. His studies deal with areas such as Evoked potential, Statistical classification, Discriminative model and Pattern matching as well as Electroencephalography. His work deals with themes such as Visual field, Adjacency list and Visual angle, which intersect with Speech recognition.
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Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis.
Yu Zhang;Guoxu Zhou;Jing Jin;Xingyu Wang.
International Journal of Neural Systems (2014)
Sparse Bayesian Classification of EEG for Brain–Computer Interface
Yu Zhang;Guoxu Zhou;Jing Jin;Qibin Zhao.
IEEE Transactions on Neural Networks (2016)
L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI
Yu Zhang;Guoxu Zhou;Jing Jin;Minjue Wang.
international conference of the ieee engineering in medicine and biology society (2013)
Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface
Yu Zhang;Guoxu Zhou;Jing Jin;Xingyu Wang.
Journal of Neuroscience Methods (2015)
Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces
Yu Zhang;Yu Wang;Guoxu Zhou;Jing Jin.
Expert Systems With Applications (2018)
Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI
Yu Zhang;Chang S. Nam;Guoxu Zhou;Jing Jin.
IEEE Transactions on Systems, Man, and Cybernetics (2019)
Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.
Yu Zhang;Yu Wang;Jing Jin;Xingyu Wang.
International Journal of Neural Systems (2017)
An adaptive P300-based control system.
Jing Jin;Brendan Zachary Allison;Eric Sellers;Clemens Brunner.
Journal of Neural Engineering (2011)
Correlation-based channel selection and regularized feature optimization for MI-based BCI
Jing Jin;Yangyang Miao;Ian Daly;Cili Zuo.
Neural Networks (2019)
A novel BCI based on ERP components sensitive to configural processing of human faces
Yu Zhang;Qibin Zhao;Jing Jin;Xingyu Wang.
Journal of Neural Engineering (2012)
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