His scientific interests lie mostly in Artificial intelligence, Brain–computer interface, Computer vision, Graphical user interface and Sparse approximation. Artificial intelligence is frequently linked to Pattern recognition in his study. The Motor imagery research Yuanqing Li does as part of his general Brain–computer interface study is frequently linked to other disciplines of science, such as User interface, therefore creating a link between diverse domains of science.
His research in Motor imagery tackles topics such as Cursor which are related to areas like Speech recognition, Beta Rhythm and Motor control. His studies in Sparse approximation integrate themes in fields like Cluster analysis, Voxel, Source separation and Blind signal separation. His Blind signal separation study incorporates themes from Sparse PCA, Underdetermined system and Linear programming, Mathematical optimization.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Brain–computer interface, Algorithm and Computer vision. His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Training set. His Pattern recognition research incorporates themes from Artificial neural network, Algorithm design and Bayesian inference.
His research in the fields of Motor imagery overlaps with other disciplines such as Graphical user interface. Yuanqing Li combines subjects such as Independent component analysis and Blind signal separation with his study of Algorithm. He applies his multidisciplinary studies on Blind signal separation and Sparse matrix in his research.
Yuanqing Li focuses on Artificial intelligence, Pattern recognition, Electroencephalography, Brain–computer interface and Feature extraction. Artificial intelligence and Machine learning are frequently intertwined in his study. His Feature vector study in the realm of Pattern recognition interacts with subjects such as Symmetric matrix.
His study in Electroencephalography is interdisciplinary in nature, drawing from both Cued speech and Precuneus. His Motor imagery study, which is part of a larger body of work in Brain–computer interface, is frequently linked to Stereoelectroencephalography, bridging the gap between disciplines. His Feature extraction research is multidisciplinary, incorporating elements of Segmentation and Decoding methods.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature extraction, Motor imagery and Brain–computer interface. His Artificial intelligence study frequently draws connections to other fields, such as Machine learning. He usually deals with Pattern recognition and limits it to topics linked to Selection and Optimization problem, Communication channel, Filter and Relevance.
His Feature extraction research also works with subjects such as
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Underdetermined blind source separation based on sparse representation
Yuanqing Li;S. Amari;A. Cichocki;D.W.C. Ho.
IEEE Transactions on Signal Processing (2006)
Analysis of sparse representation and blind source separation
Yuanqing Li;Andrzej Cichocki;Shun-ichi Amari.
Neural Computation (2004)
A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control
Yuanqing Li;Jiahui Pan;Fei Wang;Zhuliang Yu.
IEEE Transactions on Biomedical Engineering (2013)
An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential
Yuanqing Li;Jinyi Long;Tianyou Yu;Zhuliang Yu.
IEEE Transactions on Biomedical Engineering (2010)
A Hybrid Brain Computer Interface to Control the Direction and Speed of a Simulated or Real Wheelchair
Jinyi Long;Yuanqing Li;Hongtao Wang;Tianyou Yu.
international conference of the ieee engineering in medicine and biology society (2012)
A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system
Yuanqing Li;Cuntai Guan;Huiqi Li;Zhengyang Chin.
Pattern Recognition Letters (2008)
Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation
Rui Zhang;Yuanqing Li;Yongyong Yan;Hao Zhang.
international conference of the ieee engineering in medicine and biology society (2016)
Probabilistic Common Spatial Patterns for Multichannel EEG Analysis
Wei Wu;Zhe Chen;Xiaorong Gao;Yuanqing Li.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)
Noninvasive BCIs: Multiway Signal-Processing Array Decompositions
A. Cichocki;Y. Washizawa;T. Rutkowski;H. Bakardjian.
IEEE Computer (2008)
Deep learning based on Batch Normalization for P300 signal detection
Mingfei Liu;Wei Wu;Zhenghui Gu;Zhuliang Yu.
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