What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
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 most cited work include:
- Underdetermined blind source separation based on sparse representation (292 citations)
- Analysis of sparse representation and blind source separation (268 citations)
- An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential (208 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (50.00%)
- Pattern recognition (27.86%)
- Brain–computer interface (27.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (50.00%)
- Pattern recognition (27.86%)
- Electroencephalography (12.86%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- MMAN: Multi-modality aggregation network for brain segmentation from MR images (16 citations)
- Dilated-Inception Net: Multi-Scale Feature Aggregation for Cardiac Right Ventricle Segmentation (12 citations)
- A novel multi-step Q-learning method to improve data efficiency for deep reinforcement learning (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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
- Segmentation and related Feature and Multi modality,
- Event-related potential, Ensemble averaging and Restricted Boltzmann machine most often made with reference to Discriminative model.
His Motor imagery research integrates issues from Speech recognition, Electrooculography, Asynchronous communication and Web browser.
His work on Hybrid brain computer interface as part of general Brain–computer interface study is frequently linked to Prosthetic limb, therefore connecting diverse disciplines of science.
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