What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Neuroscience
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 most cited work include:
- Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. (219 citations)
- Sparse Bayesian Classification of EEG for Brain–Computer Interface (160 citations)
- Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface (151 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Brain–computer interface (81.90%)
- Artificial intelligence (56.03%)
- Pattern recognition (42.24%)
What were the highlights of his more recent work (between 2019-2021)?
- Brain–computer interface (81.90%)
- Artificial intelligence (56.03%)
- Pattern recognition (42.24%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface (31 citations)
- Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components (25 citations)
- Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Neuroscience
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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