What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Telecommunications
The scientist’s investigation covers issues in Brain–computer interface, Speech recognition, Electroencephalography, Visual evoked potentials and Artificial intelligence.
His Brain–computer interface study combines topics in areas such as Information transfer, Human–computer interaction and Signal processing.
His Speech recognition research is multidisciplinary, incorporating elements of Training set and Perceptron.
The concepts of his Electroencephalography study are interwoven with issues in Computer monitor, Communication channel, Steady state, Linear discriminant analysis and Neural engineering.
His research in Visual evoked potentials intersects with topics in Stimulus, Component analysis, Refresh rate and Evoked potential.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Computer vision and Pattern recognition.
His most cited work include:
- A practical VEP-based brain-computer interface (401 citations)
- High-speed spelling with a noninvasive brain–computer interface (330 citations)
- High-speed spelling with a noninvasive brain–computer interface (330 citations)
What are the main themes of his work throughout his whole career to date?
Yijun Wang focuses on Brain–computer interface, Electroencephalography, Artificial intelligence, Speech recognition and Computer vision.
Yijun Wang has included themes like Stimulus, Visual perception, Steady state, Evoked potential and Visual evoked potentials in his Brain–computer interface study.
His research on Electroencephalography also deals with topics like
- Human–computer interaction which intersects with area such as Simulation,
- Signal which connect with Visual cortex.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Filter bank, Signal processing and Pattern recognition.
Modulation is closely connected to Information transfer in his research, which is encompassed under the umbrella topic of Speech recognition.
His work on Gaze as part of general Computer vision research is often related to User interface, thus linking different fields of science.
He most often published in these fields:
- Brain–computer interface (80.98%)
- Electroencephalography (56.44%)
- Artificial intelligence (44.17%)
What were the highlights of his more recent work (between 2018-2021)?
- Brain–computer interface (80.98%)
- Electroencephalography (56.44%)
- Artificial intelligence (44.17%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Brain–computer interface, Electroencephalography, Artificial intelligence, Pattern recognition and Evoked potential.
Yijun Wang combines subjects such as Stimulus, Speech recognition, Flicker, Decoding methods and Visual evoked potentials with his study of Brain–computer interface.
His Speech recognition research incorporates elements of Character, Information transfer and Task.
His Electroencephalography study incorporates themes from Acoustics, Rapid serial visual presentation, Output device, Usability and Signal.
His work on Computer vision expands to the thematically related Artificial intelligence.
His work in Evoked potential covers topics such as Steady state which are related to areas like Neuroscience and Visual perception.
Between 2018 and 2021, his most popular works were:
- Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm. (28 citations)
- Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components (25 citations)
- Enhance decoding of pre-movement EEG patterns for brain-computer interfaces. (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Telecommunications
Yijun Wang mainly focuses on Brain–computer interface, Electroencephalography, Artificial intelligence, Pattern recognition and Speech recognition.
His work deals with themes such as Spatial filter, Visual evoked potentials, Task and Human–computer interaction, which intersect with Brain–computer interface.
His Electroencephalography study integrates concerns from other disciplines, such as Face and Biometrics.
His study connects Computer vision and Artificial intelligence.
The study incorporates disciplines such as Signal-to-noise ratio, Decoding methods and Pattern matching in addition to Pattern recognition.
His research on Speech recognition also deals with topics like
- Evoked potential together with Information transfer,
- Steady state, which have a strong connection to Flicker, Waveform and Harmonic.
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