What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Neuroscience
Xiaorong Gao mainly investigates Brain–computer interface, Electroencephalography, Speech recognition, Artificial intelligence and Computer vision.
His research investigates the connection between Brain–computer interface and topics such as Information transfer that intersect with issues in Modulation, Joint and Demodulation.
His Electroencephalography study combines topics in areas such as Stimulus, Neural engineering, Visual Objects and Communication.
His work carried out in the field of Speech recognition brings together such families of science as Neurophysiology, Luminance, Linear discriminant analysis, Feature extraction and Visual evoked potentials.
The concepts of his Artificial intelligence study are interwoven with issues in Adaptive filter and Pattern recognition.
His Computer vision research is multidisciplinary, relying on both Evoked potential and Computer hardware.
His most cited work include:
- Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs (600 citations)
- Design and implementation of a brain-computer interface with high transfer rates (599 citations)
- An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method (515 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Brain–computer interface, Electroencephalography, Artificial intelligence, Speech recognition and Pattern recognition.
His Brain–computer interface study integrates concerns from other disciplines, such as Information transfer, Stimulus, Canonical correlation, Evoked potential and Visual evoked potentials.
His Electroencephalography research is multidisciplinary, incorporating perspectives in Visual perception, Cognitive psychology and Audiology.
The study incorporates disciplines such as Signal, Signal processing and Computer vision in addition to Artificial intelligence.
Xiaorong Gao focuses mostly in the field of Speech recognition, narrowing it down to topics relating to Motor imagery and, in certain cases, Neurophysiology.
His Pattern recognition research includes themes of Rapid serial visual presentation and Spatial filter.
He most often published in these fields:
- Brain–computer interface (57.83%)
- Electroencephalography (44.58%)
- Artificial intelligence (41.57%)
What were the highlights of his more recent work (between 2018-2021)?
- Brain–computer interface (57.83%)
- Artificial intelligence (41.57%)
- Electroencephalography (44.58%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Brain–computer interface, Artificial intelligence, Electroencephalography, Pattern recognition and Speech recognition.
His biological study spans a wide range of topics, including Encoding, Stimulus, Flicker, Evoked potential and Visual evoked potentials.
His Artificial intelligence study frequently draws connections to adjacent fields such as Computer vision.
His research integrates issues of Rapid serial visual presentation and Positive emotion in his study of Electroencephalography.
His Pattern recognition research is multidisciplinary, incorporating elements of Visualization, Spatial filter and Signal, Equalization.
The various areas that Xiaorong Gao examines in his Speech recognition study include Statistical classification and Task.
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)
- A novel system of SSVEP-based human–robot coordination (7 citations)
- Boosting the Information Transfer Rate of an SSVEP-BCI System Using Maximal-Phase-Locking Value and Minimal-Distance Spatial Filter Banks (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Neuroscience
His scientific interests lie mostly in Brain–computer interface, Artificial intelligence, Pattern recognition, Spatial filter and Evoked potential.
His work deals with themes such as Robot, Speech recognition and Human–computer interaction, which intersect with Brain–computer interface.
His studies in Speech recognition integrate themes in fields like Statistical classification and Task.
He connects Artificial intelligence with Software portability in his study.
Xiaorong Gao combines subjects such as Stimulus, Decoding methods, Visual evoked potentials and Visual motion with his study of Pattern recognition.
His work deals with themes such as Electroencephalography, Flicker, Visual perception, Steady state and Waveform, which intersect with Evoked potential.
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