What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Mechanical engineering
Keum-Shik Hong mainly focuses on Artificial intelligence, Functional near-infrared spectroscopy, Control theory, Brain–computer interface and Speech recognition.
Keum-Shik Hong combines subjects such as General linear model, Temporal resolution, Deception and Pattern recognition with his study of Artificial intelligence.
His Functional near-infrared spectroscopy research includes themes of Reduction, Brain mapping and Computer vision.
His work in Adaptive control, Exponential stability, Lyapunov function, Actuator and Control theory is related to Control theory.
His work deals with themes such as Brain activity and meditation, Linear discriminant analysis, Prefrontal cortex and Elementary cognitive task, which intersect with Brain–computer interface.
His research integrates issues of Auditory cortex, Artificial neural network, Electroencephalography, Lateralization of brain function and Decoding methods in his study of Speech recognition.
His most cited work include:
- fNIRS-based brain-computer interfaces: a review (460 citations)
- Robust adaptive boundary control of a flexible marine riser with vessel dynamics (299 citations)
- Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface (211 citations)
What are the main themes of his work throughout his whole career to date?
Keum-Shik Hong mainly investigates Control theory, Artificial intelligence, Functional near-infrared spectroscopy, Nonlinear system and Pattern recognition.
His Control theory research includes elements of Vibration and Control engineering.
His Artificial intelligence study integrates concerns from other disciplines, such as Brain activity and meditation, Brain–computer interface, Electroencephalography and Computer vision.
His studies in Brain–computer interface integrate themes in fields like Speech recognition, Signal and Support vector machine.
His Functional near-infrared spectroscopy study necessitates a more in-depth grasp of Prefrontal cortex.
His research investigates the connection between Adaptive control and topics such as Partial differential equation that intersect with issues in Boundary value problem.
He most often published in these fields:
- Control theory (41.70%)
- Artificial intelligence (19.81%)
- Functional near-infrared spectroscopy (13.40%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (19.81%)
- Functional near-infrared spectroscopy (13.40%)
- Control theory (41.70%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Functional near-infrared spectroscopy, Control theory, Pattern recognition and Brain–computer interface.
His biological study spans a wide range of topics, including Quadcopter, Brain activity and meditation, Electroencephalography and Computer vision.
His Functional near-infrared spectroscopy research is multidisciplinary, incorporating perspectives in Imaging phantom, Resting state fMRI and Neuroimaging.
His Control theory study combines topics in areas such as Vibration and Vibration control.
His work carried out in the field of Pattern recognition brings together such families of science as Feature and Finger tapping.
His research in Brain–computer interface intersects with topics in Speech recognition and Signal.
Between 2015 and 2021, his most popular works were:
- Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control. (112 citations)
- Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces (97 citations)
- Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review. (95 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Mechanical engineering
His primary areas of investigation include Functional near-infrared spectroscopy, Artificial intelligence, Control theory, Pattern recognition and Brain–computer interface.
The Functional near-infrared spectroscopy study combines topics in areas such as Neuroimaging and Biomedical engineering.
His studies deal with areas such as Electroencephalography, Temporal resolution, Adaptive filter, Prefrontal cortex and Computer vision as well as Artificial intelligence.
His research in the fields of Nonlinear system, Lyapunov function and Exponential stability overlaps with other disciplines such as Decoupling.
His research in Pattern recognition intersects with topics in Linear regression, Second derivative, Exponential forgetting, Region of interest and State-space representation.
His Brain–computer interface research incorporates elements of Speech recognition, Naive Bayes classifier, Support vector machine, Human–computer interaction and Linear discriminant analysis.
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