What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Tom Chau focuses on Brain–computer interface, Artificial intelligence, Speech recognition, Physical medicine and rehabilitation and Physical therapy.
His Brain–computer interface study which covers Linear discriminant analysis that intersects with Prefrontal cortex, Audiology and Task.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Affect, Computer vision and Pattern recognition.
His Speech recognition study combines topics in areas such as Frequency domain, Information transfer, Gaze and Time–frequency analysis.
His Physical medicine and rehabilitation research is multidisciplinary, relying on both Cervical auscultation, Swallowing, Dysphagia and Motor skill.
His work on Rehabilitation as part of general Physical therapy study is frequently connected to Access method, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His most cited work include:
- Upper limb prosthesis use and abandonment: A survey of the last 25 years: (590 citations)
- Short-Term Music Training Enhances Verbal Intelligence and Executive Function (391 citations)
- Upper-limb prosthetics: critical factors in device abandonment. (375 citations)
What are the main themes of his work throughout his whole career to date?
Tom Chau mainly focuses on Brain–computer interface, Speech recognition, Artificial intelligence, Physical medicine and rehabilitation and Swallowing.
His studies in Brain–computer interface integrate themes in fields like Classifier, Task, Linear discriminant analysis and Cognition.
His work on Hidden Markov model as part of general Speech recognition study is frequently linked to Verbal fluency test, therefore connecting diverse disciplines of science.
Tom Chau has included themes like Machine learning, Computer vision and Pattern recognition in his Artificial intelligence study.
His Physical medicine and rehabilitation research incorporates themes from Physical therapy, Simulation and Handwriting.
The study incorporates disciplines such as Signal and Dysphagia in addition to Swallowing.
He most often published in these fields:
- Brain–computer interface (42.62%)
- Speech recognition (32.87%)
- Artificial intelligence (31.20%)
What were the highlights of his more recent work (between 2015-2021)?
- Brain–computer interface (42.62%)
- Speech recognition (32.87%)
- Electroencephalography (13.65%)
In recent papers he was focusing on the following fields of study:
Tom Chau spends much of his time researching Brain–computer interface, Speech recognition, Electroencephalography, Motor imagery and Artificial intelligence.
His Brain–computer interface research integrates issues from Classifier, Task, Cognition and Linear discriminant analysis.
His Speech recognition research is multidisciplinary, incorporating elements of Imagined speech, Information transfer, Error detection and correction and Elementary cognitive task.
His Electroencephalography study incorporates themes from Human–computer interaction, Signal, Asynchronous communication and Audiology.
His work in Motor imagery tackles topics such as Neurofeedback which are related to areas like Affect.
Tom Chau combines subjects such as Machine learning, Filter, Computer vision and Pattern recognition with his study of Artificial intelligence.
Between 2015 and 2021, his most popular works were:
- An Auditory-Tactile Visual Saccade-Independent P300 Brain-Computer Interface. (72 citations)
- A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State (40 citations)
- EEG Classification of Covert Speech Using Regularized Neural Networks (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Tom Chau mainly investigates Brain–computer interface, Speech recognition, Motor imagery, Electroencephalography and Artificial intelligence.
His Brain–computer interface study combines topics from a wide range of disciplines, such as Classifier, Training set, Task and Event-related potential.
His Speech recognition research incorporates elements of Information transfer, Error detection and correction, Eye movement and Elementary cognitive task.
His Motor imagery research also works with subjects such as
- Cognition which connect with Cognitive psychology, Workload, Visual perception and Affect,
- Discriminative model, Linear discriminant analysis, Functional near-infrared spectroscopy and Artificial neural network most often made with reference to Imagined speech.
His work in the fields of Electroencephalography, such as Neurofeedback and Brain activity and meditation, overlaps with other areas such as Covert.
Tom Chau interconnects Percentage point, Machine learning, Stimulus and Filter in the investigation of issues within Artificial intelligence.
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