T
Tao Wang
Researcher at University of Illinois at Chicago
Publications - 4
Citations - 317
Tao Wang is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Motor imagery & Brain–computer interface. The author has an hindex of 4, co-authored 4 publications receiving 302 citations. Previous affiliations of Tao Wang include University of Illinois at Urbana–Champaign.
Papers
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Journal ArticleDOI
Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns
TL;DR: The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from electroencephalogram (EEG) rhythmic components as the feature description.
Journal ArticleDOI
An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain-computer interface.
TL;DR: A new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements and promising results suggest that it can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.
Classification of motor imagery EEG patterns and their topographic representation
TL;DR: A single trial motor imagery classification strategy for the brain computer interface (BCI) applications is developed by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from EEG rhythmic components as the feature description.
Proceedings ArticleDOI
Classification of motor imagery EEG patterns and their topographic representation
TL;DR: In this paper, a single trial motor imagery (MI) classification strategy for the brain computer interface (BCI) applications by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from EEG rhythmic components as the feature description.