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Channel and Class Dependent Time-Series Embedding Using Partial Mutual Information Improves Sensorimotor Rhythm Based Brain-Computer Interfaces
DH Coyle
School of Computing, Eng & Intel. Sys
Faculty Of Computing, Eng. & Built Env.
Research output
:
Chapter in Book/Report/Conference proceeding
›
Chapter
›
peer-review
2
Citations (Scopus)
Overview
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Dive into the research topics of 'Channel and Class Dependent Time-Series Embedding Using Partial Mutual Information Improves Sensorimotor Rhythm Based Brain-Computer Interfaces'. Together they form a unique fingerprint.
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Computer Science
Embedding
100%
Computer Interface
100%
Channels
100%
Mutual Information
100%
Fuzzy-Neural System
50%
Signal Processing
33%
Recurrent Neural Network
33%
Feature Selection
16%
Interface System
16%
Modeling
16%
Feature Extraction
16%
Series Prediction
16%
Prediction Accuracy
16%
Engineering
Brain-Computer Interface
100%
Prediction
66%
Optimization
33%
Separability
33%
Measures
16%
Quantity
16%
Measurement
16%
Feature Extraction
16%
Specific Class
16%
Specific Data
16%
Recurrent Neural Network
16%