Skip to main navigation
Skip to search
Skip to main content
University of Copenhagen Research Portal Home
Help & FAQ
Dansk
English
Home
Profiles
Research output
Research units
Press/Media
Activities
Prizes
???studenttheses???
Datasets
Search by expertise, name or affiliation
A Deep Learning MI - EEG Classification Model for BCIs
Hauke Dose, Jakob S. Moller, Sadasivan Puthusserypady,
Helle K. Iversen
Department of Clinical Medicine
12
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'A Deep Learning MI - EEG Classification Model for BCIs'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
MI-EEG
100%
Electroencephalogram Classification
100%
Temporal Convolutional Network
50%
Spatial Convolution
50%
Convolutional Neural Network Model
50%
Raw Electroencephalogram
50%
End-to-end Convolutional Neural Network
50%
Motor Imagery Training
50%
Raw Signal
50%
Electroencephalogram Data
50%
Engineering
Deep Learning Method
100%
Brain-Computer Interface
100%
Motor Imagery
100%
Feature Extraction
25%
Limited Number
25%
Convolutional Neural Network
25%
Learning Approach
25%
Network Model
25%
State-of-the-Art Method
25%
Classification Method
25%
Computer Science
Classification Models
100%
Deep Learning Method
100%
Classification Method
25%
Fully Connected Layer
25%
Single Individual
25%
Preprocessing
25%
Neural Network Model
25%
Computer Interface
25%
Reported Result
25%
Learning Approach
25%
Feature Extraction
25%
Convolutional Neural Network
25%
Earth and Planetary Sciences
State of the Art
100%
Pattern Recognition
100%
Preprocessing
100%
Chemical Engineering
Deep Learning Method
100%
Neural Network
25%