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Multi-Scale Neural Network for EEG Representation Learning in BCI
Wonjun Ko
, Eunjin Jeon
, Seungwoo Jeong
,
Heung Il Suk
*
*
Corresponding author for this work
Research output
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Contribution to journal
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Article
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peer-review
99
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Keyphrases
Electroencephalogram
100%
Electroencephalography
100%
Brain-computer Interface
100%
Representation Learning
100%
Multi-scale Neural Network
100%
Spatio-spectral
50%
Multi-frequency
50%
Convolutional Neural Network
50%
Spectro-temporal
50%
Spatial Filtering
25%
Signal Representation
25%
State-of-the-art Techniques
25%
Levels of Abstraction
25%
Performance Improvement
25%
Neural Network Method
25%
Passive Brain-computer Interfaces (pBCI)
25%
Recent Advances
25%
Frequency Component
25%
Interface Research
25%
Real-world Problems
25%
Active-passive
25%
Feature Representation
25%
Activation Patterns
25%
Relevance Ranking
25%
Interface Experiments
25%
Application Method
25%
Extracting Features
25%
Nonlinear Operation
25%
Starting Model
25%
Spatial Representation
25%
Backbone Network
25%
Deep Learning
25%
Deep Learning Architectures
25%
Practical Effects
25%
T-distributed Stochastic Neighbor Embedding (t-SNE)
25%
Signal Information
25%
Pattern Mapping
25%
Densely Connected Layers
25%
Active BCI
25%
PSD Curve
25%
Condition Identification
25%
Passive BCI
25%
Frequency Property
25%
Computer Science
Computer Interface
100%
Neural Network
100%
Representation Learning
100%
Experimental Result
40%
Convolutional Neural Network
40%
Signal Representation
20%
Network Architecture
20%
Real-World Problem
20%
Activation Pattern
20%
Performance Improvement
20%
Temporal Information
20%
Frequency Component
20%
Relevance Score
20%
Spatial Representation
20%
Backbone Network
20%
Information Signal
20%
Interface Dataset
20%
Deep Learning Method
20%
Neuroscience
Brain-Computer Interface
100%
Electroencephalogram
100%
Neural Network
100%
Electroencephalography
14%
Vertebral Column
14%
Neurophysiology
14%