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Electroencephalogram (EEG)
Eeg On Seed Iv
Eeg On Seed Iv
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
BiHDM
74.35
A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition
DGCNN
69.88
EEG emotion recognition using dynamical graph convolutional neural networks
DBN
66.77
Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks
0 of 3 row(s) selected.
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Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Electroencephalogram (EEG)
Eeg On Seed Iv
Eeg On Seed Iv
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
BiHDM
74.35
A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition
DGCNN
69.88
EEG emotion recognition using dynamical graph convolutional neural networks
DBN
66.77
Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks
0 of 3 row(s) selected.
Previous
Next