IEEE

Fine-grained EEG emotion recognition using lite residual convolution-based transformer neural network

Abstract: Current electroencephalogram (EEG)-based emotion recognition research confronts two main challenges. On the one hand, most EEG emotion datasets have limited stimulus diversity and ...
IEEE

A Novel Dual-Task Model for EEG-Based Emotion and Cognition Recognition

Abstract: Human cognition and emotion states are interrelated. Constructing electroencephalography (EEG)-based deep learning models to simultaneously recognize the cognitive and emotional states of an ...
GitHub

Masqiller/NeuroSky_BCI_Arduino

MindControlExamples/ ├── README.md # This documentation file ├── Drivers/ # USB-to-Serial drivers (CH341) │ ├── CH341SER.SYS │ ├── CH341SER_MW.INF │ ├── SETUP.EXE # Driver installer │ └── DRVSETUP64/ ...
GitHub

Using SEED dataset, CNN with SE module and a Transformer for temporal encoding, achieve an acuraccy of 94.77 ± 0.34 %

models.py is the original net design from patrickdmiller/SEED-EEG-Deep-neural-network: residual deep cnn and lstm for classifying SEED data modeltest.py is the new ...