55 skills found · Page 1 of 2
ncclabsustech / EEGdenoiseNetEEGdenoiseNet, a benchmark dataset, that is suited for training and testing deep learning-based EEG denoising models, as well as for comparing the performance across different models.
meiyor / SincNet For Autism EEG Based Emotion RecognitionThis project describes the necessary code to implement an EEG-based emotion recognition using SincNet [Ravanelli & Bengio 2018] including data from individuals diagnosed with Autism (ASD). For more details and data request send an email to the authors and contributors Juan Manuel Mayor Torres (juan.mayortorres@unitn.it) and Mirco Ravanelli (Mila)
IoBT-VISTEC / EEGWaveNetsource codes for EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection (IEEE Transactions on Industrial Informatics)
Promise-Z5Q2SQ / EEG ImageNet DatasetNo description available
kiselev1189 / EEGClassificationMCNNSolution for EEG Classification via Multiscale Convolutional Net coded for NeuroHack at Yandex.
erinqhu / EEG Motor ImageryECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification and Demystification
Amir-Hofo / EEGNetThis code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces".
ardkastrati / EEGEyeNetEEGEyeNet is benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty
ceresOPA / Automatic Sleep Staging Based On EEG Signal Using Deep Network基于单通道脑电信号的自动睡眠分期研究（Automatic Sleep Staging Based on EEG Signal using Deep Network）
Ma-Xinzhi / EEG TransNetNo description available
cuijiancorbin / A Compact And Interpretable Convolutional Neural Network For Single Channel EEGIn this project, we propose a CNN model to classify single-channel EEG for driver drowsiness detection. We use the Class Activation Map (CAM) method for visualization. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.
sari-saba-sadiya / EEG Channel Interpolation Using Deep Encoder Decoder NetworksCode for the paper "EEG Channel Interpolation Using Deep Encoder-decoder Networks"
geopopos / EEG Convolutional Neural NetA convolutional neural network developed in python using the Keras machine learning framework used to categorize brain signal based on what a user was looking at when the EEG data was collected.
pbizopoulos / Signal2image Modules In Deep Neural Networks For Eeg ClassificationNo description available
cvmdsp / MAS DGAT NetMAS-DGAT-Net: A Dynamic Graph Attention Network with Multibranch Feature Extraction and Staged Fusion for EEG Emotion Recognition
bruAristimunha / Re Deep Convolution Neural Network And Autoencoders Based Unsupervised Feature Learning Of EEGNo description available
KnightofDawn / EEG Classification Using Recurrent Neural NetworkUsed LSTM Network to classify eeg signals based on stimuli the subject recieved (visual or audio)
ivsemenkov / EEGSimpleNetA simple interpretable classification neural network for EEG data with toolbox for its weights interpretation
HeyyyyyyG / Multi Souce Separation And Alignment Network For EEG Emotion ClassificationTransfer learning for multi source EEG-emotion-classification
jesus-333 / Dynamic PyTorch NetClass to automatic create Convolutional Neural Network in PyTorch
