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A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.

Source Code for "Adaptive Transfer Learning with Deep CNN for EEG Motor Imagery Classification".

Hands-on tutorial on deep learning for EEG classification.

Using Deep Learning for Emotion Classification on EEG signals (SEED Dataset). CNN, RNN, Hybrid model, and Ensemble

CNN-SAE program for MI-BCI classification. (Based on "Tabar et al-2016-J Neural Eng. A novel deep learning approach for classification of EEG motor imagery signals")

Existing work in the field of BCI treats deep learning models as black-box classifiers. In this project, we develop a novel model named "InterpretableCNN" that allows sample wise analysis of important features for classification. The model not only achieves SOTA classification accuracy of EEG sig... Read more

The Cross-modal SleepTransformer is a deep learning model designed for sleep stage classification based on multimodal physiological signals. It utilizes the transformer architecture, and cross-attention mechanism to effectively process and integrate information from multiple input modalities, inc... Read more

This repository contains a Python code script for performing emotion classification using EEG (Electroencephalogram) data. Emotion classification from EEG signals is an important application in neuroscience and human-computer interaction. The code leverages deep learning techniques to analyze EEG... Read more