Open-Source board for converting RaspberryPI to Brain-computer interface
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Updated
Aug 6, 2024 - Python
Open-Source board for converting RaspberryPI to Brain-computer interface
Open-Source Brain-Computer Interface, ADS1299 and STM32
YASA (Yet Another Spindle Algorithm): a Python package to analyze polysomnographic sleep recordings.
CS198-96: Intro to Neurotechnology @ UC Berkeley
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.
EEGraph: Convert EEGs to graphs with frequency and time-frequency domain connectivity measures.
An R package for processing and plotting of electroencephalography (EEG) data
Code to accompany our International Joint Conference on Neural Networks (IJCNN) paper entitled - Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
Python API for Mentalab biosignal aquisition devices
Code for the paper "Multi-Task CNN Model for Emotion Recognition from EEG Brain Maps". DEAP dataset. Python/Keras/Tensorflow 2 Impementation.
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
JMIR AI'23: EEG dataset processing and EEG Self-supervised Learning
Emotion Recognition, EEG Mapping, Azimuthal Projection Technique, CNN
Implementation of Domain Specific Denoising Diffusion Probabilistic Models for Brain Dynamics/EEG Signals
Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals
Code to accompany our International Conference on Pattern Recognition (ICPR) paper entitled - Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.
ADHDeepNet is a model that integrates temporal and spatial characterization, attention modules, and explainability techniques, optimized for EEG data ADAD diagnosis. Neural Architecture Search (NAS), Hyper-parameter optimization, and data augmentation are also incorporated to enhance the model's performance and accuracy.
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