EEG-ANALYSIS-FOR-MOTOR-IMAGERY-APPLICATION

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

PyTorch implementation of beam search decoding for seq2seq models

CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification

[ICLR2025 Spotlight] MagicPIG: LSH Sampling for Efficient LLM Generation

code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"

Tensorflow based Neural Conversation Models

A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

Python Brain-Computer Interface Software

SOMns: A Newspeak for Concurrency Research

Transformer-based fNIRS Classification. Paper: Transformer Model for Functional Near-Infrared Spectroscopy Classification