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3.eeg_recognation

Python (deep learning and machine learning) for EEG signal processing on the example of recognizing the disease of alcoholism

arl-eegmodels

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

bci_challenge

cs249r_book

Collaborative book for CS249r: Tiny Machine Learning

dagdata

Data for the HarvardX courses: PH525x

deep-learning-for-bci

Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications

dvm

python scripts for analyzing eeg data and behavior

dyconnmap

A dynamic connectome mapping module in python.

eeg-based-emotion-recognition-on-deap

eeg-dl

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

eeglib

A library with tools for EEG analysis

emotion-detection

Emotion recognition is the process of identifying human emotion, most typically from facial expressions. Build using OpenCv and neural network giving live video feed emotions

emotions

This program aims to accurately predict the emotional states of a subject while watching various movie scenes, using their EEG readings and a recurrent neural network

erp

https://www.sciencedirect.com/science/article/pii/S1053811920309502

extracting-power-bands-theta-delta-bands-from-the-eeg-signal-using-mne-and-yasa-python

Extracting power bands (theta, delta, alpha, beta bands) from the EEG signal using MNE and YASA Python

flask-deployment-of-model

frites

Framework for Information Theoretical analysis of Electrophysiological data and Statistics

infant-cluster-effectsize

Enhancing reproducibility in developmental EEG research by using BIDS, cluster-based permutation tests, and reporting effect sizes

machine-learning-with-r-datasets

Formatted datasets for Machine Learning With R by Brett Lantz

machine_learning_evaluates_changes_in_functional_connectivity_under_a_prolonged_cognitive_load

Source code of "Machine learning evaluates changes in functional connectivity under a prolonged cognitive load"

mi-eeg-1d-cnn

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

ml-for-beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

mne-nirs

Process Near-Infrared Spectroscopy Data in MNE

mrgaze

Eye Tracking and Gaze Estimation in Python

nirs-toolbox

nsp-course

Python implementations for the "Complete neural signal processing and analysis: Zero to hero" course

reinforcement-learning-c-sharp-unity-ppo-ddpg-dqn

Reinforcement Learning completely written from PyTorch to C# Unity Asset

snirf

SNIRF Format Specification

ssqueezepy

Synchrosqueezing, wavelet transforms, and time-frequency analysis in Python

stanford-cs-230-deep-learning

VIP cheatsheets for Stanford's CS 230 Deep Learning