[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
A P300 online spelling mechanism for Emotiv headsets. It's completely written in Node.js, and the GUI is based on Electron and Vue.
EEG BCI Real-Time Applications: Contains real-time demonstrations of BCI applications
A basic demonstration how to use Python, MNE, and PyTorch to analyze EEG signal.
Timeflux demos
P300 Classification for EEG-based BCI system with Bayes LDA, SVM, LassoGLM and a Deep CNN methods
An ESPHome external component for Viessmann heating controllers over the Optolink interface.
Welcome to the EEG Signal Analysis repository, focusing on the extraction of P300 signals using synchronous averaging techniques. This project aims to provide insights into the optimal number of repetitions required to reliably capture the P300 response, a crucial component in various applications such as BCIs and cognitive neuroscience research.
A tool for teaching P300 by showing the ongoing averaging process and classification
Framework for P300 wave detection and noise-based cyberattacks in Brain-Computer Interfaces - Enrique Tomás Martínez Beltrán
Workshop on standardized Brain-Computer Interface Framework
Extract the independant sources with Composite Approximate Joint Diagonalization (CAJD) for linear/bilinear data models
Assess ICA-denoising impact on the analysis of the event related potential P300, for an Autism Spectrum Disorder BCI dataset. Reject different numbers of Independent Components and compare them to common noise sources of EEG acquisitions.
📚 Index and enrich your PDFs and Markdown files locally for a powerful, unified knowledge base with semantic search capabilities.
P300 Matrix for brain computer interfaces using html, CSS and JavaScript with mean error 1 millisecond
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