P300-BCI

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P300 Classification for EEG-based BCI system with Bayes LDA, SVM, LassoGLM and a Deep CNN methods

P300-BCI 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# P300-classification-methods

A MATLAB toolbox for P300 Classification in EEG-based BCI system with Bayes LDA, SVM, LassoGLM and a Deep CNN methods

Codes and data for the following paper are extended to different methods:

An efficient P300-based brain-computer interface for disabled subjects

## 1. Introduction.

This package includes the prototype MATLAB codes for P300-based brain-computer interfaces.

The implemented methods include:

1. Bayesian linear discriminant analysis (Bayes-LDA)
  2. Support-vector machines (SVMs)       
  3. Penalized generalized linear models (LassoGLM)
  4. Deep Convolutional Neural Networks (Deep-CNNs)

## 2. Usage & Dependency.

## Dependency:
     https://www.epfl.ch/labs/mmspg/research/page-58317-en-html/bci-2/bci_datasets/
     
     https://github.com/lrkrol/plot_erp

## Usage:
It is recommended that you create a /dataset folder for the EPFL dataset without any new and extra codes like the below image.

Run "p300_pattern.m" to analyze the P300 ERP over baseline events.

Run "p300_classifiers.m" to check P300-BCI systems with different classifiers and performances.

![image](https://user-images.githubusercontent.com/50092872/188487693-35cb5ff7-fae1-4b26-8bb0-de76f28ca27a.png)

P300-BCI

P300-BCI 최신버젼 다운로드

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P300-BCI

P300-BCI 최신버젼 다운로드

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