For ear EEG testing and verifying
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Updated
May 3, 2024 - Python
For ear EEG testing and verifying
An executable (.exe) from a python script for p300 segment extraction using different channels
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Implementation of Correlation function and signal averaging method for detecting P300.
For my MSc dissertation, and in my role as a research data analyst, I am undertaking an analysis of electroencephalography data to investigate whether detection of the P300 neural signal can be utilised within an EEG Brain-Computer Interface to discern information from the minds of individuals, without the need for explicit communication.
This example compares the classification performance of linear support vector machine (LinearSVC) on the Riemannian Transfer Learning method (RPA, Rodrigues et al., 2018) and the golden-standard subject-wise train-test cross-validation method using real P300 BCI data.
this is a simple artificial neural network that used to classify P300 without using any libraries
Workshop on standardized Brain-Computer Interface Framework
The Histogram of Gradient Orientations of EEG Signal Plots for BCI
P300 Matrix for brain computer interfaces using html, CSS and JavaScript with mean error 1 millisecond
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.
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.
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