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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.

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AutICA

Signal Processing in Bioengineering Course Project @ IST Lisbon

Requirements

  • MATLAB v2016 or superior
  • MATLAB Statistical and Signal Processing toolboxes
  • Python 3.6 or superior
  • Up-to-date Python packages scipy, numpy, matplotlib

To Run Automatic ICA Analysis

Alter subject, session numbers and ICA algorithm to run in eeglab2021.0/analysis_protocol.m. Run it on MATLAB. An EEGLAB session will start analysing the dataset of that subject-session pair and it will save some images and datasets on the device. So, be sure to have at least 300 Mb free of storage per subject-session pair. The analysis will stop to ask you which independent components to remove. Answer in the command line with an array, e.g. [2 5 7]. Results will be saved under the directory results/SBJXX/SYY/algorithm according to subject, session, and ICA algorithm selected.

To Run Automatic P300 Detection

Alter subject, session numbers in p300/p300_plot.py and run the script with the command python3 p300_plot.py. Results will be saved under the directory results/SBJXX/SYY according to subject and session selected.

Authorship

(C) 2021 João Saraiva -- joaomiguelsaraiva@tecnico.ulisboa.pt

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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.

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