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Covert Speech EEG Classification model and FC measures/Analysis

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Τίτλος: Covert Speech EEG Classification model and FC measures/Analysis
Συγγραφείς: Duraisamy, Saravanakumar
Στοιχεία εκδότη: Zenodo
Έτος έκδοσης: 2025
Συλλογή: Zenodo
Θεματικοί όροι: Brain Computer Interface, Brain-Computer Interfaces/classification
Περιγραφή: Project Description This repository contains: Preprocessed EEG trials with artifact removal and a feature extraction module Classification models for imagined (covert) speech tasks Datasets Used EEG data for this study was obtained from two publicly available sources: 2020 International BCI Competition: Imagined Speech Classification Datasethttps://osf.io/pq7vb/files/osfstorage Rekrut, M., & Mohamed Selim, A. (2025).EEG data recorded during spoken and imagined speech interaction with a simulated robotZenodo. https://doi.org/10.5281/zenodo.14645653 Instructions for Use Please follow these steps to run the code: Step 1: Download both datasets and place them into separate folders: Dataset 1 (D1): BCI Competition Dataset Dataset 2 (D2): Zenodo Dataset Step 2: Run the script extract_trials_D1.jpynb to extract EEG segments from Dataset 1. Step 3: Run the script extract_trials_D2.jpynb to extract EEG segments from Dataset 2. Step 4: Use the file Classification_model.jpynb to train and evaluate the classification models separately for Dataset 1 and Dataset 2 covert speech respectively.Make sure the EEG segments from Steps 2 and 3 are prepared before running this step. Coherence and PLV computation Implementation details for Coherence and PLV computation are provided in the file Coherence_PLV_within_Subject_analysis.ipynb. This file demonstrates the procedure for a single subject and inter-trial variability analysis. The same steps can be extended to inter-class variability analysis, and inter-subject variability analysis. I have provided this file to illustrate how the features are computed. Please follow this procedure for between-class, between-subject, and overall subject-level analysis.
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Relation: https://zenodo.org/records/15555946; oai:zenodo.org:15555946; https://doi.org/10.5281/zenodo.15555946
DOI: 10.5281/zenodo.15555946
Διαθεσιμότητα: https://doi.org/10.5281/zenodo.15555946
https://zenodo.org/records/15555946
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
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  Data: Covert Speech EEG Classification model and FC measures/Analysis
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  Data: Project Description This repository contains: Preprocessed EEG trials with artifact removal and a feature extraction module Classification models for imagined (covert) speech tasks Datasets Used EEG data for this study was obtained from two publicly available sources: 2020 International BCI Competition: Imagined Speech Classification Datasethttps://osf.io/pq7vb/files/osfstorage Rekrut, M., & Mohamed Selim, A. (2025).EEG data recorded during spoken and imagined speech interaction with a simulated robotZenodo. https://doi.org/10.5281/zenodo.14645653 Instructions for Use Please follow these steps to run the code: Step 1: Download both datasets and place them into separate folders: Dataset 1 (D1): BCI Competition Dataset Dataset 2 (D2): Zenodo Dataset Step 2: Run the script extract_trials_D1.jpynb to extract EEG segments from Dataset 1. Step 3: Run the script extract_trials_D2.jpynb to extract EEG segments from Dataset 2. Step 4: Use the file Classification_model.jpynb to train and evaluate the classification models separately for Dataset 1 and Dataset 2 covert speech respectively.Make sure the EEG segments from Steps 2 and 3 are prepared before running this step. Coherence and PLV computation Implementation details for Coherence and PLV computation are provided in the file Coherence_PLV_within_Subject_analysis.ipynb. This file demonstrates the procedure for a single subject and inter-trial variability analysis. The same steps can be extended to inter-class variability analysis, and inter-subject variability analysis. I have provided this file to illustrate how the features are computed. Please follow this procedure for between-class, between-subject, and overall subject-level analysis.
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