neurodeckit 0.2.0

Full chain toolkit for EEG signal decoding

NeuroDecKit: A Comprehensive MI-EEG Decoding Toolbox

NeuroDecKit is a modular and extensible MATLAB toolbox for Motor Imagery Electroencephalography (MI-EEG) decoding, featuring comprehensive algorithm integration and flexible pipeline construction capabilities.

🚀 Key Features

Comprehensive Algorithm Ecosystem

NeuroDecKit systematically integrates algorithms across nine functional components:

Module	Methods Count	Representative Algorithms
Spectral Filtering	2	Butterworth, Chebyshev
Channel Selection	3	Correlation-based, CSP weights, Riemannian distance
Spatial Filtering	3	CSP, RSF, Laplace
Data Alignment	4	EA, RA, RPA, None
Feature Extraction	8	CSP, CSSP, RCSP, DSP, MDM, FgMDM, TSM, CTSSP
Feature Selection	6	ANOVA, MIC, PCA, LASSO, RFE, None
Feature Alignment	5	Z-score, MMD, MEKT, MEFA, None
Classification	7	LDA, SVM, LR, KNN, DTC, GNB, MLP
End-to-End Models	5	SBLEST, RKNN, RKSVM, TRCA, DCPM
Deep Learning	11	sCNN, dCNN, FBCNet, EEGNet, Tensor-CSPNet, Graph-CSPNet, LMDA-Net, EEGConformer, LightConvNet, IFNet, MSVTNet
Ensemble Learning	3	Voting, Boosting, Stacking

Advanced Transfer Learning Framework

NeuroDecKit features a sophisticated transfer learning system with:

• 6,000+ configurable transfer learning pipelines
• Multi-level domain adaptation: Data-level and feature-level alignment
• Comprehensive transfer scenarios: Cross-session, Cross-subject & Cross-dataset

Flexible Pipeline Construction

• 300+ non-transfer learning pipeline configurations
• Modular design for easy algorithm combination
• Extensible architecture for custom method integration

📊 Experimental Results

Experimental Protocols

NeuroDecKit supports multiple experimental paradigms to evaluate algorithm generalization:

🔄 Cross-Session Validation

• Training: All data from first experimental session
• Testing: All data from second experimental session
• Objective: Evaluate temporal stability and session-to-session transfer

👥 Cross-Subject Validation (LOSO)

• Training: All subjects except one (source domain)
• Testing: Left-out subject (target domain)
• Objective: Evaluate intersubject generalization and domain adaptation

🧬 Cross-Dataset Validation

• Training: All data from one dataset (source domain)
• Testing: All data from another dataset (target domain)
• Objective: Evaluate cross-dataset generalization and domain adaptation

Benchmark Performance

Detailed results available in:

BNCI2014-001 (BCI Competition IV-2a)

Cross-Session | Cross-Subject

BNCI2015_001

Cross-Session | Cross-Subject

Pan2023 Dataset

Cross-Session | Cross-Subject

Shin2017A Dataset

Cross-Session | Cross-Subject

🤝 Related Research Resources

We express our gratitude to the open-source community, which facilitates the broader dissemination of research by other researchers and ourselves. The coding style in this repository is relatively rough. We welcome anyone to refactor it to make it more efficient. Our model codebase is largely based on the following repositories:

• A widely used machine learning library in Python that provides simple and efficient tools for data mining and data analysis.

• An open science project aimed at establishing a comprehensive benchmark for BCI algorithms using widely available EEG datasets.

• An open-source non-invasive brain-computer interface platform.

• A Python library focused on Riemannian geometry methods for EEG signal classification. pyRiemann provides a suite of tools for processing and classifying EEG signals in Riemannian space.

• Contains several deep learning models such as EEGNet, ShallowConvNet (sCNN), and DeepConvNet (dCNN), designed specifically for EEG signal classification. Braindecode aims to provide an easy-to-use deep learning toolbox.

• provides several deep learning models such as EEGConformer, LightConvNet, and MSVTNet for EEG signal classification.

📄 License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.