SSVEPAnalysisToolbox 0.0.4

Python package for SSVEP datasets and algorithms

SSVEP Analysis Toolbox

This repository provides a python package for SSVEP datasets and recognition algorithms. The goal of this toolbox is to make researchers be familier with SSVEP signals and related recognition algorithms quickly, and focus on improving algorithms with shortest preparation time.

Most conventional recognition algorithms are implemented using both eigen decomposition and least-square unified framework. The least-square unified framework demonstrates various design strategies applied in the correlatiion analysis (CA)-based SSVEP spatial filtering algorithms and their relationships.

More detailed information can be found in document.

The lease-square unified framework has been published in IEEE TNSRE: https://ieeexplore.ieee.org/document/10587150/. If you use this toolbox, the lease-square unified framework, or the new spatial filtering methods (ms-MsetCCA-R-1, ms-MsetCCA-R-2, ms-MsetCCA-R-3, ms-(e)TRCA-R-1, ms-(e)TRCA-R-2), please cite this paper.

  @article{LSFrameWork,
    author={Wang, Ze and Shen, Lu and Yang, Yi and Ma, Yueqi and Wong, Chi Man and Liu, Zige and Lin, Cuiyun and Hon, Chi Tin and Qian, Tao and Wan, Feng},
    journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, 
    title={A Least-Square Unified Framework for Spatial Filtering in SSVEP-based BCIs}, 
    year={2024},
    note={early access},
    doi={10.1109/TNSRE.2024.3424410}
  }

Features

• Mutiple implementations of various algorithms:
  • Eigen decomposition
  • Least-square unified framework
• Unify formats of SSVEP datasets
• Provide a standard processing procedure for faire performance comparisons
• Python implementations of SSVEP recognition algorithms

Datasets and Algorithms

• Datasets

  • Benchmark dataset: Y. Wang, X. Chen, X. Gao, and S. Gao, "A benchmark dataset for SSVEP-based braincomputer interfaces," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 10, pp. 1746–1752, 2017. DOI: 10.1109/TNSRE.2016.2627556.
  • BETA dataset: B. Liu, X. Huang, Y. Wang, X. Chen, and X. Gao, "BETA: A large benchmark database toward SSVEP-BCI application," Front. Neurosci., vol. 14, p. 627, 2020. DOI: 10.3389/fnins.2020.00627.
  • Nakanishi2015 dataset: M. Nakanishi, Y. Wang, Y.-T. Wang, T.-P. Jung, "A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials," PLoS ONE, vol. 10, p. e0140703, 2015. DOI: 10.1371/journal.pone.0140703.
  • eldBETA dataset: B. Liu, Y. Wang, X. Gao, and X. Chen, "eldBETA: A Large eldercare-oriented benchmark database of SSVEP-BCI for the aging population," Scientific Data, vol. 9, no. 1, pp.1-12, 2022. DOI: 10.1038/s41597-022-01372-9.
  • openBMI dataset: M.-H. Lee, O.-Y. Kwon, Y.-J. Kim, H.-K. Kim, Y.-E. Lee, J. Williamson, S. Fazli, and S.-W. Lee, "EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy," GigaScience, vol. 8, no. 5, p. giz002, 2019. DOI: 10.1093/gigascience/giz002.
  • Wearable dataset: F. Zhu, L. Jiang, G. Dong, X. Gao, and Y. Wang, “An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces,” Sensors, vol. 21, no. 4, p. 1256, 2021. DOI: 10.3390/s21041256.
  • ...

• Algorithms

  • Implementations based on eigen decomposition
    • Standard canonical correlation analysis (sCCA) and filterbank CCA (FBCCA): Chen, X., Wang, Y., Gao, S., Jung, T.P. and Gao, X., "Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface," J. Neural Eng., vol. 12, no. 4, p. 046008, 2015. DOI: 10.1088/1741-2560/12/4/046008.
    • Individual template CCA (itCCA) and Extended CCA (eCCA): X. Chen, Y. Wang, M. Nakanishi, X. Gao, T.-P. Jung, and S. Gao, "High-speed spelling with a noninvasive brain–computer interface," Proc. Natl. Acad. Sci., vol. 112, no. 44, pp. E6058–E6067, 2015. DOI: 10.1073/pnas.1508080112.
    • Multi-stimulus CCA (ms-CCA): C. M. Wong, F. Wan, B. Wang, Z. Wang, W. Nan, K. F. Lao, P. U. Mak, M. I. Vai, and A. Rosa, "Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs," J. Neural Eng., vol. 17, no. 1, p. 016026, 2020. DOI: 10.1088/1741-2552/ab2373.
    • Multi-set CCA (MsetCCA): Y. Zhang, G. Zhou, J. Jin, X. Wang, A. Cichocki, "Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis," Int J Neural Syst., vol. 24, 2014, p. 1450013. DOI: 10.1142/ S0129065714500130.
    • Multi-set CCA with reference signals (MsetCCA-R): C. M. Wong, B. Wang, Z. Wang, K. F. Lao, A. Rosa, and F. Wan, "Spatial filtering in SSVEP-based BCIs: Unified framework and new improvements.," IEEE Transactions on Biomedical Engineering, vol. 67, no. 11, pp. 3057-3072, 2020. DOI: 10.1109/TBME.2020.2975552.
    • Task-related component analysis (TRCA) and Ensemble TRCA (eTRCA): M. Nakanishi, Y. Wang, X. Chen, Y.-T. Wang, X. Gao, and T.-P. Jung, "Enhancing detection of SSVEPs for a high-speed brain speller using task-related component Analysis," IEEE Trans. Biomed. Eng., vol. 65, no. 1, pp. 104–112, 2018. DOI: 10.1109/TBME.2017.2694818.
    • Task-related component analysis with reference signals (TRCA-R) and Ensemble TRCA with reference signals (eTRCA-R): C. M. Wong, B. Wang, Z. Wang, K. F. Lao, A. Rosa, and F. Wan, "Spatial filtering in SSVEP-based BCIs: Unified framework and new improvements.," IEEE Transactions on Biomedical Engineering, vol. 67, no. 11, pp. 3057-3072, 2020. DOI: 10.1109/TBME.2020.2975552.
    • Sum of squared correlations (SSCOR) and Ensemble sum of squared correlations (eSSCOR): G. K. Kumar, and M. R. Reddy, "Designing a sum of squared correlations framework for enhancing SSVEP-based BCIs," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 10, pp. 2044-2050, 2019. DOI: 10.1109/TNSRE.2019.2941349.
    • Multi-stimulus TRCA (ms-TRCA): C. M. Wong, F. Wan, B. Wang, Z. Wang, W. Nan, K. F. Lao, P. U. Mak, M. I. Vai, and A. Rosa, "Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs," J. Neural Eng., vol. 17, no. 1, p. 016026, 2020. DOI: 10.1088/1741-2552/ab2373.
    • Task-discriminant component analysis (TDCA): B. Liu, X. Chen, N. Shi, Y. Wang, S. Gao, X. Gao, "Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis." IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, pp. 1998-2007, 2021. DOI: 10.1109/TNSRE.2021.3114340.
    • Online adaptive CCA (OACCA) (OACCA): C. M. Wong et al., “Online adaptation boosts SSVEP-based BCI performance,” IEEE Trans. Biomed. Eng., vol. 69, no. 6, pp. 2018-2028, 2022. DOI: 10.1109/TBME.2021.3133594.
    • ...
  • Implementations based on least-square unified framework
    • sCCA, itCCA, eCCA, (e)TRCA, (e)TRCA-R, MsetCCA, MsetCCA-R, ms-CCA, ms-(e)TRCA, TDCA
    • ms-MsetCCA-R-1
    • ms-MsetCCA-R-2
    • ms-MsetCCA-R-3
    • ms-(e)TRCA-R-1
    • ms-(e)TRCA-R-2

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Acknowledgements

• edwin465/SSVEP-MSCCA-MSTRCA
• edwin465/SSVEP-OACCA
• mnakanishi/TRCA-SSVEP
• TBC-TJU/brainda