zuo785843091 / canonical_classifier

This repository is provided for replicating the canonical classifier of SSVEP signals.

Introduction

This repository is provided for replicating the canonical recognition methods of SSVEP signals. The replicated methods include CCA [1], MSI [2], FBCCA [3], TRCA [4], and TDCA [5]. The file distribution follow the code desgin of SSVEPNet [6]. And a 12-class public dataset [7] was used to conduct evaluation.

Running Environment

• Setup a virtual environment with python 3.8 or newer
• Install requirements

pip install -r Resource/requirements.txt

Running Demo Experiments

cd Exeperiment
python TDCA_SSVEP_Classification.py

Reference

[1] Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs[J]. IEEE transactions on biomedical engineering, 2006, 53(12): 2610-2614. https://ieeexplore.ieee.org/abstract/document/4015614

[2] Zhang Y, Xu P, Cheng K, et al. Multivariate synchronization index for frequency recognition of SSVEP-based brain–computer interface[J]. Journal of neuroscience methods, 2014, 221: 32-40. https://www.sciencedirect.com/science/article/abs/pii/S0165027013002677

[3] Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface[J]. Journal of neural engineering, 2015, 12(4): 046008. https://iopscience.iop.org/article/10.1088/1741-2560/12/4/046008/meta

[4] Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis[J]. IEEE Transactions on Biomedical Engineering, 2017, 65(1): 104-112. https://ieeexplore.ieee.org/abstract/document/7904641

[5] Liu B, Chen X, Shi N, et al. Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1998-2007. https://ieeexplore.ieee.org/abstract/document/9541393

[6] Pan Y, Chen J, Zhang Y, et al. An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition[J]. Journal of Neural Engineering, 2022, 19(5): 056014. https://iopscience.iop.org/article/10.1088/1741-2552/ac8dc5/meta

[7] Nakanishi M, Wang Y, Wang Y T, et al. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials[J]. PloS one, 2015, 10(10): e0140703. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703