SSVEP-CCA-Algorithm
This repository contains an implementation of the Canonical Correlation Analysis (CCA) algorithm for frequency recognition in Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interfaces (BCIs).
Overview
Steady-State Visual Evoked Potentials (SSVEPs) are brain signals that occur in response to visual stimuli flickering at specific frequencies. SSVEP-based BCIs use these signals to detect the frequency at which the user is attending, allowing for hands-free communication and control. The CCA algorithm is a promising approach for frequency recognition in SSVEP-based BCIs, offering advantages such as improved accuracy, robustness to noise, and the ability to utilize harmonic frequencies as stimuli.
Features
Frequency Recognition: Implements the CCA algorithm for extracting and recognizing SSVEP frequencies from multi-channel EEG data. Multi-Channel Support: Supports the use of multiple EEG channels for improved performance and robustness. Harmonic Frequency Detection: Can recognize SSVEP frequencies that are harmonics of the stimuli frequencies. Channel Selection: Includes a method for selecting the most relevant EEG channels for SSVEP detection. Parameter Optimization: Provides tools for optimizing parameters such as window length and the number of harmonics used.
In additionaly this repository contains a Matlab implementation for distinguish SSVEP Frequency using Mr. Lin's mathematical technique (CCA) features extracted from EEG data and then improving its by machine learning methods such as SVM,KNN
References
Lin, Z., Zhang, C., Wu, W., & Gao, X. (2006). Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Transactions on Biomedical Engineering, 53(12), 2610-2614. Bin, G., Gao, X., Yan, Z., Hong, B., & Gao, S. (2009). An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. Journal of Neural Engineering, 6(4), 046002.
