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Sinc-Windowing and Multiple Correlation Coefficients Improve SSVEP Recognition Based on Canonical Correlation
Valeria Mondini1, Anna Lisa Mangia1, Luca Talevi1
1Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Cesena, Italy.
Computational Intelligence and Neuroscience
|June 1, 2018
Summary
Simple variations to Canonical Correlation Analysis (CCA) for Steady-State Visually Evoked Potential (SSVEP) recognition significantly boost accuracy. These methods enhance SSVEP classification with minimal computational cost, benefiting portable devices.
Area of Science:
- Neuroscience
- Signal Processing
- Biomedical Engineering
Background:
- Canonical Correlation Analysis (CCA) is a key method for Steady-State Visually Evoked Potential (SSVEP) recognition.
- Existing CCA variations often increase complexity, computational load, or require additional user training.
- There is a need for simple, modular, and computationally inexpensive CCA variations for practical SSVEP applications.
Purpose of the Study:
- To evaluate the impact of two simple, modular variations on classical CCA for SSVEP recognition.
- To assess the trade-offs between classification accuracy, computational cost, and modularity.
- To determine the generalizability of these variations for different CCA-based algorithms.
Main Methods:
- Investigated two variations: adjusting the number of canonical correlations and incorporating sinc-window prefiltering.
- Tested the variations on ten volunteers in a 4-class SSVEP experimental setup.
- Analyzed classification accuracy and computational steps required for each variation.
Main Results:
- Both variations, individually and combined, significantly improved SSVEP classification accuracy.
- Accuracy increments ranged from 7-8% on average, with peaks of 25-30%.
- Variation (i) had no impact on computational steps, while variation (ii) had minimal impact.
Conclusions:
- Simple, modular variations of CCA can substantially enhance SSVEP classification accuracy.
- These variations offer a practical solution for low-cost, portable SSVEP devices.
- The proposed modifications are easily adaptable to various CCA-based algorithms and setups.
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