A Comprehensive Comparison Between Steady-State Visual Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With The Minimum Number of EEG Channels

Abstract

Introduction: Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications. Methods: In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes. Results: It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set. Conclusion: Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems.

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

“…Note that the classification accuracies of the L1-MCCA related methods with varied TWs were consistently higher than the CCA-related and PSDrelated methods. Our results are in agreement with previous research [30,41,42]. It has been reported that the L1-MCCA method can improve the classification accuracy of SSVEP-based BCI by optimizing the reference signal.…”

Active Claw-Shaped Dry Electrodes for EEG Measurement in Hair Areas

“…Due to limitations associated with the connection mode between electrode and circuit and the claw-shaped dry electrode structure, the diameter of the active dry electrode is approximately 20 mm, which satisfies EEG acquisition design according to the 10-20 international system, but not the 10-10 international system. At the moment, many researchers are focusing on reducing the number of channels in EEG acquisition systems [ 35 , 36 , 37 ]. However, the high-density EEG and the accuracy of electrode position are still very important [ 38 , 39 ].…”

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach

“…LASSO is considered a subject-independent technique since the penalty parameter that influences the LASSO performance is calculated offline by taking advantage of the SSVEP data from multiple subjects [ 41 ]. There has been recent research where the LASSO method was applied because of the ability to perform multichannel analysis for identifying SSVEP signal frequencies (e.g., [ 32 , 42 ]).…”

“…There has been recent research where the LASSO method was applied because of the ability to perform multichannel analysis for identifying SSVEP signal frequencies (e.g., [32,42]).…”