Combining the Benefits of CCA and SVMs for SSVEP-based BCIs in Real-world Conditions

Abstract

In this paper we propose a novel method for SSVEP classification that combines the benefits of the inherently multi-channel CCA, the state-of-the-art method for detecting SSVEPs, with the robust SVMs, one of the most popular machine learning algorithms. The employment of SVMs, except for the benefit of robustness, provides us also with a confidence score allowing to dynamically trade-off the trial length with the accuracy of the classifier, and vice versa. By balancing this trade-off we are able to offer personalized selfpaced BCIs that maximize the ITR of the system. Furthermore, we propose to perturb the template frequencies of CCA so as to accommodate with real world BCI applications requirements, where the environmental conditions may not be ideal compared to existing methods that rely on the assumption of soundproof and distractionfree environments.

Impact of adaptive filtering-based component analysis method on steady-state visual evoked potential based brain computer interface systems

“…For subject-1, the improvement in classification accuracy is evaluated as 27.67% against LASSO method and 7.81% against CNN method. Similarly, for subject S-6, the enhancement in classification accuracy is observed as 12.32% and 41.98% whereas the enhancement in classification accuracy considering subject S- Classification accuracy SVM, LDA, K-NN, [22] 79.47%, 64.11%, 49.40% K-NN, C4.5, CNN [23] 46.17%, 49.41%, 69.03% SVM along with CCA [24] 93.11% CNN [25] 73.74% SVM [26] 88.3% CNN+LOSO [27] 69.75% CNN [27] 80.83% Proposed AFCA Model 93.48%…”

“…The obtained classification accuracy is mean of varied data lengths (0.2-0.6). The improvement of classification accuracy using proposed AFCA model against [24] is 0.38% and against [25] is 26.75%, against [26] is 5.85% and against [27] is 15.63%. Thus, proposed AFCA model outperforms all the SSVEP algorithms in terms of classification accuracy.…”

“…Moreover, a cross-validation method is utilized to obtain efficient ITR and classification accuracy results. Further, Table 1 shows performance of classification accuracy using proposed AFCA model in comparison with several SSVEP classification methods such as support vector machines (SVM), linear discriminant analysis (LDA), k-nearest neighbor (K-NN) [22], K-NN, C4.5, CNN [23], and SVM along with canonical correlation analysis (CCA) [24], CNN [25] and SVM [26]. The efficiency of proposed AFCA model is evaluated considering classification accuracy.…”

Review of brain–computer interface based on steady‐state visual evoked potential

“…The experimental results demonstrated that the architecture could be used for classification of an SSVEP‐BCI system and real‐time application of BCI. In 2017, Chatzilari et al [75] proposed a brain electrolytic‐code algorithm that combined the multichannel CCA decoding and SVM algorithms. The SVM algorithm provided a confidence score, which allowed dynamic adjustment of the length of analysis data and accuracy of the classifier.…”