Multiple-Classifier Binary Convolutional Siamese Networks for Code-Modulated Visual Evoked Potential Classification

Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (c-VEPs) using electroencephalography (EEG) signals require robust classification algorithms. It is unclear whether the best approach is to use a similarity measure or to follow a discriminant method.

We propose a multiple-classifier binary convolutional Siamese (MCBCS) network for single-trial c-VEP decoding, in which the multi-class recognition problem is decomposed into a set of class-specific binary similarity-learning tasks. The proposed MCBCS framework is systematically compared against a single multi-class Siamese network, convolutional neural networks for 63-bit m-sequence reconstruction and direct classification, and conventional correlation-based and canonical correlation analysis approaches. The study also investigates distance-based decoding strategies and the effect of temporal data augmentation with small to medium time shifts.

Experimental results on EEG data from 13 subjects demonstrate that the MCBCS architecture consistently outperforms other tested methods under within-subject evaluation, with a mean single-trial accuracy of 96.89%. However, the MCBCS approach achieves 96.17% under a leave-one-subject-out protocol, while EEGNet achieves 96.79%. Finally, the Wasserstein Distance (WD$_{1}$) achieved the highest accuracy (93.88%) among the distance metrics.

The multiple-classifier convolutional binary Siamese network achieved the highest overall performance.

The results highlight the effectiveness of class-specific similarity learning for robust compared to direct discriminant approaches.