TRCA-Net: using TRCA filters to boost the SSVEP classification with convolutional neural network

Abstract

Objective. The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research due to its simple system, less training data, and high information transfer rate (ITR). There are currently two prominent methods dominating the classification of SSVEP signals. One is the knowledge-based task-related component analysis (TRCA) method, whose core idea is to find the spatial filters by maximizing the inter-trial covariance. The other is the deep learning-based approach, which directly learns a classification model from data. However, how to integrate the two methods to achieve better performance has not been studied before. Approach. In this study, we develop a novel algorithm named Task Related Component Analysis-Net (TRCANet) to enhance SSVEP signal classification, which enjoys the advantages of both the knowledge-based method and the deep model. Specifically, the proposed TRCA-Net first performs TRCA to obtain spatial filters, which extract task-related components of data. Then the TRCA-filtered features from different filters are rearranged as new multi-channel signals for a deep convolutional neural network for classification. Introducing the TRCA filters to a deep learning-based approach improves the signal-to-noise ratio (SNR) of input data, hence benefiting the deep learning model. Main results. We evaluate the performance of TRCA-Net using two publicly available large-scale benchmark datasets, and the results demonstrate the effectiveness of TRCA-Net. Additionally,
offline and online experiments separately testing ten and five subjects further validate the robustness of TRCA-Net. Further,
we conduct ablation studies on different convolutional neural network (CNN) backbones and demonstrate that our approach
can be transplanted into other CNN models to boost their performance. Significance. The proposed approach is believed to
have a promising potential for SSVEP classification and promote its practical applications in communication and control.

Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy

“…Combining DL methods with TRCA methods is a promising direction for enhancing the performance of SSVEP-BCIs. Deng et al developed a novel algorithm named TRCA-Net to enhance SSVEP signal classification, which enjoys the advantages of both the knowledge-based TRCA method and the deep learning-based approach [44]. However, the neural network-based methods require ample training trials.…”

Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis

“…SSVEP-based BCI systems typically have several benefits over alternative methods, including a higher information transfer rate (ITR) ( Sadeghi and Maleki, 2019 ), better classification accuracy, and fewer recording electrodes. These systems also require less training and have higher user and patient validity ( Sadeghi and Maleki, 2018 ; Maleki and Azadimoghadam, 2022 ; Deng et al, 2023 ).…”