用深度模型提升脑机接口对视觉诱发电位的解码准确率
Deep Learning Architectures for Code-Modulated Visual Evoked Potentials Detection
- 设计CNN与孪生网络，通过数据驱动方法解码脑电信号
- 多类孪生网络达96.89%准确率，优于传统方法
- 适合需要高精度、单次试次解码的脑机接口研究者
基于非侵入式脑-机接口的编码调制视觉诱发电位（C-VEPs）需具备强鲁棒性的解码方法，以应对脑电信号的时间变异性与会话间噪声。本研究提出并评估了多种深度学习架构，包括用于63比特m序列重构与分类的卷积神经网络（CNN），以及基于相似性解码的孪生网络，同时对比了典型相关分析（CCA）基线。实验在13名健康成人上进行，采用单目标闪烁刺激获取脑电信号。所提深度模型显著优于传统方法，基于地球移动距离（EMD）及受限EMD的距离解码在时延变化下表现更鲁棒，优于欧氏与马氏距离。引入微小时间偏移的数据增强进一步提升了跨会话泛化能力。所有模型中，多类孪生网络表现最佳，平均准确率达96.89%，表明数据驱动深度架构在自适应非侵入式脑机接口系统中实现可靠单次试次解码的潜力。
原文摘要 · Abstract (English)
Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (C-VEPs) require highly robust decoding methods to address temporal variability and session-dependent noise in EEG signals. This study proposes and evaluates several deep learning architectures, including convolutional neural networks (CNNs) for 63-bit m-sequence reconstruction and classification, and Siamese networks for similarity-based decoding, alongside canonical correlation analysis (CCA) baselines. EEG data were recorded from 13 healthy adults under single-target flicker stimulation. The proposed deep models significantly outperformed traditional approaches, with distance-based decoding using Earth Mover's Distance (EMD) and constrained EMD showing greater robustness to latency variations than Euclidean and Mahalanobis metrics. Temporal data augmentation with small shifts further improved generalization across sessions. Among all models, the multi-class Siamese network achieved the best overall performance with an average accuracy of 96.89%, demonstrating the potential of data-driven deep architectures for reliable, single-trial C-VEP decoding in adaptive non-invasive BCI systems.
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