Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.
Sensors (Basel). 2023 Nov 8;23(22):9049. doi: 10.3390/s23229049.
We present a novel architecture designed to enhance the detection of Error Potential (ErrP) signals during ErrP stimulation tasks. In the context of predicting ErrP presence, conventional Convolutional Neural Networks (CNNs) typically accept a raw EEG signal as input, encompassing both the information associated with the evoked potential and the background activity, which can potentially diminish predictive accuracy. Our approach involves advanced Single-Trial (ST) ErrP enhancement techniques for processing raw EEG signals in the initial stage, followed by CNNs for discerning between ErrP and NonErrP segments in the second stage. We tested different combinations of methods and CNNs. As far as ST ErrP estimation is concerned, we examined various methods encompassing subspace regularization techniques, Continuous Wavelet Transform, and ARX models. For the classification stage, we evaluated the performance of EEGNet, CNN, and a Siamese Neural Network. A comparative analysis against the method of directly applying CNNs to raw EEG signals revealed the advantages of our architecture. Leveraging subspace regularization yielded the best improvement in classification metrics, at up to 14% in balanced accuracy and 13.4% in .
我们提出了一种新的架构，旨在提高在 ErrP 刺激任务中检测 ErrP 信号的能力。在预测 ErrP 存在的情况下，传统的卷积神经网络（CNN）通常接受原始 EEG 信号作为输入，其中包含与诱发电位和背景活动相关的信息，这可能会降低预测准确性。我们的方法涉及先进的单试（ST）ErrP 增强技术，用于在初始阶段处理原始 EEG 信号，然后在第二阶段使用 CNN 区分 ErrP 和非 ErrP 段。我们测试了不同的方法和 CNN 的组合。就 ST ErrP 估计而言，我们研究了各种方法，包括子空间正则化技术、连续小波变换和 ARX 模型。在分类阶段，我们评估了 EEGNet、CNN 和孪生神经网络的性能。与直接将 CNN 应用于原始 EEG 信号的方法进行比较分析，显示了我们架构的优势。利用子空间正则化技术在分类指标上取得了最佳的改进，在平衡准确性方面提高了 14%，在. 方面提高了 13.4%。
