IEEE J Biomed Health Inform. 2024 Oct;28(10):5696-5707. doi: 10.1109/JBHI.2023.3327734. Epub 2024 Oct 3.
Accurately measuring nonlinear effective connectivity is a crucial step in investigating brain functions. Brain signals like EEG is nonstationary. Many effective connectivity methods have been proposed but they have drawbacks in their models such as a weakness in proposing a way for hyperparameter and time lag selection as well as dealing with non-stationarity of the time series. This paper proposes an effective connectivity model based on a hybrid neural network model which uses Empirical Wavelet Transform (EWT) and a long short-term memory network (LSTM). The best hyperparameters and time lag are selected using Bayesian Optimization (BO). Due to the importance of generalizability in neural networks and calculating GC, an algorithm was proposed to choose the best generalizable weights. The model was evaluated using simulated and real EEG data consisting of attention deficit hyperactivity disorder (ADHD) and healthy subjects. The proposed model's performance on simulated data was evaluated by comparing it with other neural networks, including LSTM, CNN-LSTM, GRU, RNN, and MLP, using a Blocked cross-validation approach. GC of the simulated data was compared with GRU, linear Granger causality (LGC), Kernel Granger Causality (KGC), Partial Directed Coherence (PDC), and Directed Transfer Function (DTF). Our results demonstrated that the proposed model was superior to the mentioned models. Another advantage of our model is robustness against noise. The results showed that the proposed model can identify the connections in noisy conditions. The comparison of the effective connectivity of ADHD and the healthy group showed that the results are in accordance with previous studies.
准确测量非线性有效连通性是研究大脑功能的关键步骤。脑电等脑信号是非平稳的。已经提出了许多有效的连通性方法，但它们在模型中有一些缺点，例如在提出超参数和时滞选择的方法以及处理时间序列的非平稳性方面存在弱点。本文提出了一种基于混合神经网络模型的有效连通性模型，该模型使用经验小波变换（EWT）和长短期记忆网络（LSTM）。使用贝叶斯优化（BO）选择最佳超参数和时滞。由于神经网络的泛化能力和计算 GC 的重要性，提出了一种算法来选择最佳的可泛化权重。该模型使用包含注意力缺陷多动障碍（ADHD）和健康受试者的模拟和真实 EEG 数据进行了评估。通过使用阻塞交叉验证方法，将所提出的模型与其他神经网络（包括 LSTM、CNN-LSTM、GRU、RNN 和 MLP）进行比较，评估了其在模拟数据上的性能。模拟数据的 GC 与 GRU、线性格兰杰因果关系（LGC）、核格兰杰因果关系（KGC）、偏置定向相干性（PDC）和定向传递函数（DTF）进行了比较。我们的结果表明，所提出的模型优于上述模型。我们的模型的另一个优点是对噪声的鲁棒性。结果表明，该模型可以在噪声条件下识别连接。ADHD 和健康组的有效连通性比较表明，结果与先前的研究一致。
