Key result
Hybrid EWT-LSTM neural network with Bayesian optimization outperforms traditional methods for estimating effective connectivity.
Why the study?
Existing effective connectivity methods have drawbacks including weaknesses in hyperparameter and time lag selection and difficulty dealing with the non-stationarity of brain signals like EEG.
A novel hybrid neural network model using EWT, LSTM, and Bayesian Optimization improves the estimation of nonlinear effective connectivity in nonstationary EEG signals.
May enhance EEG effective connectivity estimation; leaves open clinical validation before practice adoption.
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.
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Esmaeil-Zadeh et al. (2023) studied Attention deficit hyperactivity disorder (ADHD). Hybrid neural network model using Empirical Wavelet Transform (EWT) and LSTM with Bayesian Optimization vs. LSTM, CNN-LSTM, GRU, RNN, MLP, LGC, KGC, PDC, and DTF was evaluated on Effective connectivity estimation performance and Granger Causality. A hybrid neural network model using Empirical Wavelet Transform and LSTM with Bayesian Optimization was superior to other neural networks and traditional methods for estimating effective connectivity.
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