GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

Early detection and diagnosis of Autism Spectrum Disorder (ASD) can significantly improve the quality of life for affected individuals. Identifying ASD based on brain functional connectivity (FC) poses a challenge due to the high heterogeneity of subjects’ fMRI data in different sites. Meanwhile, deep learning algorithms show efficacy in ASD identification but lack interpretability. In this paper, a novel approach for ASD recognition is proposed based on graph attention networks. Specifically, we treat the region of interest (ROI) of the subjects as node, conduct wavelet decomposition of the BOLD signal in each ROI, extract wavelet features, and utilize them along with the mean and variance of the BOLD signal as node features, and the optimized FC matrix as the adjacency matrix, respectively. We then employ the self-attention mechanism to capture long-range dependencies among features. To enhance interpretability, the node-selection pooling layers are designed to determine the importance of ROI for prediction. The proposed framework are applied to fMRI data of children (younger than 12 years old) from the Autism Brain Imaging Data Exchange datasets. Promising results demonstrate superior performance compared to recent similar studies. The obtained ROI detection results exhibit high correspondence with previous studies and offer good interpretability.

to the fully connected layer.…”

Section: Methodsmentioning

confidence: 99%

A novel approach for ASD recognition based on graph attention networks

Wang,

Xiao,

et al. 2024

“…The pre-trained model consists of an embedding block, a stylefeature randomization block, a multi-level temporal-spectral feature extraction network (Hu et al, 2018;Li et al, 2020;Wan et al, 2023b), a category classifier, and a patient discriminator, as illustrated in Figure 1. The embedding block extends the data across multiple channels to enhance the discriminative properties.…”

Section: Pre-trained Modelmentioning

confidence: 99%

Epileptic focus localization using transfer learning on multi-modal EEG

Yang,

Li,

Luo

et al. 2023

The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG). A 10-fold cross-validation approach was applied to validate the performance of the pre-trained model on the Bern-Barcelona and Bonn datasets, achieving accuracy rates of 94.50 and 97.50%, respectively. The experimental results have demonstrated that the pre-trained model outperforms the competitive state-of-the-art baselines in terms of accuracy, sensitivity, and negative predictive value. Furthermore, we fine-tuned our pre-trained model using the epilepsy dataset from Chongqing Medical University and tested it using the leave-one-out cross-validation method, obtaining an impressive average accuracy of 90.15%. This method shows significant feature differences between epileptic and non-epileptic channels. By extracting data features using neural networks, accurate classification of epileptic and non-epileptic channels can be achieved. Therefore, the superior performance of the model has demonstrated that the proposed method is highly effective for localizing epileptic focus and can aid physicians in clinical localization diagnosis.

“…Generative adversarial networks (GANs) have gained prominence in the fields of medical image analysis (Hong et al, 2022 ) and functional time series reconstruction as a powerful tool for generating synthetic data that closely resemble real-world time series data (Luo et al, 2018 , 2019 ). In addition, Transformer's self-attention mechanism has been successfully applied in medical data analysis (Li et al, 2023 ; Wan et al, 2023a , b , c ). The parallel processing capability and adaptability to various data types make it a versatile tool for time series generation (Tang and Matteson, 2021 ; Zerveas et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

Zuo,

Li,

Shi

et al. 2024

IntroductionThe blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.MethodsIn this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics.ResultsWe theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement.ConclusionOverall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.