Fast reconstruction of EEG signal compression sensing based on deep learning.

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals.

A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.

Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network.

EEG signals combined with deep learning play an important role in the study of human-computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields. The generator consists of a long short-term memory (LSTM) network and the discriminator consists of a convolutional neural network (CNN) which uses the gradient penalty-based Wasserstein distance as the loss function in model training. The model can learn the statistical features of EEG signals and generate EEG data that approximate real samples. In addition, the performance of the compressed sensing reconstruction model can be improved by using augmented datasets. Experiments show that, compared with the existing advanced data amplification techniques, the proposed model produces EEG signals closer to the real EEG signals as measured by RMSE, FD and WTD indicators. In addition, in the compressed reconstruction of EEG signals, adding the new data reduces the loss by about 15% compared with the original data, which greatly improves the reconstruction accuracy of the EEG signals' compressed sensing.

The protective mechanism of Tao Hong Si Wu decoction against breast cancer through regulation of EGFR/ERK1/2 signaling.

ETHNOPHARMACOLOGICAL RELEVANCE: Tao Hong Si Wu Decoction (THSWD), a traditional Chinese herbal medicine, is widely utilized in clinical settings, either alone or in combination with other medications, for the treatment of breast cancer. AIM OF THE STUDY: The specific targeting molecule(s) of THSWD and its associated molecular mechanisms remain unclear. This research aims to elucidate the underlying molecular mechanisms of THSWD in the treatment of breast cancer. MATERIALS AND METHODS: The pharmacological properties of THSWD were investigated in breast cancer cells and tumor tissues using a range of methods including Acridine Orange/Ethidium Bromide (AO/EB) staining, Transwell assay, flow cytometry, immunofluorescence assay, and breast cancer mice models. RESULTS: Our findings demonstrate that THSWD induces necrosis and/or apoptosis in breast cancer cells, while significantly inhibiting cell migration. Target proteins of THSWD in anticancer activity include EGFR, RAS, and others. THSWD treatment for breast cancer is associated with the EGFR/ERK1/2 signaling pathway. CONCLUSION: Our findings offer initial insights into the primary mechanism of action of THSWD in breast cancer treatment, indicating its potential as a complementary therapy deserving further investigation.

Blockage of L2HGDH-mediated S-2HG catabolism orchestrates macrophage polarization to elicit antitumor immunity.

The high infiltration of tumor-associated macrophages (TAMs) in the immunosuppressive tumor microenvironment prominently attenuates the efficacy of immune checkpoint blockade (ICB) therapies, yet the underlying mechanisms are not fully understood. Here, we investigate the metabolic profile of TAMs and identify S-2-hydroxyglutarate (S-2HG) as a potential immunometabolite that shapes macrophages into an antitumoral phenotype. Blockage of L-2-hydroxyglutarate dehydrogenase (L2HGDH)-mediated S-2HG catabolism in macrophages promotes tumor regression. Mechanistically, based on its structural similarity to α-ketoglutarate (α-KG), S-2HG has the potential to block the enzymatic activity of 2-oxoglutarate-dependent dioxygenases (2-OGDDs), consequently reshaping chromatin accessibility. Moreover, S-2HG-treated macrophages enhance CD8+ T cell-mediated antitumor activity and sensitivity to anti-PD-1 therapy. Overall, our study uncovers the role of blockage of L2HGDH-mediated S-2HG catabolism in orchestrating macrophage antitumoral polarization and, further, provides the potential of repolarizing macrophages by S-2HG to overcome resistance to anti-PD-1 therapy.