A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes.

Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.

Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.

Objective.Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.Approach.In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.Main results.Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.Significance.This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.

Cytology, metabolomics, and proteomics reveal the grain filling process and quality difference of wheat.

Comparative proteomics and non-target metabolomics, together with physiological and microstructural analyses of wheat grains (at 15, 20, 25, and 30 days after anthesis) from two different quality wheat varieties (Gaoyou 5766 (strong-gluten) and Zhoumai 18) were performed to illustrate the grain filling material dynamics and to search for quality control genes. The differential expressions of 1541 proteins and 406 metabolites were found. They were mostly engaged in protein metabolism, stress/defense, energy metabolism, and amino acid metabolism, and the metabolism of stored proteins and carbohydrates was the major focus of the latter stages. The core proteins and metabolites in the growth process were identified, and the candidate genes for quality differences were screened. In conclusion, this study offers a molecular explanation for the establishment of wheat quality, and it aids in our understanding of the intricate metabolic network between different qualities of wheat at the filling stage.

An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition.

Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46 %, 92.15/5.13 %, 97.60/1.68 %(valence), and 97.48/1.42 %(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.

A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces.

In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively.

Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods.

Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance. However, such endeavour is rarely reported for invasive BCIs, especially those using deep learning methods. Two deep learning-based methods, referred to as Gumbel and STG, were proposed in this paper. They were evaluated using the Stereo-electroencephalography (SEEG) signals, and compared with three other methods, including manual selection, mutual information-based method (MI), and all channels (all channels without selection). The task is to classify the SEEG signals into five movements using channels selected by each method. When 10 channels were selected, the mean classification accuracies using Gumbel, STG (referred to as STG-10), manual selection, and MI selection were 65%, 60%, 60%, and 47%, respectively, whilst the accuracy was 59% using all channels (no selection). In addition, an investigation of the selected channels showed that Gumbel and STG have successfully identified the pre-central and post-central areas, which are closely related to motor control. Both Gumbel and STG successfully selected the informative channels in SEEG recordings while maintaining decoding accuracy. This study enables future high-throughput BCIs using deep learning methods, to identify useful channels and reduce computing and wireless transmission pressure.

Data augmentation for invasive brain-computer interfaces based on stereo-electroencephalography (SEEG).

Objective.Deep learning is increasingly used for brain-computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness. However, all the existing studies on brain signals were based on convolutional neural networks and ignored the temporal dependence. This paper attempted to enhance generative models by capturing the temporal relationship from a time-series perspective.Approach. A conditional generative network (conditional transformer-based generative adversarial network (cTGAN)) based on the transformer model was proposed. The proposed method was tested using a stereo-electroencephalography (SEEG) dataset which was recorded from eight epileptic patients performing five different movements. Three other commonly used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE), and conditional Wasserstein generative adversarial network with gradient penalty (cWGANGP). Using the proposed method, the artificial SEEG data was generated, and several metrics were used to compare the data quality, including visual inspection, cosine similarity (CS), Jensen-Shannon distance (JSD), and the effect on the performance of a deep learning-based classifier.Main results. Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE outputted inferior samples when visualized as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of CS and JSD and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively).Significance. This is the first time that DA methods have been applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.

Pseudo-submucosal tumor in the colon: seeing is believing.

A 65-year-old woman was admitted to our hospital with complaints of lower abdominal pain. Her physical examination was unremarkable. The results of routine laboratory testing were within the normal limits. In addition, abdominal CT was normal. Colonoscopy showed a cecum submucosal tumor with a pale yellow surface. Endoscopic ultrasound revealed homogeneous hypoechoic lesions originated from submucosal layer. ESD was subsequently performed to remove the submucosal lesion. During the ESD procedure, fecal outflowed from appendix opening . Yellow fecal-like material was visible after submucosal incision. The trap electrocut surface uplift showed more fecal attachment on the lamina propria surface, and myolayer integrity after clean the fecal (Fig1c), The final pathology of the surface bulge suggested hyperplasia (Fig1d). Patients were discharged with relieved lower abdominal pain. The final diagnosis was submucosal fecalith mimicking a submucosal tumor, eventually leads to chronic appendicitis. Common causes of cecal submucosal tumor include neuroendocrine tumors, lipomas, etc. There was few report about fecalith mimicking a submucosal tumor. ERTA is currently an effective endoscopic method for treating appendicitis combined with fecalith blockage. To our knowledge, this is the first report on a case of cecum submucosal fecalith mimicking a submucosal tumor and was successfully removed using endoscopy.

Endoloop pre-ligation assisted resection of a giant Brunner's gland adenoma of the duodenal bulb.

Brunner's gland adenoma (BGA), also known as Brunneroma or polypoid hamartoma, is a rare benign duodenal tumor that proliferates from Brunner's glands of the duodenum. They are usually asymptomatic and discovered by chance during endoscopy. Some giant lesions can sometimes present with chronic abdominal pain, nausea, vomiting, and anemia, including gastrointestinal bleeding and obstructive symptoms, and need to be resected by surgery or endoscopy. Here we report a giant BGA that was easily and safely removed by Endoloop pre-ligation assisted resection.

Combining microbiome and pseudotargeted metabolomics revealed the alleviative mechanism of Cupriavidus sp. WS2 on the cadmium toxicity in Vicia unijuga A.Br.

Cadmium (Cd) pollution is one of the most severe toxic metals pollution in grassland. Vicia unijuga (V. unijuga) A.Br. planted nearby the grassland farming are facing the risk of high Cd contamination. Here, we investigated the beneficial effects of a highly Cd tolerant rhizosphere bacterium, Cupriavidus sp. WS2, on Cd contaminated V. unijuga. Through plot experiments, we set up four groups of treatments: the control group (without WS2 or Cd), the Cd group (with only Cd addition), the WS2 group (with only WS2 addition), and the WS2/Cd group (with WS2 and Cd addition), and analyzed the changes in physiological indicators, rhizosphere microorganisms, and stem and leaf metabolites of V. unijuga. Results of physiological indicators indicated that Cupriavidus sp. WS2 had strong absorption and accumulation capacity of Cd, exogenous addition of strain WS2 remarkably decreased the Cd concentrations, and increased the plant heights, the biomass, the total protein concentrations, the chlorophyll contents and the photosynthetic rate in stems and leaves of V. unijuga under Cd stress. Cd treatment increased the abundance of Cd tolerant bacterial genera in rhizosphere microbiome, but these genera were down-regulated in the WS2/Cd group. Pseudotargeted metabolomic results showed that six common differential metabolites associated with antioxidant stress were increased after co-culture with WS2. In addition, WS2 activated the antioxidant system including glutathione (GSH) and catalase (CAT), reduced the contents of oxidative stress markers including malondialdehyde (MDA) and hydrogen peroxide (H2O2) in V. unijuga under Cd stress. Taken together, this study revealed that Cupriavidus sp.WS2 alleviated the toxicity of V. unijuga under Cd exposure by activating the antioxidant system, increasing the antioxidant metabolites, and reducing the oxidative stress markers.

Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals.

Surface electromyography (sEMG) signals are crucial in developing human-machine interfaces, as they contain rich information about human neuromuscular activities. OBJECTIVE: The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. METHODS: This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. RESULTS: The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. CONCLUSION: The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. SIGNIFICANCE: This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.

Influence of stone load on the outcome of same-session flexible ureteroscopy for bilateral upper urinary tract stones: a multicenter retrospective study.

Purpose: This study aimed to evaluate the efficacy and safety of same-session flexible ureteroscopy (fURS) for the treatment of bilateral upper urinary tract stones and to examine the influence of stone load on the outcome of same-session fURS, stratifying by total diameter of stones (TDS) ≤30 mm vs. >30 mm. Patients and methods: We retrospectively reviewed all cases of same-session fURS performed for bilateral upper urinary tract stones at four institutions between January 2017 and September 2020. All patients were divided into two groups based on TDS, ≤30 mm and >30 mm. Data on patient demographics, stone characteristics, surgical results, and complications were collected and analyzed for differences between the two groups. Stone-free rate (SFR) was defined as patients endoscopically stone-free or with radiological fragments <2 mm of each renal unit. Results: A total of 121 patients with bilateral upper urinary tract stones underwent same-session fURS, consisting of 73 patients in the TDS ≤ 30 mm group and 48 patients in the TDS > 30 mm group. The mean bilateral stone size was 28.2 ± 12.2 mm (range: 9.1-38.4 mm), with a mean operating time of 97.1 ± 39.6 min (range: 19-220 min). The SFR was 54.5% after the first fURS, and SFR increased to 97.5% after re-fURS for residual stones. The operation time for the TDS > 30 mm group was longer than that of the TDS ≤ 30 mm group (85.1 ± 36.5 vs. 115.4 ± 37.4 min, p < 0.001). The SFR after the first fURS was significantly lower in the TDS > 30 mm group than in the TDS ≤ 30 mm group (25.0% vs. 73.9%, p < 0.001). Although there was no statistically significant difference in overall SFR between the two groups (93.7% vs. 100%, p = 0.060), the rate of re-fURS for residual stones was higher in the TDS > 30 mm group than in the TDS ≤ 30 mm group (75% vs. 26%, p < 0.001). There were no significant differences in length of hospital stay (LOS) (2.2 ± 0.7 vs. 2.3 ± 1.0, p = 0.329) or complication rate (10.9% vs. 14.6%, p = 0.582) between the two groups. Conclusion: The results suggested that same-session fURS can be effectively performed with a low complication rate. A higher SFR after the first fURS can be achieved in the case of bilateral upper urinary tract stones with TDS ≤ 30 mm, and priority should be given to same-session fURS.

Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing.

Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.

Super-enhancer-associated SNHG15 cooperating with FOSL1 contributes to bladder cancer progression through the WNT pathway.

Small nucleolar RNA host gene 15 (SNHG15) plays an oncogenic role in many cancers. However, the role of SNHG15 in bladder cancer (BLCA) remains unclear. In this study, the regulation of SNHG15 on the activities of BLCA cells (T24 and RT112) was investigated. In detail, super-enhancers (SEs), differentially expressed genes, and functional enrichment were detected by bioinformatic analyses. Mutant cell lines lacking SNHG15-SEs were established using CRISPR-Cas9. Relative gene expression was detected by quantitative polymerase chain reaction (qPCR), western blot, in situ hybridization, and immunohistochemistry assays. Cell senescence, apoptosis, viability, and proliferation were measured. Chromatin immunoprecipitation (ChIP)-qPCR and luciferase reporter gene assays were conducted to analyze the interactions between genes. A novel super-enhancer of SNHG15 (SNHG15-SEs) was discovered in several BLCA datasets. The deletion of SNHG15-SEs resulted in a significant downregulation of SNHG15. Mechanistically, the core active region of SNHG15-SEs recruited the transcription factor FOSL1 to facilitate the SNHG15 transcription, thereby inducing the proliferation and metastasis of BLCA cells. Deletion of SNHG15-SEs inhibited the growth and metastasis of T24 and RT112 cells by inactivating the WNT/CTNNB1 pathway activation. Overexpression of FOSL1 in SNHG15-SEs restored the cell proliferation and metastasis. Next, a xenograft mouse model showed that SNHG15-SEs deletion inhibited the proliferation and metastasis of BLCA cells in vivo. Collectively, our data indicate that SNHG15-SEs recruit FOSL1 to promote the expression of SNHG15 which interacts with CTNNB1 in the nucleus to activate the transcription of ADAM12, leading to the malignance of BLCA cells.

Best practices in assessing cardiac protein O-GlcNAcylation by immunoblot.

The modification of serine and threonine amino acids of proteins by O-linked N-acetylglucosamine (O-GlcNAc) regulates the activity, stability, function, and subcellular localization of proteins. Dysregulation of O-GlcNAc homeostasis is well established as a hallmark of various cardiac diseases, including cardiac hypertrophy, heart failure, complications associated with diabetes, and responses to acute injuries such as oxidative stress and ischemia-reperfusion. Given the limited availability of site-specific O-GlcNAc antibodies, studies of changes in O-GlcNAcylation in the heart frequently use pan-O-GlcNAc antibodies for semiquantitative evaluation of overall O-GlcNAc levels. However, there is a high degree of variability in many published cardiac O-GlcNAc blots. For example, many blots often have regions that lack O-GlcNAc positive staining of proteins either below 50 or above 100 kDa. In some O-GlcNAc blots, only a few protein bands are detected, while in others, intense bands around 75 kDa dominate the gel due to nonspecific IgM band staining, making it difficult to visualize less intense bands. Therefore, the goal of this study was to develop a modifiable protocol that optimizes O-GlcNAc positive banding of proteins in cardiac tissue extracts. We showed that O-GlcNAc blots using CTD110.6 antibody of proteins ranging from <30 to ∼450 kDa could be obtained while also limiting nonspecific staining. We also show that some myofilament proteins are recognized by the CTD110.6 antibody. Therefore, by protocol optimization using the widely available CTD110.6 antibody, we found that it is possible to obtain pan-O-GlcNAc blots of cardiac tissue, which minimizes common limitations associated with this technique.NEW & NOTEWORTHY The post-translational modification of proteins by O-linked N-acetylglucosamine (O-GlcNAc) is recognized as mediating cardiac pathophysiology. However, there is considerable variability in the quality of O-GlcNAc immunoblots used to evaluate changes in cardiac O-GlcNAc levels. Here we show that with relatively minor changes to a commonly used protocol it is possible to minimize the intensity of nonspecific bands while also reproducibly generating O-GlcNAc immunoblots covering a range of molecular weights from <30 to ∼450 kDa.

Spatio-temporal evolution of human neural activity during visually cued hand movements.

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.