Five days of integrative body-mind training (IBMT) improves attention and self-regulation in comparison with the same amount relaxation training. This paper explores underlying mechanisms this finding. We measured physiological brain changes at rest before, during, after 5 IBMT During training, group showed significantly better reactions heart rate, respiratory amplitude skin conductance response (SCR) than control. Differences rate variability (HRV) EEG power suggested greater involvement...

Transparent electrodes that form seamless contact and enable optical interrogation at the electrode-brain interface are potentially of high significance for neuroscience studies. Silk hydrogels can offer an ideal platform transparent neural interfaces owing to their superior biocompatibility. However, conventional silk too weak have difficulties integrating with highly conductive stretchable electronics. Here, a hydrogel electrode based on poly(3,4-ethylenedioxythiophene):polystyrene...

Objective: The key principle of motor imagery (MI) decoding for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from spectral, spatial, and temporal domains jointly efficiently, whereas limited, noisy, non-stationary EEG samples challenge the advanced design algorithms. Methods: Inspired by concept cross-frequency coupling its correlation with different behavioral tasks, this paper proposes a lightweight Interactive Frequency...

A hybrid modality brain-computer interface (BCI) is proposed in this paper, which combines motor imagery with selective sensation to enhance the discrimination between left and right mental tasks, e.g., classification left/ stimulation right/ imagery. In paradigm, wearable vibrotactile rings are used stimulate both skin on wrists. Subjects required perform tasks according randomly presented cues (i.e., hand imagery, or sensation). Two-way ANOVA statistical analysis showed a significant group...

Many machine learning methods have been applied on the biomedical named entity recognition and achieve good results GENIA corpus.However most of those reply feature engineering which is labor-intensive.In this paper，huge potential information represented as word vectors are generated by neutral networks based unlabeled text files.We propose a Biomedical Named Entity Recognition (Bio-NER) method deep neural network architecture has multiple layers each layer abstracts features upon lower...

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical usage would facilitate the selection suitable end-users and improve efficiency In current study, we proposed two physiological variables, i.e., laterality index (LI)...

Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success adaptive deep brain stimulation (aDBS) therapy. Here, we investigated potential use feature engineering machine learning methods for more accurate rest PD.We analyzed local field (LFP) recordings from subthalamic nucleus region 12 patients with PD (16 recordings). To explore optimal biomarkers best performing classifier, performance state-of-the-art (ML) algorithms various features LFPs...

High performance of the brain-computer interface (BCI) needs efficient algorithms to extract discriminative features from raw electroencephalography (EEG) signals. In this paper, we present a novel scheme spatial spectral for motor imagery-based BCI. The learning task is formulated by maximizing mutual information between (MMISS) and class labels, which unique objective function directly related Bayes classification error optimized. are assumed follow parametric Gaussian distribution, has...

Complementary to brain–computer interface (BCI) based on motor imagery (MI) task, sensory (SI) task provides a way for BCI construction using brain activity from somatosensory cortex. The underlying neurophysiological correlation between SI and MI was unclear difficult measure through behavior recording. In this study, we investigated the neurodynamic of motor/tactile tactile sensation tasks high-density electroencephalogram (EEG) recording, EEG source imaging used systematically explore...

Objective: Brain-computer interface (BCI) decoding accuracy plays a crucial role in practical applications. With accurate feedback, BCI-based therapy determines beneficial neural plasticity stroke patients. In this study, we aimed at improving sensorimotor rhythm (SMR) based BCI performance by integrating motor tasks with tactile stimulation. Methods: Eleven patients were recruited for three experimental conditions, i.e., attempt (MA) condition, stimulation (TS) and stimulation-assisted...

Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of imagery (MI) attentional orientation (SAO). In this paper, we hypothesize that a combination these two signal modalities provides improvements in brain-computer interface (BCI) performance with respect to using methods separately, generate novel types multi-class BCI systems. Thirty subjects were randomly divided into Control-Group Hybrid-Group. Control-Group, performed left right hand (i.e.,...

Abstract Objective. Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use Riemannian-space features and temporal dynamics electrocorticography (ECoG) signal combined with modern machine learning (ML) tools to improve motor accuracy at level fingers. Approach. We selected a set informative biomarkers that correlated evaluated performance state-of-the-art ML algorithms on brain-computer interface (BCI) competition IV...

In this work, mechanical vibrotactile stimulation was applied to subjects’ left and right wrist skins with equal intensity, a selective sensation perception task performed achieve two types of selections similar motor imagery Brain-Computer Interface. The proposed system based on event-related desynchronization/synchronization (ERD/ERS), which had correlation processing afferent inflow in human somatosensory system, attentional effect modulated the ERD/ERS. experiments were carried out nine...

DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation transcription, and gene expression. Developing an effective model identifying is urgent research problem. Up to now, many methods have been proposed, but most them focus on only one classifier cannot make full use the large number negative samples improve predicting performance. This study proposed a predictor called enDNA-Prot protein identification by employing ensemble...

Objective. Lack of efficient calibration and task guidance in motor imagery (MI) based brain-computer interface (BCI) would result the failure communication or control, especially patients, such as a stroke with impairment intact sensation, locked-in state amyotrophic lateral sclerosis, which sources data for may worsen subsequent decoding. In addition, enhancing proprioceptive experience MI might improve BCI performance. Approach. this work, we propose new calibrating methodology to further...

We propose and test a novel brain-computer interface (BCI) based on imagined tactile sensation. During an sensation, referred to as somatosensory attentional orientation (SAO), the subject shifts maintains attention body part, e.g., left or right hand. The SAO can be detected from EEG recordings for establishing communication channel. To hypothesis that different parts discriminated EEG, 14 subjects were assigned group who received actual sensory stimulation (STE-Group), 18 only (SAO-Group)....

Brain-computer interface (BCI) has attracted great interests for its effectiveness in assisting disabled people. However, due to the poor BCI performance, this technique is still far from daily-life applications. One of critical issues confronting research how enhance performance. This study aimed at improving motor imagery (MI) based accuracy by integrating MI tasks with unilateral tactile stimulation (Uni-TS). The effects were tested on both healthy subjects and stroke patients a...

A network of modular protein complexes inside a cell coordinates many biological processes and is known as protein-protein interaction (PPI) network. APPI can be modeled graph, in which edges represent interactions among proteins, sub graphs complexes. Previous methods for complex mining from PPI mainly focused on few topological features like density degree statistics based the assumption that proteins are highly interactive with each other thus form dense subgraphs. While this true some...

Brain Computer Interface (BCI) inefficiency indicates that there would be 10% to 50% of users are unable operate Motor-Imagery-based BCI systems. Importantly, the almost all previous studieds on were based tests Sensory Motor Rhythm (SMR) feature. In this work, we assessed occurrence with SMR and Movement-Related Cortical Potential (MRCP) features.A pool datasets resting state movements related EEG signals was recorded 93 subjects during 2 sessions in separated days. Two methods, Common...

A proportion of users cannot achieve adequate brain-computer interface (BCI) control. The diversity BCI modalities provides a way to solve this emerging issue. Here, we investigate the accuracy somatosensory based on sensory imagery (SI). During SI tasks, subjects were instructed imagine tactile sensation and maintain attention corresponding hand, as if there was stimulus skin wrist. performance across 106 healthy in left- right-hand discrimination 78.9±13.2%. In 70.7% above 70%. task...

Abstract Bioprosthetic heart valves (BHVs) for transcatheter replacement often face deterioration due to thrombosis, inflammation, and calcification, which are irreversible. Here, a multidimensional endothelium‐mimicking healable hydrogel shielded BHV that not only withstand the complex valvular physiological hemodynamic environment but also able reverse damage‐induced structural degeneration by in situ healing is proposed. Polydopamine/selenocystamine nanoparticles with photothermal effect...

Subthreshold depression (SD) is a prevalent condition among young adults, significantly increasing the risk of developing major depressive disorder (MDD). While symptoms MDD are well-documented, network structure and key SD, which forms complex, interdependent system, have not been fully elucidated. This study sought to identify central their interconnections within symptom in adults with SD. A total 834 Chinese SD completed 21-item Beck Depression Inventory 2nd version (BDI-II) were...