Traditional visual brain-computer interfaces (BCIs) preferred to use large-size stimuli attract the user's attention and elicit distinct electroencephalography (EEG) features. However, are of no interest users as they just serve hidden codes behind characters. Furthermore, using stronger could cause fatigue other adverse symptoms users. Therefore, it's imperative for BCIs small inconspicuous code characters.This study developed a new BCI speller based on miniature asymmetric evoked...

Event-related potentials (ERPs) are one of the most popular control signals for brain-computer interfaces (BCIs). However, they very weak and sensitive to experimental settings including paradigms, stimulation parameters even surrounding environments, resulting in a diversity ERP patterns across different BCI experiments. It's still challenge develop general decoding algorithm that can adapt diversities datasets with small training sets. This study compared recently developed algorithm,...

Currently, ensemble task-related component analysis (eTRCA) and task discriminative (TDCA) are the state-of-the-art algorithms for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, training BCIs requires multiple calibration trials. With insufficient data, accuracy of BCI will degrade, or even become invalid with only one trial. collecting a large amount electroencephalography (EEG) data is time-consuming laborious process, which hinders practical...

. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator how well BCIs can decode the brain's intentions. No studies reported BCI system with over 200 targets.

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these is especially important for network-based approaches to human disease, instance identify coherent modules (subnetworks) that can inform functional mechanisms pathological pathways. Yet, network analysis across significantly hampered by the paucity robust computationally-efficient methods. Building on...

Expanding the application possibilities of brain-computer interfaces (BCIs) is possible through their implementation in mixed reality (MR) environments. However, visual stimuli are displayed against a realistic scene MR environment, which degrades BCI performance. The purpose this study was to optimize stimulus colors order improve MR-BCI system's In 10-command SSVEP-BCI deployed. Various and background for system were tested optimized offline online experiments. Color contrast ratios (CCRs)...

Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal noise ratio and information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.Approach.This study proposed novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited advantages both spatial filtering deep learning (DL)....

Spatial division multiple access (SDMA) is a way of encoding BCI systems based on spatial distribution brain signal characteristics. However, SDMA-BCI EEG had poor system performance limited by resolution. MEG-EEG fusion modality analysis can help solve this problem. According retina-cortical relationship, study used stimulus out the central visual field and tiny fixation points to construct 16-command SDMA coded system. We achieved synchronously acquiring MEG signals from 10 subjects....

Compromised functional integration between cerebral subsystems and dysfunctional brain network organization may underlie the neurocognitive deficits seen in psychiatric disorders. Applying topological measures from science to imaging data allows quantification of complex connectivity. While this approach has recently been used further elucidate nature dysfunction schizophrenia, value applying preclinical models disease not recognized. For first time, we apply both established derived...

Abstract Objective. P300s are one of the most studied event-related potentials (ERPs), which have been widely used for brain–computer interfaces (BCIs). Thus, fast and accurate recognition is an important issue BCI study. Recently, there emerges a lot novel classification algorithms P300-speller. Among them, discriminative canonical pattern matching (DCPM) has proven to work effectively, in spatial (DSP) filter can significantly enhance features P300s. However, ERPs space varies with time,...

. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG's capabilities, specifically optically pumped magnetometers (OPM-MEG), proves be promising avenue advancing MEG-BCIs, owing its exceptional sensitivity portability. This study harnesses power of high-frequency steady-state visual evoked...

Abstract Background The quantification of experimentally-induced alterations in biological pathways remains a major challenge systems biology. One example this is the quantitative characterization defined, established metabolic from complex metabolomic data. At present, disruption given pathway inferred data by observing an alteration level one or more individual metabolites present within that pathway. Not only approach open to subjectivity, as participate multiple pathways, but it also...

Anxiety disorder is a mental illness that involves extreme fear or worry, which can alter the balance of chemicals in brain. This change and evaluation anxiety state are accompanied by comprehensive treatment procedure. It well-known chiefly based on psychotherapy drug therapy, there no objective standard evaluation. In this paper, proposed method focuses examining neural changes to explore effect mindfulness regulation accordance with neurofeedback patients anxiety. We designed closed...

Brain–computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with lack of variety. It imperative to adapt more voluntary mental activities for control, which can induce separable electroencephalography (EEG) features. This study aims demonstrate brain function timing prediction, i.e., expectation upcoming time intervals, accessible BCIs. Eighteen subjects were selected this study. They trained precise idea two...

Although massive open online courses (MOOC) have successfully attracted a large number of learners, the low completion rate has severely hindered development MOOC. The complex and diverse information on forum often makes many students' urgent problems unable to be solved in time. Therefore, how identify posts from becomes critical problem solved. This paper presents model for identifying “urgent” that require immediate attention instructors. It is first time deep learning methods been...

Background Endogenous circadian rhythms result from genetically-encoded molecular clocks, whose components and downstream output factors cooperate to generate cyclic changes in activity. Mating is an important activity of mosquitoes, however, the key aspects mating rhythm patterns their regulatory mechanisms two vector mosquito species, Aedes albopictus Culex quinquefasciatus , remain unclear. Methodology/Principal findings We determined compared diel these species discovered that Ae . had...

Steady State Visual Evoked Potentials (SSVEPs) have been widely used in Brain-Computer Interfaces (BCIs). SSVEP-BCIs advantages of high classification accuracy, information transfer rate, and strong anti-interference ability. Traditional studies mostly low/medium frequency SSVEPs as system control signals. However, visual flickers with frequencies are uncomfortable, even cause fatigue epilepsy seizure. High-frequency SSVEP is a promising approach to solve these problems, but its miniature...

Abstract Objective . Decomposition methods are efficient to decode steady-state visual evoked potentials (SSVEPs). In recent years, the brain–computer interface community has also been developing deep learning networks for decoding SSVEPs. However, there is no clear evidence that current models outperform decomposition on SSVEP tasks. Many studies lacked comparison with state-of-the-art in a fair environment. Approach This study proposed novel network design motivated by works of methods....

Abstract Objective. Currently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy depends deeply on number of training samples, system performance would a dramatic drop when dataset decreased to small size. To date, no study has been reported incorporate unsupervised learning information from testing trails into construction supervised...

. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant (TDCA). However, training individual models requires tedious time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation...

Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all of chain.

Motor imagery (MI) has been demonstrated beneficial in motor rehabilitation patients with movement disorders. In contrast simple limb imagery, less work was reported about the effective connectivity networks of compound which involves several parts limbs. This aimed to investigate differences information flow patterns between and imagery. Ten subjects participated experiment involving three tasks (left hand, right feet) (both hands, left hand combined foot, foot). The causal interactions...