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BackgroundBrain–computer interface (BCI) is an assistive technology that conveys users’ intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interactions. However, errors existing in BCI systems affect their performance greatly, which in turn confines the development and application of BCI technology. It has been demonstrated viable to extract error potential from electroencephalography recordings.MethodsThis study proposed a new approach of fusing multiple-channel features from temporal, spectral, and spatial domains through two times of dimensionality reduction based on neural network. 26 participants (13 males, mean age = 28.8 ± 5.4, range 20–37) took part in the study, who engaged in a P300 speller task spelling cued words from a 36-character matrix. In order to evaluate the generalization ability across subjects, the data from 16 participants were used for training and the rest for testing.ResultsThe total classification accuracy with combination of features is 76.7 %. The receiver operating characteristic (ROC) curve and area under ROC curve (AUC) further indicate the superior performance of the combination of features over any single features in error detection. The average AUC reaches 0.7818 with combined features, while 0.7270, 0.6376, 0.7330 with single temporal, spectral, and spatial features respectively.ConclusionsThe proposed method combining multiple-channel features from temporal, spectral, and spatial domain has better classification performance than any individual feature alone. It has good generalization ability across subject and provides a way of improving error detection, which could serve as promising feedbacks to promote the performance of BCI systems.

Objectives The aim of this study was to systematically assess the accuracy of circulating microRNAs (miRNAs) as a promising biomarker for sepsis via a meta-analysis. Methods PubMed, Cochrane Library, Embase, Web of Science, Scopus, and Ovid databases were searched up to April 3, 2020. The Quality in Prognostic Studies (QUADAS-2) tool was used to assess methodological quality. The pooled sensitivity (Sen), specificity (Spe), positive or negative likelihood ratios (PLR or NLR), diagnostic odds ratio (DOR), curve, and area under the curve (AUC) were calculated with 95% confidence interval (95% CI). The overall accuracy (OA) of miRNAs, procalcitonin (PCT), and C-reactive protein (CRP) was analyzed by the chi-square test. Results A total of 22 records were eligible for systematic review, including 2210 sepsis, 426 systemic inflammatory response syndrome (SIRS), and 1076 healthy controls (HC). The pooled Sen, Spe, and DOR of miRNAs were 0.80 (95% CI 0.75–0.83), 0.85 (95% CI 0.80–0.89), and 22 (15–32), respectively. The DOR of PCT and CRP were 17 (95% CI 4–68) and 7 (95% CI 1–48), respectively. The OA value of miRNAs (79.02%) and PCT (76.95%) were higher than CRP (61.22%) (P < 0.000). The subgroup analysis indicated that miRNAs in adults, serum type, downregulation of miRNA expression, criteria of Sepsis-3, internal reference of non-U6, and dysregulation expression of miR-223 had superior diagnostic accuracy. In addition, there was no significant publication bias among the included studies. Fagan’s nomogram showed valuable clinical utility. Conclusions Our meta-analysis indicated that the level of circulating miRNAs, particularly the miR-223, could be used as an indicator for sepsis.

A Brain-computer interface (BCI) is a novel communication system that translates brain signals into a control signal. Now with the appearance of the commercial EEG headsets and mobile smart platforms (tablet, smartphone), it is possible to develop the mobile BCI system, which can greatly improve the life quality of patients suffering from motor disease, such as amyotrophic lateral scleroses (ALS), multiple sclerosis, cerebral palsy and head trauma. This study adopted a 14-channel Emotiv EPOC headset and Microsoft surface pro 3 to realize a dialing system, which was represented by 4×3 matrices of alphanumeric characters. The performance of the online portable dialing system based on P300 is satisfying. The average classification accuracy reaches 88.75±10.57% in lab and 73.75±16.94% in metro, while the information transfer rate (ITR) reaches 7.17±1.80 and 5.05±2.17 bits/min respectively. This means the commercial EEG headset and tablet has good prospect in developing real time BCI system in realistic environments.

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BackgroundBrain-computer interfaces (BCIs) based on Steady State Visual Evoked Potential (SSVEP) have attracted more and more attentions for their short time response and high information transfer rate (ITR). The use of a high stimulation frequency (from 30 Hz to 40 Hz) is more comfortable for users and can avoid the amplitude-frequency problem, but the number of available phases for stimulation source is limited. To circumvent this deficiency, a novel protocol named Multi-Phase Cycle Coding (MPCC) for SSVEP-based BCIs was proposed in the present study.MethodsIn MPCC, each target is coded by a block word that includes a series of cyclic codewords, and each block word is corresponding to a certain flickering visual stimulus, which is a combination of multiple phases from an available phase set and flickers at single frequency. The methods of generating block code and extracting phase were presented and experiments were performed to investigate the feasibility of MPCC.ResultsThe optimal stimulation frequency was subject-specific, and the optimal duration was longer than 0.5 s. The BCI system with MPCC could achieve average discrimination accuracy 93.51 ± 5.62% and information transfer rate 33.77 ± 8.67%.ConclusionsThe MPCC has the error correction ability, can effectively increase the encoded targets and improve the performance of the system. Therefore, the MPCC is promising for practical BCIs.