Objective To introduce the multivariate random effects model (MREM) in the meta-analysis of diagnostic tests with multiple thresholds. Methods This paper expanded and extended the bivariate random effects model (BREM) to develop the MREM, and implemented it in the SAS Proc NLMIXED procedure. Results The MREM could obtain the study specific ROC curve for each study through empirical Bayes estimation, and the summary ROC curve located in between all study specific ROC curves evenly, while the BREM couldn’t obtain the study specific ROC curve. In addition, in the aspect of parameters estimation, the MREM didn’t depend on the choice of the diagnosis threshold and the type of SROC. The MREM could get only one SROC curve and its AUC was between the AUC of the 5 types of SROC from BREM, so it could avoid overestimation or underestimation. Conclusion The MREM can fully exploit the data, obtain stable and reliable results, and have a good application value in meta-analysis of diagnostic tests with multiple thresholds.
Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.
The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.
With the transformation of modern medical models, patient-reported outcomes, clinician-reported outcomes, observer-reported outcomes, and performance outcomes have become internationally recognized clinical outcome assessment indicators, and scales have also become important evaluation tools, among which translation and cross-cultural adaptation are one of the important sources of scales. However, at present, there are fewer guidelines for scale translation in China. At present, domestic scale translation has not yet been unified and standardized in clinical reporting. Most translation reports provide readers with incomplete information, which affects the development of scale translation, and the methodology related to the translation of clinical outcome assessment scales still focuses on patient-reported outcome scales, which creates a gap in terms of the recommendations for the rest of the types of translations, a gap which leads to inconsistencies in the translation methodology and process. In this paper, we will develop specific translation methods and processes for each of the four current types of clinical outcome assessments by combining scale translation guidelines to support a standardized approach to translation, cross-cultural adaptation, and linguistic validation for use in standardizing the process of recommending translations of patient-reported outcome scales, clinical-reported outcome scales, observer-reported outcome scales, and behavioral outcome scales.
High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.