Effects of anesthesia on conventional and speckle tracking echocardiographic parameters in a mouse model of pressure overload

Genetically‑modified mice are widely applied in cardiovascular studies as model organisms. Echocardiography is a key tool for evaluating cardiac and hemodynamic functions in mice. The present study aimed to examine the effects of isoflurane (ISF) on conventional and speckle tracking echocardiography (STE) parameters under healthy and pathological conditions using a murine model of pressure overload. In addition, the optimal dose of ISF in the process of echocardiographic measurement, with minimum cardiac contraction depression, was investigated. Conventional echocardiographic and STE examinations were performed on 38 adult C57BL/6 male mice. The mice were divided into the following three groups: The sham (n=15); mild thoracic aortic banding (TAB; n=15); and severe TAB (n=8) groups. ISF was administered under deep anesthesia (DA; 1‑2% ISF), light anesthesia (LA; 0.5‑1% ISF) and immediately prior to the mice waking up (awake; 0‑0.5% ISF). Conventional echocardiographic parameters were preserved within the sham and mild TAB groups (P>0.05 for each parameter) under LA and awake conditions. However, under DA conditions, the majority of these parameters were reduced compared with the LA and awake conditions (P<0.05). In the severe TAB group, conventional echocardiographic parameters remained constant under LA, DA and awake conditions. STE parameters in the groups remained similar between the LA and awake conditions, but were significantly reduced under DA conditions. Therefore, conventional echocardiography and STE may be performed using LA induced with low doses of ISF, under various pathological conditions without affecting cardiac function

BCI Competition IV – Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection

Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set

A Transformer-based deep neural network model for SSVEP classification

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data become urgent. In recent years, developing the methods that can work in inter-subject classification scenario has become a promising new direction. As the popular deep learning model nowadays, Transformer has excellent performance and has been used in EEG signal classification tasks. Therefore, in this study, we propose a deep learning model for SSVEP classification based on Transformer structure in inter-subject classification scenario, termed as SSVEPformer, which is the first application of the transformer to the classification of SSVEP. Inspired by previous studies, the model adopts the frequency spectrum of SSVEP data as input, and explores the spectral and spatial domain information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) is proposed to further improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12-class task; Dataset 2: 35 subjects, 40-class task) in the inter-subject classification scenario. The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate, compared with other baseline methods. The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems

Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively

A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder

10.1371/journal.pone.0046692PLoS ONE710

A brain-computer interface based cognitive training system for healthy elderly: A randomized control pilot study for usability and preliminary efficacy

10.1371/journal.pone.0079419PLoS ONE811-POLN