A Neural Network-Based Optimal Spatial Filter Design Method for Motor Imagery Classification

In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.     

Tilt Table Therapies for Patients with Severe Disorders of Consciousness: A Randomized,Controlled Trial

One major aim of the neurological rehabilitation of patients with severe disorders of consciousness (DOC) is to enhance patients’ arousal and ability to communicate. Mobilization into a standing position by means of a tilt table has been shown to improve their arousal and awareness. However, due to the frequent occurrence of syncopes on a tilt table, it is easier to accomplish verticalization using a tilt table with an integrated stepping device. The objective of this randomized controlled clinical trial was to evaluate the effectiveness of a tilt table therapy with or without an integrated stepping device on the level of consciousness. A total of 50 participants in vegetative or minimally conscious states 4 weeks to 6 month after injury were treated with verticalization during this randomized controlled trial. Interventions involved ten 1-hour sessions of the specific treatment over a 3-week period. Blinded assessors made measurements before and after the intervention period, as well as after a 3-week follow-up period. The coma recovery scale-revised (CRS-R) showed an improvement by a median of 2 points for the group receiving tilt table with integrated stepping (Erigo). The rate of recovery of the group receiving the conventional tilt table therapy significantly increased by 5 points during treatment and by an additional 2 points during the 3-week follow-up period. Changes in spasticity did not significantly differ between the two intervention groups. Compared to the conventional tilt table, the tilt table with integrated stepping device failed to have any additional benefit for DOC patients. Verticalization itself seems to be beneficial though and should be administered to patients in DOC in early rehabilitation. Trial Registration: Current Controlled Trials Ltd (www.controlled-trials.com), identifier number ISRCTN72853718     

Classification of Motor Imagery EEG Based on Time-Domain and Frequency-Domain Dual-Stream Convolutional Neural Network

Background and objectiveAn important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot.MethodsIn order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically.ResultsThe experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs.ConclusionsFurther analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.     

Bedside Evaluation of the Functional Organization of the Auditory Cortex in Patients with Disorders of Consciousness

To measure the level of residual cognitive function in patients with disorders of consciousness, the use of electrophysiological and neuroimaging protocols of increasing complexity is recommended. This work presents an EEG-based method capable of assessing at an individual level the integrity of the auditory cortex at the bedside of patients and can be seen as the first cortical stage of this hierarchical approach. The method is based on two features: first, the possibility of automatically detecting the presence of a N100 wave and second, in showing evidence of frequency processing in the auditory cortex with a machine learning based classification of the EEG signals associated with different frequencies and auditory stimulation modalities. In the control group of twelve healthy volunteers, cortical frequency processing was clearly demonstrated. EEG recordings from two patients with disorders of consciousness showed evidence of partially preserved cortical processing in the first patient and none in the second patient. From these results, it appears that the classification method presented here reliably detects signal differences in the encoding of frequencies and is a useful tool in the evaluation of the integrity of the auditory cortex. Even though the classification method presented in this work was designed for patients with disorders of consciousness, it can also be applied to other pathological populations.     

Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.     

Online Classification of Multiple Motor Imagery Tasks Using Filter Bank Based Maximum-a-Posteriori Common Spatial Pattern Filters

ObjectiveThe main objective of this paper is to propose a novel technique, called filter bank maximum a-posteriori common spatial pattern (FB-MAP-CSP) algorithm, for online classification of multiple motor imagery activities using electroencephalography (EEG) signals. The proposed technique addresses the overfitting issue of CSP in addition to utilizing the spectral information of EEG signals inside the framework of filter banks while extending it to more than two conditions.Materials and methodsThe classification of motor imagery signals is based upon the detection of event-related de-synchronization (ERD) phenomena in the μ and β rhythms of EEG signals. Accordingly, two modifications in the existing MAP-CSP technique are presented: (i) The (pre-processed) EEG signals are spectrally filtered by a bank of filters lying in the μ and β brainwave frequency range, (ii) the framework of MAP-CSP is extended to deal with multiple (more than two) motor imagery tasks classification and the spatial filters thus obtained are calculated for each sub-band, separately. Subsequently, the most imperative features over all sub-bands are selected and un-regularized linear discriminant analysis is employed for classification of multiple motor imagery tasks.ResultsPublicly available dataset (BCI Competition IV Dataset I) is used to validate the proposed method i.e. FB-MAP-CSP. The results show that the proposed method yields superior classification results, in addition to be computationally more efficient in the case of online implementation, as compared to the conventional CSP based techniques and its variants for multiclass motor imagery classification.ConclusionThe proposed FB-MAP-CSP algorithm is found to be a potential / superior method for classifying multi-condition motor imagery EEG signals in comparison to FBCSP based techniques.     

Different Methods for Assessing the Features of Eating Disorders in Patients with Binge Eating Disorder: A Replication

Objective: To compare different methods for assessing the features of eating disorders in patients with binge eating disorder (BED). Research Methods and Procedures: A total of 47 participants with BED were administered the Eating Disorder Examination (EDE) Interview and completed the EDE‐Questionnaire (EDE‐Q) at baseline. A total of 37 participants prospectively self‐monitored their eating behaviors daily for 4 weeks and then completed another EDE‐Q. Results: At baseline, the EDE and the EDE‐Q were significantly correlated on frequencies of objective bulimic episodes (binge eating), overeating episodes, and on the dietary restraint, eating concern, weight concern, and shape concern subscales. Mean differences in the EDE and EDE‐Q frequencies of objective bulimic episodes and overeating were not significant but scores on the four subscales differed significantly, with the EDE‐Q yielding higher scores. At the 4‐week point, the EDE‐Q retrospective 28‐day assessment was significantly correlated with the prospective daily self‐monitoring records for frequency of objective bulimic episodes and the mean difference between the methods was not significant. The EDE‐Q and self‐monitoring findings for subjective bulimic episodes and objective overeating differed significantly. Discussion: In patients with BED, the three assessment methods showed some areas of acceptable convergence.     

基于听觉刺激的意识障碍患者脑功能连通性研究

目的 目前对意识障碍（DOC）患者的分级评估仍是相关领域的重点和难点。因效性网络可以通过时间序列间的因果关系直观地反映信息传递方向,帮助人们更好地理解患者大脑不同区域之间的信息交互作用。本文结合脑电图和因效性网络探讨听觉刺激下无反应觉醒综合征（VS）患者与最低意识状态（MCS）患者的脑功能连通性差异。方法 共纳入23例DOC患者,采集并分析唤名刺激下的脑电信号,通过多元格兰杰因果方法构建脑功能网络,利用脑网络节点度、聚类系数、全局效率以及因果流向性等参量从脑区之间协同工作的角度对比研究听觉刺激下不同意识水平患者的网络特征。结果 唤名刺激下MCS患者的脑功能连通性强于VS患者,且呈现出因果流向差异,MCS与VS患者四个脑区的信息传递方向均不相同。结论 唤名听觉刺激下MCS患者的信息传递能力强于VS患者;与VS患者相比MCS患者为因果源的电极通道数增多,对其他脑区的信息输出增多。本研究可为DOC患者意识水平的分级评估提供一定的理论依据。     

Nonlinear Analysis of Quantitative EEGs in Patients with Syndromes of Post-Coma Disorders of Consciousness after Severe Traumatic Brain Injury

Neurophysiology - Electroencephalograms of 220 patients with post-coma disorders of consciousness after severe traumatic brain injury were analyzed using nonlinear multidimensional analysis, and...     

A Comparison of Nonparametric Error Rate Estimation Methods in Classification Problems

Assessment of the misclassification error rate is of high practical relevance in many biomedical applications. As it is a complex problem, theoretical results on estimator performance are few. The origin of most findings are Monte Carlo simulations, which take place in the “normal setting”: The covariables of two groups have a multivariate normal distribution; The groups differ in location, but have the same covariance matrix and the linear discriminant function LDF is used for prediction. We perform a new simulation to compare existing nonparametric estimators in a more complex situation. The underlying distribution is based on a logistic model with six binary as well as continuous covariables. To study estimator performance for varying true error rates, three prediction rules including nonparametric classification trees and parametric logistic regression and sample sizes ranging from 100‐1,000 are considered. In contrast to most published papers we turn our attention to estimator performance based on simple, even inappropriate prediction rules and relatively large training sets. For the major part, results are in agreement with usual findings. The most strikingly behavior was seen in applying (simple) classification trees for prediction: Since the apparent error rate Êrr.app is biased, linear combinations incorporating Êrr.app underestimate the true error rate even for large sample sizes. The .632+ estimator, which was designed to correct for the overoptimism of Efron's .632 estimator for nonparametric prediction rules, performs best of all such linear combinations. The bootstrap estimator Êrr.B0 and the crossvalidation estimator Êrr.cv, which do not depend on Êrr.app, seem to track the true error rate. Although the disadvantages of both estimators – pessimism of Êrr.B0 and high variability of Êrr.cv – shrink with increased sample sizes, they are still visible. We conclude that for the choice of a particular estimator the asymptotic behavior of the apparent error rate is important. For the assessment of estimator performance the variance of the true error rate is crucial, where in general the stability of prediction procedures is essential for the application of estimators based on resampling methods. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

3种梅毒诊断实验对梅毒患者的临床诊断效果对比

目的:对比TPPA(梅毒螺旋体明胶颗粒凝集实验)、RPR(快速血浆反应测定)和USR(不加热血清反应素实验)对不同临床分期梅毒患者的临床诊断效果。方法:回顾性分析疑似梅毒病患共113人,其中被确诊为梅毒的99人,其余14人正常。对113人均采用RPR和USR初筛实验和TPPA梅毒确诊实验。评价RPR、USR、TPPA三种诊断的敏感性、特异性、阳性预测值和阴性预测值。结果:RPR和USR对梅毒诊断的敏感性、特异性、阳性预测值和阴性预测值差异无统计学意义(P0.05)。TPPA各项值均高于RPR和USR,差异有统计学意义(P均0.05)。RPR、USR对Ⅰ、Ⅱ和Ⅲ期梅毒确诊率无统计学差异(P0.05),但TPPA对Ⅰ、Ⅱ期梅毒确诊率基本为100%,但是对Ⅲ期梅毒的确诊率降低,且差异有统计学意义(P0.05)。结论:USR和RPR对临床梅毒均存在一定的误诊率,为提高临床疑似病例确诊率,应联合梅毒检测,TPPA虽为确诊实验,但对Ⅲ期梅毒仍有一定的漏诊率。     

Effect of Motor Imagery in Children with Unilateral Cerebral Palsy: fMRI Study

Background

Motor imagery is considered as a promising therapeutic tool for rehabilitation of motor planning problems in patients with cerebral palsy. However motor planning problems may lead to poor motor imagery ability.

Aim

The aim of this functional magnetic resonance imaging study was to examine and compare brain activation following motor imagery tasks in patients with hemiplegic cerebral palsy with left or right early brain lesions. We tested also the influence of the side of imagined hand movement.

Method

Twenty patients with clinical hemiplegic cerebral palsy (sixteen males, mean age 12 years and 10 months, aged 6 years 10 months to 20 years 10 months) participated in this study. Using block design, brain activations following motor imagery of a simple opening-closing hand movement performed by either the paretic or nonparetic hand was examined.

Results

During motor imagery tasks, patients with early right brain damages activated bilateral fronto-parietal network that comprise most of the nodes of the network well described in healthy subjects. Inversely, in patients with left early brain lesion brain activation following motor imagery tasks was reduced, compared to patients with right brain lesions. We found also a weak influence of the side of imagined hand movement.

Conclusion

Decreased activations following motor imagery in patients with right unilateral cerebral palsy highlight the dominance of the left hemisphere during motor imagery tasks. This study gives neuronal substrate to propose motor imagery tasks in unilateral cerebral palsy rehabilitation at least for patients with right brain lesions.     

测定糖尿病患者血清LDL-C水平的方法学比较

目的：比较不同方法检测糖尿病患者血清LDL-C水平,为临床诊疗提供准确可行的检验方法。方法：采用沉淀法、匀相法、电泳法及超速离心法对233例糖尿病患者和102例健康人群的血清LDL-C水平进行测定,比较各方法之间的相关性,同时分析导致结果差异的因素。结果：四种方法检测健康人群LDL-C水平,结果间无统计学差异（P〉0.05）;糖尿病组,当TG≤2.26mmol/L时,四种方法检测LDL-C结果间相关性良好。高胆红素、血红蛋白、高TG及乳糜等干扰因素存在时,与其他方法相比,电泳法和超速离心法检测血清LDL-C结果受影响较小（P〉0.05）。结论：超速离心法虽耗时、价格贵,但仍为检测LDL-C的经典方法,电泳法受高胆红素、血红蛋白、高三酰甘油等干扰因素的影响相对较小,适用于糖尿病合并高血脂患者血清LDL-C水平检测。     

测定糖尿病患者血清LDL-C 水平的方法学比较

目的:比较不同方法检测糖尿病患者血清LDL-C水平,为临床诊疗提供准确可行的检验方法。方法:采用沉淀法、匀相法、电泳法及超速离心法对233例糖尿病患者和102例健康人群的血清LDL-C水平进行测定,比较各方法之间的相关性,同时分析导致结果差异的因素。结果:四种方法检测健康人群LDL-C水平,结果间无统计学差异(P>0.05);糖尿病组,当TG≤2.26mmol/L时,四种方法检测LDL-C结果间相关性良好。高胆红素、血红蛋白、高TG及乳糜等干扰因素存在时,与其他方法相比,电泳法和超速离心法检测血清LDL-C结果受影响较小(P>0.05)。结论:超速离心法虽耗时、价格贵,但仍为检测LDL-C的经典方法,电泳法受高胆红素、血红蛋白、高三酰甘油等干扰因素的影响相对较小,适用于糖尿病合并高血脂患者血清LDL-C水平检测。