Listening to the Brain in Real Time-- The Chengdu Brainwave Music

Electroencephalogram （EEG） provides a window for the activity of the human brain. As a novel form of the brain-computer interface （BCI）, the online/offline EEG data may be interpreted through its auditory representation which can be considered as a specific tool in EEG monitoring and analysis. In this work, after a comprehensive comparison of the various designs of brainwave music generations, a waveform event mapping system for music display in real time-- the Chengdu Brainwave Music （CBM） is proposed, which is a special on-line BCI system. In CBM, the user datagram protocol （UDP） is adopted to transport EEG data from the recorder to a music generator. The CBM could possibly be used as an audio feedback tool in BCI, or a monitoring tool in clinic EEG, and a subject specified music therapy method.     

A Study on SSVEP-Based BCI

Brain-computer interface (BCI) can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extracting in a short time, stimulating frequency selection, and signal electrode selection. The conclusion is that the stimulator type should be based on the complexity of the BCI system, the method based on wavelet analysis is more valid than the power spectrum method in extracting the SSVEP in a short period, and the selections of stimulating frequency and electrode are important in designing a BCI system. These contents are meaningful for implementing a real SSVEP-based BCI.     

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.     

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiological index for brain-computer interface (BCI), electroencephalogram (EEG) attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.     

Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.     

Semi-Supervised Learning Based on Manifold in BCI

Abstract-A Laplacian support vector machine （LapSVM） algorithm, a semi-supervised learning based on manifold, is introduced to brain-computer interface （BCI） to raise the classification precision and reduce the subjects＇ training complexity. The data are collected from three subjects in a three-task mental imagery experiment. LapSVM and transductive SVM （TSVM） are trained with a few labeled samples and a large number of unlabeled samples. The results confirm that LapSVM has a much better classification than TSVM.     

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.     

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Common spatial pattern (CSP) algorithm is a successful tool in feature estimate of brain-computer interface (BCI). However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble (CSPE) classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.     

用于音乐的MPEG7和符合MPEG7的音乐

ＭＰＥＧ－７,即多媒体内容描述接口（Ｍｕｌｔｉｍｅｄｉａ Ｃｏｎｔｅｎｔ Ｄｅｓｃｒｉｐｔｉｏｎ Ｉｎｔｅｒｆａｃｅ）致力于对视听材料描述进行标准化,以实现通用的索引、检索、过滤、控制以及由量的元数据所支持的其他操作。文章给出了ＭＰＥＧ－７的概念的一个综述,并提供了一怎样将目前的实际结合进ＭＰＥＧ－７框架中的例子,阐述了对标准的可能发展方向和解决方案的一些想法。     

Brain–computer interface connected to telemedicine and telecommunication in virtual reality applications

A novel brain–computer interface work is proposed for the applications of virtual reality in telemedicine and telecommunication in this study, in which the aim is to enhance the interactions between the humans and computers with virtual reality technologies. The system mainly contains automatic artifacts removal, feature extraction, significant feature selection and classification. A similarity measure approach is proposed to automatically remove the artifacts, which effectively reduce the influence of artifacts and simultaneously achieve higher accuracy. The artificial bee colony algorithm is used to select significant sub-features from feature combinations, which further greatly enhance the classification accuracy. Experimental results indicate that the proposed system performs better than several state-of-the-art approaches. It is also recommended that it is suitable for the applications of virtual reality in telemedicine and telecommunication.     

Increased Arousal Levels and Decreased Sleep by Brain Music in Rats

More and more studies have been reported on whether music and other types of auditory stimulation would improve the quality of sleep. Many of these studies have found significant results, but others argue that music is not significantly better than the tones or control conditions in improving sleep. For further understanding the relationship between music and sleep or music and arousal, the present study therefore examines the effects of brain music on sleep and arousal by means of biofeedback. The music is from the transformation of rapid eye movement （REM） sleep electroencephalogram （EEG） of rats using an algorithm in the Chengdu Brain Music （CBM） system. When the brain music was played back to rats, EEG data were recorded to assess the efficacy of music to induce or improve sleep, or increase arousal levels by sleep staging, etc. Our results demonstrate that exposure to the brain music increases arousal levels and decreases sleep in rats, and the underlying mechanism of decreased non-rapid eye movement （NREM） and REM sleep may be different.     

An Algorithm for Idle-State Detection and Continuous Classifier Design in Motor-Imagery-Based BCI

Abstract-The development of asynchronous brain-computer interface （BCI） based on motor imagery （M1） poses the research in algorithms for detecting the nontask states （i.e., idle state） and the design of continuous classifiers that classify continuously incoming electroencephalogram （EEG） samples. An algorithm is proposed in this paper which integrates two two-class classifiers to detect idle state and utilizes a sliding window to achieve continuous outputs. The common spatial pattern （CSP） algorithm is used to extract features of EEG signals and the linear support vector machine （SVM） is utilized to serve as classifier. The algorithm is applied on dataset IVb of BCI competition Ⅲ, with a resulting mean square error of 0.66. The result indicates that the proposed algorithm is feasible in the first step of the development of asynchronous systems.     

Increased Arousal Levels and Decreased Sleep by Brain Music in Rats

More and more studies have been reported on whether music and other types of auditory stimulation would improve the quality of sleep. Many of these studies have found significant results, but others argue that music is not significantly better than the tones or control conditions in improving sleep. For further understanding the relationship between music and sleep or music and arousal, the present study therefore examines the effects of brain music on sleep and arousal by means of biofeedback. The music is from the transformation of rapid eye movement (REM) sleep electroencephalogram (EEG) of rats using an algorithm in the Chengdu Brain Music (CBM) system. When the brain music was played back to rats, EEG data were recorded to assess the efficacy of music to induce or improve sleep, or increase arousal levels by sleep staging, etc. Our results demonstrate that exposure to the brain music increases arousal levels and decreases sleep in rats, and the underlying mechanism of decreased non-rapid eye movement (NREM) and REM sleep may be different.     

An Algorithm for Idle-State Detection and Continuous Classifier Design in Motor-Imagery-Based BCI

The development of asynchronous braincomputer interface (BCI) based on motor imagery (MI) poses the research in algorithms for detecting the nontask states (i.e., idle state) and the design of continuous classifiers that classify continuously incoming electroencephalogram (EEG) samples. An algorithm is proposed in this paper which integrates two two-class classifiers to detect idle state and utilizes a sliding window to achieve continuous outputs. The common spatial pattern (CSP) algorithm is used to extract features of EEG signals and the linear support vector machine (SVM) is utilized to serve as classifier. The algorithm is applied on dataset IVb of BCI competition III, with a resulting mean square error of 0.66. The result indicates that the proposed algorithm is feasible in the first step of the development of asynchronous systems.     

构建农村信息服务体系加快成都村村通建设

1 农村通信现状及原因 目前我国通信事业高速发展，但是许多农村，特别是偏远山区、丘陵地区，与城市在通信基础设施建设、通信质量、通信服务上存在很大差距。当室内覆盖、IP电话和宽带上网等通信手段已经成为城市居民日常生活一部分的时候，“装电话难”、“信息不畅”等问题仍困扰着我市部分山区、丘陵地区。     

电影中的高科技通信变成现实

“世界是平的”这句话在今天的沟通世界之中正在成为切近的现实。先进的思科统一通信(Unified Communications)解决方案正在将电影中的高科技通信变成现实的沟通体验,而体验创新的沟通模式同样为商业带来便利和快捷。近日,思科统一通信解决方案从《谍中谍3》和《反恐24小时》等     

利用自动测试系统(ATS)提高产品的批生产性

本文结合产品的生产过程,详细介绍了利用计算机、GPIB接口等组成的自动测试系统来提高产品批生产性的实现方法.     

通信瓶颈下的实时并行任务分配研究

提出了实时并行任务粒度划分的具体设计方法，采用了系统内通信迟延最小的约束条件进行多处理器上的任务分配，解决了高速实时信号处理中的数据通信瓶颈问题。在自行设计的超立方体结构的通用信号处理机上，实现了空时二维自适应信号处理和SAR实时成像，显著减小了数据通信迟延，提高了并行效率和性价比。     

Brain–computer interface: The next frontier of telemedicine in human–computer interaction

The study proposes a novel brain–computer interface scheme for the next frontier of telemedicine in human–computer interaction, where the goal is to improve the interactions between users and computers in telemedicine. The system consists of discriminative area selection, feature extraction and classification. Discriminative area selection is proposed to obtain the optimal discriminative area, which can decrease the time length of event-related area to achieve more efficient computation and higher accuracy. A fuzzy Hopfield neural network is used to classify the features extracted by means of wavelet-fractal approach. Experimental results show that the proposed system is robust and performs better than several previous methods. It is also suggested being suitable for the applications of telemedicine in human–computer interaction.     

制造报文规范中的虚拟制造机及实设备入网技术

本文对工厂网络自动化协议（ＭＡＰ）及其典型的通信协议制造报文规范（ＭＭＳ）作了简单介绍，着重介绍了ＭＭＳ中最重要的客体－虚拟制造机（ＶＭＤ），并对机器系统的入网技术作了研究，最后提出了实设备入网的策略。