脑机接口技术的专利分析

<正>脑机接口是一种涉及多学科多领域的新颖人机交互技术，本文基于Incopat专利库，对脑机接口技术的专利进行申请趋势、技术生命周期、申请人排名、IPC申请趋势、申请人国别等进行分析及总结，可以为脑机接口领域的技术发展动态提供一定的情报参考。脑机接口(BCI)技术是一种全新颠覆性的人机交互技术^[1-3]。BCI由美国加州大学Jacques Vidal教授于1973年提出。1999年，第1届国际BCI大会正式给出BCI定义:通过分析人或动物的脑电信号而建立起来的交互系统^[4,6]。     

利用C API实现基于RTX实时仿真系统的在线调参

介绍了基于Matlab/RTW(Real-time Workshop)和RTX(Real-time extension)构建实时仿真系统的方法;针对基于RTX的实时仿真系统不能直接进行在线调参的不足,提出了一种利用C API(C文件应用程序接口)实现在线调参的方法。经过实验证明,此仿真系统不仅具有很强的实时性,并且拥有良好的人机交互能力;另外,在线调参功能的实现使仿真试验的效率得到了大大的提高,而且还可以作为一种故障注入方法来考察模型的容错能力,是基于RTX实时仿真系统的一大改良。     

电视与广播

TN93 99061200基于P DR300的多通道硬盘播出系统/陶黎生,胡强,韩炜(南京有线广播电视台)11广播与电视技术.一1999,26(6)一66一72文中讨论了一种采用P DR300的多通道硬盘播出系统,它能够存储广播质量的M PEG一2图像数据和CD质量的音频数据。这一系统提供了3个输入通道和10个输出通道,分别用于录入、编辑、回放和延时播出.通过光纤通道,可以实现系统内部视音频文件的共享.图4(许)播发射系统微机实时监控系统的基本方案和系统结构,并利用分布式网络结构和先进的计算机控制技术完成了整个监控系统的硬件和软件设计,实现了发射台“有人留守…     

脑机接口技术研究概述

脑机接口(Brain-Computer Interface, BCI)是在人脑和外界之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的一种通讯系统.本文概述了基于脑电信号(EEG)的BCI技术的基本原理、研究方法、类型、研究现状,并分析了目前存在的问题与应用前景.     

脑机接口技术研究综述

脑机接口技术(brain computer interface,BCI)不依赖于常规大脑信息输出通路,该技术建立了一种直接的信息交流和控制通道,为人脑和外界之间提供了一种全新的交互方式。简要介绍了BCI技术的定义和基本组成及发展现状,并对皮层慢电位、视觉诱发电位、眼动产生的α波、P300电位和基于运动想象的μ节律及β波5种脑机接口技术的研究方向作了简要阐述,最后指出目前BCI研究面临的挑战及未来的应用前景。     

一种基于运动图像的在线手写汉字识别方法

在分析传统联机与脱机手写汉字识别系统基础上,本文提出了一种基于运动图像的在线手写汉字识别新方法.该方法利用普通摄像头,实时地采集手写汉字墨迹的运动图像,通过图像处理技术提取手写汉字,并进行在线识别.文中给出了笔尖匹配的快速算法和基于数学形态学的汉字提取方法.最后,通过实验验证了本文方法的有效性.     

用于单片机或嵌入式系统汉字输入法设计

随着单片机和嵌入式系统的广泛应用,单片机的汉字输入和显示越来越重要.为此,介绍一种单片机显控系统的输入方法设计与实现.以Philips公司生产的P89V51RD2单片机为核心,采用奥可拉中文集成模块(OCMJ)B系列液晶显示器,并利用通用小键盘(4x4按键)快速实现字母、数字、汉字的检索录入.     

基于Labview的点阵显示控制图形的设计

基于利用点阵的排列可以显示出完美、多样的图形,对点阵显示控制提出了一种基于Labview的汉字解码显示控制方案.并完成系统的软件设计和仿真.该系统的软件部分主要由前面板和后面板的程序框图构成,前面板主要用来输入文本和按要求控制显示输入文本;后面板主要依据点阵显示控制原理进行Labview编程.本次设计能够完成各种数字、文字和图形的绘制.实际应用表明,该系统具有控制精度高、调试简便、实用性强等特点.     

触摸屏技术相关申请的专利分析

触摸屏技术是一种新型的人机交互输入方式,是目前研发的重点.在大量专利申请的基础上,对触摸屏技术进行详细地分析研究,基于专利视角研究了此技术的专利发展趋势并重点分析了三星公司在触摸屏技术领域的核心专利申请,希望借此能够提高触摸屏技术领域的专利申请的技术水平.     

基于新型P2P与CDN融合技术的流媒体系统设计

提出一种在对等互联网络技术(P2P)流媒体系统中引入内容分发网络(CDN)的管理机制,整合并利用用户剩余带宽资源的设计.从P2P流媒体系统中选取高性能、高带宽、在线时间稳定的节点作为CDN边缘服务器,在智能化的调度下为非P2P用户包括手机用户提供C/S模式流媒体服务.使部分用户可以直接通过标准控件接入.通过详细的性能分析阐明了该系统的优势,并具体讨论了该设计在实现过程中关于控件选择、节点选取、负载平衡算法等细节问题.通过实际测试与调查证明,该设计在未来流媒体业务,特别是手机流媒体业务中有较好的应用前景.     

xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin. An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier, show that the proposed method is efficient and accurate.     

BCI Competition 2003--Data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications

An algorithm based on independent component analysis (ICA) is introduced for P300 detection. After ICA decomposition, P300-related independent components are selected according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.     

基于相关性和稀疏表示的运动想象脑电通道选择方法

在基于运动想象(MI)的脑机接口(BCI)中,通常采用较多通道的脑电信号(EEG)来提高分类精度,但其中会有包含与MI任务无关或冗余信息的通道,从而影响BCI的性能提升。该文针对运动想象脑电分类中的通道选择问题,提出一种采用相关性和稀疏表示对通道进行选择的方法(CSR-CS)。首先计算训练样本每个通道的皮尔逊相关系数来选择显著通道,然后提取显著通道所在区域的滤波器组共空间模式特征拼接成字典,利用由字典所得到的非零稀疏系数的个数表征每个区域的分类能力,选出显著区域所包含的显著通道作为最优通道,最后采用共空间模式和支持向量机分别进行特征提取与分类。在对BCI第3次竞赛数据集IVa和BCI第4次竞赛数据集I两个二分类MI任务的分类实验中,平均分类精度达到了88.61%和83.9%,表明所提通道选择方法的有效性和鲁棒性。     

BCI competition III: dataset II- ensemble of SVMs for BCI P300 speller

Brain-computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This paper addresses the problem of signal responses variability within a single subject in such brain-computer interface. We propose a method that copes with such variabilities through an ensemble of classifiers approach. Each classifier is composed of a linear support vector machine trained on a small part of the available data and for which a channel selection procedure has been performed. Performances of our algorithm have been evaluated on dataset II of the BCI Competition III and has yielded the best performance of the competition.     

BCI Competition 2003--Data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram

The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain-computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.     

An asynchronous P300 BCI with SSVEP-based control state detection

In this paper, an asynchronous brain-computer interface (BCI) system combining the P300 and steady-state visually evoked potentials (SSVEPs) paradigms is proposed. The information transfer is accomplished using P300 event-related potential paradigm and the control state (CS) detection is achieved using SSVEP, overlaid on the P300 base system. Offline and online experiments have been performed with ten subjects to validate the proposed system. It is shown to achieve fast and accurate CS detection without significantly compromising the performance. In online experiments, the system is found to be capable of achieving an average data transfer rate of 19.05 bits/min, with CS detection accuracy of about 88%.     

BCI Competition 2003--Data set IIa: spatial patterns of self-controlled brain rhythm modulations

A brain-computer interface (BCI) is a system that should in its ultimate form translate a subject's intent into a technical control signal without resorting to the classical neuromuscular communication channels. By using that signal to, e.g., control a wheelchair or a neuroprosthesis, a BCI could become a valuable tool for paralyzed patients. One approach to implement a BCI is to let users learn to self-control the amplitude of some of their brain rhythms as extracted from multichannel electroencephalogram. We present a method that estimates subject-specific spatial filters which allow for a robust extraction of the rhythm modulations. The effectiveness of the method was proved by achieving the minimum prediction error on data set IIa in the BCI Competition 2003, which consisted of data from three subjects recorded in ten sessions.     

Asynchronous P300-based brain-computer interfaces: a computational approach with statistical models

Asynchronous control is an important issue for brain-computer interfaces (BCIs) working in real-life settings, where the machine should determine from brain signals not only the desired command but also when the user wants to input it. In this paper, we propose a novel computational approach for robust asynchronous control using electroencephalogram (EEG) and a P300-based oddball paradigm. In this approach, we first address the mathematical modeling of target P300, nontarget P300, and noncontrol signals, by using Gaussian distribution models in a support vector margin space. Furthermore, we derive a method to compute the likelihood of control state in a time window of EEG. Finally, we devise a recursive algorithm to detect control states in ongoing EEG for online application. We conducted experiments with four subjects to study both the asynchronous BCI's receiver operating characteristics and its performance in actual online tests. The results show that the BCI is able to achieve an averaged information transfer rate of approximately 20 b/min at a low false positive rate (one event per minute).     

Energy constrained frequency-domain normalized LMS algorithm for blind channel identification

This paper deals with the blind adaptive identification of single-input multi-output (SIMO) finite impulse response acoustic channels from noise-corrupted observations. The normalized multichannel frequency-domain least-mean-squares (NMCFLMS) algorithm [1] is known to be a very effective and efficient technique for identification of such channels when noise effects can be ignored. It, however, misconverges in presence of noise [2]. In this paper, we present an analysis of noise effects on the NMCFLMS algorithm and propose a novel technique for ameliorating such misconvergence characteristics of the NMCFLMS algorithm for blind channel identification (BCI) with noise by attaching a spectral constraint in the adaptation rule. Experimental results demonstrate that the robustness of the NMCFLMS algorithm for BCI can be significantly improved using such a constraint.     

国际脑计算机接口竞赛介绍及参赛经验谈

脑计算机接口(BCI)技术的研究是近几年才发展起来的一个具有学科交叉特点的前沿探索领域,其目的是实现脑与计算机设备的直接通讯;国际脑计算机接口竞赛由BCI研究领域的主要学术团体联合发起,旨在征集和检验新的BCI实现思路及相关数据处理算法,吸引和鼓励不同领域的研究者参与BCI研究.本文简要阐述了脑计算机接口的概念和发展情况,分析了历届BCI竞赛的设置和开展情况,最后详细介绍了我们的参赛经验和一些体会,供感兴趣的指导教师和研究生参考.