BCI Competition 2003--Data set IIb: support vector machines for the P300 speller paradigm

We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.     

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.     

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 III: probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements

Brain-computer interfaces require effective online processing of electroencephalogram (EEG) measurements, e.g., as a part of feedback systems. We present an algorithm for single-trial online classification of imaginary left and right hand movements, based on time-frequency information derived from filtering EEG wideband raw data with causal Morlet wavelets, which are adapted to individual EEG spectra. Since imaginary hand movements lead to perturbations of the ongoing pericentral mu rhythm, we estimate probabilistic models for amplitude modulation in lower (10 Hz) and upper (20 Hz) frequency bands over the sensorimotor hand cortices both contra- and ipsilaterally to the imagined movements (i.e., at EEG channels C3 and C4). We use an integrative approach to accumulate over time evidence for the subject's unknown motor intention. Disclosure of test data labels after the competition showed this approach to succeed with an error rate as low as 10.7%.     

A Prior Model of Structural SVMs for Domain Adaptation

In this paper, we study the problem of domain adaptation for structural support vector machines (SVMs). We consider a number of domain adaptation approaches for structural SVMs and evaluate them on named entity recognition, part‐of‐speech tagging, and sentiment classification problems. Finally, we show that a prior model for structural SVMs outperforms other domain adaptation approaches in most cases. Moreover, the training time for this prior model is reduced compared to other domain adaptation methods with improvements in performance.     

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).     

基于SSC P300的电力线扩频载波通讯电路的设计

电力线载波通讯是一种低价方便、并可免除装设专用通信线路的通信技术 ,文中介绍了利用Chirps扫描频率进行载波的扩频通讯技术和CEBUS总线的有关协议 ,给出了由SSCP300芯片构成的电力线载波通讯电路在电表自动抄收系统中的应用设计实例     

An exploratory study of factors affecting single trial P300 detection

A threshold detector for single-trial P300 detection has been evaluated. The detector operates on the 0-4 Hz band, isolated from the raw electroencephalogram using low-pass filtering, wavelet transforms, or the piecewise prony method (PPM). A detection rate around 70% was found, irregardless of stimulus type, interstimulus interval (ISI), probability of occurrence (Pr) of the target stimuli, intrasession and intersession effects, or filtering method. This suggests that P300-based brain-machine interfaces can use an ISI as short as 1 s and a Pr of 45%, to increase throughput.     

企业竞争信息系统的建设

1 建设企业竞争信息系统的重要意义获取竞争信息(Competitive Information)就是对竞争环境、竞争对手的全面监测过程,通过合法手段收集和分析商业竞争中有关商业行为的优势、劣势和目的,以更好地满足决策者制定竞争战略和方案的需要。换句话说,取得的信息要经过筛选、提炼和分析,以据之采取行动。企业管理的中心就是决策,决策的成败关系到企业的存亡。在人类社会由工业化社会向信息化社会发展的过程中,不论是战略、战术决策,还是定性、定量决策,离开了准确情报将寸步难行。对于获取企业竞争信息活动而言,就是通过对企业自身、竞争对手及竞…     

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery (MI) brain-computer interface (BCI) system. In this paper, we propose a common spatial pattern (CSP) and chaotic particle swarm optimization (CPSO) twin support vector machine (TWSVM) scheme for classification of MI electroencephalography (EEG). The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine (SVM), particle swarm optimization (PSO) TWSVM, linear discriminant analysis LDA), back propagation (BP) and probabilistic neural network (PNN), respectively. Furthermore, it achieved a faster or comparable central processing unit (CPU) running time over the traditional SVM methods.     

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.     

Pulse templates for transmission over an ensemble of channels

A method is given for finding the envelope of a family of pulses whose shapes are determined by a finite number of parameters. In addition, it is assumed that the parameters are analytically constrained collectively. In the second part of the paper, a family of pulse shapes is determined that can be efficiently transmitted through a channel that is selected at random from an ensemble of channels. The pulse envelope theory of the first part of the paper is then applied using this family of pulses to determine the envelope of mean-square channel outputs of the family of pulses that provide relatively low loss through an ensemble of channels     

BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG

This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced 1s) of "BCI Competition 2003" with a classification accuracy of 84% on the test set.     

SSC P300芯片在低电网抄表系统中的应用

文章阐述了SSCP30 0型数据传输模块以线性扩频方式用于低压电网远程抄表系统的原理 ,说明了其有关硬件、软件结构 ,给出了实测性能指标     

Equivalent Circuit Model of a Bundle of Cables for Bulk Current Injection (BCI) Test

A "simulation program with integrated circuit emphasis" (SPICE) model for the evaluation, in frequency and time domain, of the coupling of a bundle of shielded coaxial cables with an external electromagnetic source such as a bulk current injection clamp, is developed. The proposed equivalent circuit takes into account the presence of the transfer impedance and the presence of the transfer admittance, and it does not need the subdivision in elementary cells. Intermediate results are validated by comparing the exact numerical solutions with the output of the SPICE circuit, and the final results are validated by means of comparison with measurements carried out on an experimental setup. The feature selective validation (FSV) technique is used as a measure of the comparisons     

Self-consistent theory of mode competition for gyrotrons

The equations for a fully self-consistent theory of mode competition in gyrotrons are derived and some possible methods of solution are discussed. The importance of including interaction in the resonator output taper is discussed in connection with the validity of linearizing the equations in the amplitude of the parasitic mode.     

果园喷药竞赛机器人设计

文章设计了一种面向中国机器人大赛的果园喷药机器人,根据竞赛要求,结合平面四杆机构的工作原理,设计了机器人的机械系统;以STM32F103ZET6单片机为控制核心,通过搭载电机驱动器、循迹传感器、平面定位系统以及喷药系统,设计了竞赛机器人的控制系统,其中,以步进电机控制器、丝杠滑台、电磁继电器、电磁阀、增压泵等构建了机器人喷药系统。最后,设计了控制算法,实现了果园喷药机器人的竞赛功能。     

Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials

We present a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. Our method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-session variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.