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.     

基于P300脑机接口系统实验范式的研究现状

脑机接口系统是一种无需借助周围神经和肌肉便可与外界交流信息、表达使用者想法的通信系统。在过去20多年,P300电位作为脑机接口技术常用的典型脑电成分一直倍受关注,众多研究者为提高脑机接口系统的性能,相继提出了众多基于P300脑机接口系统的实验范式。在总结归纳的基础上,对使用广泛、性能较优越的3种实验范式进行了论述,并比较总结了其性能,同时分析了影响实验范式的物理属性。最终对脑机接口系统实验范式未来的研究方向进行了展望。     

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%.     

基于P300电位的新型BCI中文输入虚拟键盘系统

 近年来各种信号处理技术随着计算能力的提高取得了巨大进展,推动了人机交互(HCI)技术的发展.脑机接口(BCI)是一种特殊的人机交互通道,在最近几年引起广泛关注.P300电位是一种事件相关电位,利用诱发人类P300的原理,可以实现基于P300的BCI系统.此类系统以往常用于英文字母的输入,本研究首次设计并开发了一套进行汉字输入的在线P300-BCI系统.系统利用汉字基于笔画的特性简化了P300诱发界面,并据此设计了相应的汉字虚拟键盘.利用此系统进行的在线输入实验表明,此中文BCI的设计是可行的,对系统的进一步完善将可以为汉语系的瘫痪患者的机能恢复提供新的选项.     

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.     

DVL2021: An ultra high definition video dataset for perceptual quality study

This paper describes an ultra high definition (UHD) video dataset named DVL2021 for the perceptual study of video quality assessment (VQA). To our knowledge, DVL2021 is the first authentically distorted 4K (3840 × 2160) UHD video quality dataset. The dataset contains 206 versatile 4K UHD video sequences, which are all collected in in-the-wild scenarios. Each sequence is captured at 50 frames per second (fps), stored in raw 10-bit 4:2:0 YUV format, and has a duration of 10 s. Following the subjective evaluation method of TV image quality granted by ITU-R BT.500-13, 32 unique participants take part in the manual annotation process, whose ages are from teenage to sixties (32.7 years old on average). DVL2021 has the following merits: (1) enormous variety of video contents, (2) captured by different types of cameras, (3) complex types and multiple levels of authentic distortion, (4) broadly distributed temporal/spatial information, and (5) a wide spectrum of mean opinion scores (MOS) distribution. Furthermore, we conduct a benchmark experiment by evaluating several mainstream VQA methods on DVL2021. The baseline results are higher than 0.75 in Spearman’s rank order correlation coefficient (SROCC) metric. Our study provides a basis for the UHD VQA problem. DVL2021 is publicly available at https://github.com/GZHU-DVL/DVL2021.     

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.     

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     

P300 Speller中基于权值重采样的 ABSVM字符识别方法研究

 P300 Speller是脑-机接口中重要的信息交互方式,由于其诱发的脑电特征信噪比较低与训练样本量庞大等问题,常规的线性识别算法和支持向量机等非线性识别算法难以获得理想的识别效率.本文引入了一种基于权值样本重采样过程的Adaptive Boosting SVM(ABSVM)方法,在大样本集上利用AdaBoost重采样方法建立一系列小样本子集,在其上训练支持向量机并将其集成后进行识别.对6位受试者P300 Speller字符辨识实验的脑电特征识别结果发现,该方法能够显著提高字符识别效率,在合并使用5次重复刺激特征的情况下字符识别准确率达到97.5%.使用国际脑机接口竞赛数据库数据进一步验证,在合并使用5次重复刺激特征的情况下该方法识别正确率较竞赛报告的最优方法提高7.35%,最大信息传输速率的提高达到48.9%.研究结果表明,ABSVM方法能够有效提高P300 Speller的识别效率和信息传输速率,值得进一步研究和发展.     

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.     

Saliency4ASD: Challenge,dataset and tools for visual attention modeling for autism spectrum disorder

The recent studies showing that gaze features can be useful in the identification of Autism Spectrum Disorder (ASD), have opened a new domain where Visual Attention (VA) modeling could be of great help. In this sense, this paper presents a report of the Grand Challenge “Saliency4ASD: Visual attention modeling for Autism Spectrum Disorder”, organized at IEEE ICME’19, aiming at supporting the research on VA modeling towards this healthcare societal challenge. In particular, this paper describes the workflow, obtained results, and datasets and tools that were used within this activity, in order to help on the development and evaluation of two types of VA models: (1) to predict saliency maps that fit gaze behavior of people with ASD, and (2) to identify individuals with ASD from typical development.