Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 Speller.

We describe current efforts to implement and improve P300-BCI communication tools. The P300 Speller first described by Farwell and Donchin (in 1988) adapted the so-called oddball paradigm (OP) as the operating principle of the brain-computer interface (BCI) and was the first P300-BCI. The system operated by briefly intensifying each row and column of a matrix and the attended row and column elicited a P300 response. This paradigm has been the benchmark in P300-BCI systems, and in the past few years the P300 Speller paradigm has been solidified as a promising communication tool. While promising, we have found that some people who have amyotrophic lateral sclerosis (ALS) would be better suited with a system that has a limited number of choices, particularly if the 6 x 6 matrix is difficult to use. Therefore, we used the OP to implement a four-choice system using the commands: Yes, No, Pass, and End; we also used three presentation modes: auditory, visual, and auditory and visual. We summarize results from both paradigms and also discuss obstacles we have identified while working with the ALS population outside of the laboratory environment.     

ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system

A brain-computer interface (BCI) system may allow a user to communicate by selecting one of many options. These options may be presented in a matrix. Larger matrices allow a larger vocabulary, but require more time for each selection. In this study, subjects were asked to perform a target detection task using matrices appropriate for a BCI. The study sought to explore the relationship between matrix size and EEG measures, target detection accuracy, and user preferences. Results indicated that larger matrices evoked a larger P300 amplitude, and that matrix size did not significantly affect performance or preferences.     

Novel protocols for P300-based brain-computer interfaces

The oddball protocol is often used in brain-computer interfaces (BCIs) to induce P300 ERPs, although, recently, some issues have been shown to detrimentally effect its performance. In this paper, we study a new periodic protocol and explore whether it can compete with the standard oddball protocol within the context of a BCI mouse. We found that the new protocol consistently and significantly outperforms the standard oddball protocol in relation to information transfer rates (33 bits/min for the former and 22 bits/min for the latter, measured at 90% accuracy) as well as P300 amplitudes. Furthermore, we performed a comparison of two periodic protocols with two less conventional oddball-like protocols that reveals the importance of the interactions between task and sequence in determining the success of a protocol.     

An improved P300-based brain-computer interface.

A brain-computer interface (BCI) is a system for direct communication between brain and computer. The BCI developed in this work is based on a BCI described by Farwell and Donchin in 1988, which allows a subject to communicate one of 36 symbols presented on a 6 x 6 matrix. The system exploits the P300 component of event-related brain potentials (ERP) as a medium for communication. The processing methods distinguish this work from Donchin's work. In this work, independent component analysis (ICA) was used to separate the P300 source from the background noise. A matched filter was used together with averaging and threshold techniques for detecting the existence of P300s. The processing method was evaluated offline on data recorded from six healthy subjects. The method achieved a communication rate of 5.45 symbols/min with an accuracy of 92.1% compared to 4.8 symbols/min with an accuracy of 90% in Donchin's work. The online interface was tested with the same six subjects. The average communication rate achieved was 4.5 symbols/min with an accuracy of 79.5 % as apposed to the 4.8 symbols/min with an accuracy of 56 % in Donchin's work. The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.     

An adaptive P300-based online brain-computer interface.

The P300 component of an event related potential is widely used in conjunction with brain-computer interfaces (BCIs) to translate the subjects intent by mere thoughts into commands to control artificial devices. A well known application is the spelling of words while selection of the letters is carried out by focusing attention to the target letter. In this paper, we present a P300-based online BCI which reaches very competitive performance in terms of information transfer rates. In addition, we propose an online method that optimizes information transfer rates and/or accuracies. This is achieved by an algorithm which dynamically limits the number of subtrial presentations, according to the subject's current online performance in real-time. We present results of two studies based on 19 different healthy subjects in total who participated in our experiments (seven subjects in the first and 12 subjects in the second one). In the first, study peak information transfer rates up to 92 bits/min with an accuracy of 100% were achieved by one subject with a mean of 32 bits/min at about 80% accuracy. The second experiment employed a dynamic classifier which enables the user to optimize bitrates and/or accuracies by limiting the number of subtrial presentations according to the current online performance of the subject. At the fastest setting, mean information transfer rates could be improved to 50.61 bits/min (i.e., 13.13 symbols/min). The most accurate results with 87.5% accuracy showed a transfer rate of 29.35 bits/min.     

An Adaptive P300-Based Online Brain–Computer Interface

The P300 component of an event related potential is widely used in conjunction with brain-computer interfaces (BCIs) to translate the subjects intent by mere thoughts into commands to control artificial devices. A well known application is the spelling of words while selection of the letters is carried out by focusing attention to the target letter. In this paper, we present a P300-based online BCI which reaches very competitive performance in terms of information transfer rates. In addition, we propose an online method that optimizes information transfer rates and/or accuracies. This is achieved by an algorithm which dynamically limits the number of subtrial presentations, according to the subject's current online performance in real-time. We present results of two studies based on 19 different healthy subjects in total who participated in our experiments (seven subjects in the first and 12 subjects in the second one). In the first, study peak information transfer rates up to 92 bits/min with an accuracy of 100% were achieved by one subject with a mean of 32 bits/min at about 80% accuracy. The second experiment employed a dynamic classifier which enables the user to optimize bitrates and/or accuracies by limiting the number of subtrial presentations according to the current online performance of the subject. At the fastest setting, mean information transfer rates could be improved to 50.61 bits/min (i.e., 13.13 symbols/min). The most accurate results with 87.5% accuracy showed a transfer rate of 29.35 bits/min.     

The Wadsworth BCI Research and Development Program: at home with BCI.

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.     

基于 P300 与 ErrP 决策融合的脑-机接口目标检测方法

针对脑-机接口(BCI)技术在目标检测中的应用仍然存在检测准确率受限的问题,提出基于事件相关电位(ERP)中的 P300 与错误相关电位(ErrP)决策融合的新型编解码方法。 BCI 系统编码方面通过目标图像和视觉反馈分别诱发 P300 与 ErrP 特征,解码方面采用单独 P300 特征、单独 ErrP 特征、P300 与 ErrP 特征层融合、P300 与 ErrP 决策层融合这 4 种方案进行目标检 测。 10 名健康受试者 4 种方案进行目标检测的平均结果显示,使用 P300 与 ErrP 决策层融合的平衡正确率最高,达到 80. 03%± 5. 20%,相比单独使用 P300 特征的方法提升了 4. 38%,相比单独使用 ErrP 特征的方法提升了 11. 29%,验证了混合 BCI 技术在 目标检测任务中的可行性。     

P300-based BCI mouse with genetically-optimized analogue control.

In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2-D motion of the pointer on the screen, using a novel BCI paradigm, is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm.     

The BCI competition. III: Validating alternative approaches to actual BCI problems.

A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.     

Brain-computer interface technology: a review of the first international meeting.

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.     

A general framework for brain-computer interface design

The Brain-Computer Interface (BCI) research community has acknowledged that researchers are experiencing difficulties when they try to compare the BCI techniques described in the literature. In response to this situation, the community has stressed the need for objective methods to compare BCI technologies. Suggested improvements have included the development and use of benchmark applications and standard data sets. However, as a young, multidisciplinary research field, the BCI community lacks a common vocabulary. As a result, this deficiency leads to poor intergroup communication, which hinders the development of the desired methods of comparison. One of the principle reasons for the lack of common vocabulary is the absence of a common functional model of a BCI System. This paper proposes a new functional model for BCI System design. The model supports many features that facilitate the comparison of BCI technologies with other BCI and non-BCI user interface technologies. From this model, taxonomy for BCI System design is developed. Together the model and taxonomy are considered a general framework for BCI System design. The representational power of the proposed framework was evaluated by applying it to a set of existing BCI technologies. The framework could effectively describe all of the BCI System designs tested.     

A modified oddball paradigm for investigation of neural correlates of attention: a simultaneous ERP–fMRI study

Introduction

The objective of the presented study was to develop and evaluate a P300 experimental protocol for simultaneous registration of event-related potentials (ERPs) and functional MRI (fMRI) data with continuous imaging. It may be useful for investigating attention and working memory processes in specific populations, such as children and neuropsychiatric patients.

Materials and methods

Eleven children were investigated with simultaneous ERP–fMRI. To fulfill requirements of both BOLD and electroencephalographic signal registration, a modified oddball task was used. To verify the ERP–fMRI protocol we also performed a study outside the scanner using a typical two-stimuli oddball paradigm.

Results

Localization of the P300 component of ERPs partially corresponded with fMRI results in the frontal and parietal brain regions. FMRI activations were found in: middle frontal gyrus, insula, SMA, parietal lobule, thalamus, and cerebellum. Our modified oddball task provided ERP–fMRI results with high level of significance (EEG SNR = 35, fMRI p < 0.05–Bonf.). ERPs obtained in the scanner were comparable with those registered outside the scanner, although some differences in the amplitude were noticed, mainly in the N100 component.

Conclusion

In our opinion the presented paradigm may be successfully applied for simultaneous ERP–fMRI registration of neural correlates of attention in vulnerable populations.     

Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI

System calibration and user training are essential for operating motor imagery based brain-computer interface (BCI) systems. These steps are often unintuitive and tedious for the user, and do not necessarily lead to a satisfactory level of control. We present an Adaptive BCI framework that provides feedback after only minutes of autocalibration in a two-class BCI setup. During operation, the system recurrently reselects only one out of six predefined logarithmic bandpower features (10-13 and 16-24 Hz from Laplacian derivations over C3, Cz, and C4), specifically, the feature that exhibits maximum discriminability. The system then retrains a linear discriminant analysis classifier on all available data and updates the online paradigm with the new model. Every retraining step is preceded by an online outlier rejection. Operating the system requires no engineering knowledge other than connecting the user and starting the system. In a supporting study, ten out of twelve novice users reached a criterion level of above 70% accuracy in one to three sessions (10-80 min online time) of training, with a median accuracy of 80.2 ± 11.3% in the last session. We consider the presented system a positive first step towards fully autocalibrating motor imagery BCIs.     

矩阵行最简式的唯一性

分析了具有等价行向量组的两矩阵之间行向量组、列向量组及矩阵所对应的齐线性方程组的解之间关系，通过对行最简式定义的分析，得出两个结论：１．行最简式相同是矩阵行向量组等价的充要条件，２．矩阵行最简式是唯一的。使得对向量组等价性的研究转化为只需对矩阵作行初等变换即可实现，得到了研究向量组等价性的一种简洁、有效的方法，同时使得行最简式在矩阵研究中变得更为重要。     

Towards faster Givens rotations based power system state estimator

Numerically stable and computationally efficient power system state estimation (PSSE) algorithms are designed using an orthogonalization (QR decomposition) approach. They use Givens rotations for orthogonalization which enables sparsity exploitation during factorization of the large sparse augmented Jacobian. A priori row and column ordering is usually performed to reduce intermediate and and overall fills. Column ordering methods, usually based on minimum degree algorithm (MDA), have matured. However, there exists a significant scope for improving the quality of row ordering. This paper introduces a new row ordering technique for Givens rotations based power system state estimators. The proposed row processing method (VPAIR) requires a shift from conventionally used row oriented QR decomposition implementation to a column oriented QR decomposition implementation. It is demonstrated that, the proposed column oriented QR decomposition algorithm which uses MDA for column ordering and VPAIR for row ordering can lead to a much faster PSSE. These aspects are justified by simulations on large power systems     

一种补齐式准原地转置算法

提出一种补齐式准原地转置算法,利用方阵对角线对称位置小块数据互换的思想实现大规模矩阵的原地转置,构建一种提升存储资源利用率的转置策略:以短边为基准将矩阵补齐以便划分成数个方阵,再对每个方阵划分小方阵。利用分块读写的思想每次转置一对小方阵,实现行写行读,既提高存储器读写效率,又可以提高存储空间利用率,实验结果表明,相比非原地转置算法,存储空间最大降低49.5%,且对行列相差悬殊的矩阵具有良好的转置效率。     

消去树理论及其在潮流计算中的应用

采用了基于消去树理论的符号因子分解技术以及改进的LU数值分解算法来提高牛顿法潮流计算的效率。介绍了消去树理论，并采用符号因子分解技术确定雅可比矩阵的结构，然后采用稀疏向量法求取L阵的每行和U阵的每列。这种算法和求取L阵每列和U阵每行的传统LU分解方法相比，具有编程简单、计算效率高的优点。另外，雅可比矩阵结构对称以及编译器优化的经验也应用到文中，使得算法不仅占用内存较少，且效率较高。算法的优越性在实际系统 中得到了验证。     

Parallel man-machine training in development of EEG-based cursor control.

A new parallel man-machine training approach to brain-computer interface (BCI) succeeded through a unique application of machine learning methods. The BCI system could train users to control an animated cursor on the computer screen by voluntary electroencephalogram (EEG) modulation. Our BCI system requires only two to four electrodes, and has a relatively short training time for both the user and the machine. Moving the cursor in one dimension, our subjects were able to hit 100% of randomly selected targets, while in two dimensions, accuracies of approximately 63% and 76% was achieved with our two subjects.     

集成学习在脑机接口分类算法中的研究

提出了一种基于独立分量分析的支持向量机集成学习算法,用于脑机接口中P300字符识别.首先由P300信号分解出独立分量,基于Bagging算法送入支持向量机基分类器进行集成学习,通过平均的方法获得对应类别概率进行分类决策.数据来源于P300字符拼写实验,不同导联和不同序列的分类结果表明,该分类算法学习效率和分类精度高,全...