Real-Time Brain-Computer Interface System Based on Motor Imagery

A brain-computer interface (BCI) realtime system based on motor imagery translates the user's motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.     

Development of intracranial brain-computer interface system using non-motor brain area for series of motor functions

An intracranial brain-computer interface (BCI) system using the neuronal activity of a non-motor brain area to fulfil a series of motor functions has been developed. The presented BCI system encodes a series of motor functions into a small number of neuronal units of the primary somatosensory cortex of a rat and generates real-time command signals to control a machine according to the animal's motor intentions. The results of this study demonstrate the practical usability of the BCI system using a non-motor brain area in the field of rehabilitation.     

脑机接口中脑电图-近红外光谱联合分析进展研究

脑机接口(BCI)能将受试者意图相关的大脑活动转化为外部设备控制指令,在神经疾病治疗、运动康复等方面具有较高应用潜力。BCI的实现需从人脑获取有意义的信号,而脑电图(EEG)可以反映神经电活动,主要用于对反映实时性要求较高的BCI系统;近红外光谱(NIRS)主要反映血流动力学水平,一般用于神经生理状态等需要精确定位脑活跃区域的研究。EEG和NIRS因其非侵入、方便穿戴、成本较低等优点,成为BCI的重要信号获取方法。相比于单模态BCI系统,基于EEG-NIRS 联合分析的混合BCI系统由于具有更丰富的信号特征,在生理状态检测、运动想象等领域得到了越来越多的关注与研究。该文从EEG-NIRS联合分析在脑机接口中应用的研究现状出发,在数据和特征融合程度、层面上归纳最近的相关领域研究现状,并对EEG-NIRS信号处理手段的研究前景进行了展望。     

BCI2000: a general-purpose brain-computer interface (BCI) system

Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.     

A fully on-line adaptive BCI

A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.     

基于信号投影能量特征的脑电意识动态分类

针对脑电意识任务动态分类问题,本文提出了一种基于投影能量的特征提取方法来提取反映不同思维状态的脑电特征,并结合信息累积后验贝叶斯方法进行分类以提高脑-机接口系统的分类正确率。该方法通过使两类信号在投影基上的平均投影能量比达到极值,从而达到提高脑电信号分类准确度的作用。实验结果表明两个运动想象数据集上的最大正确率都达到90%左右,最大分类准确率、kappa系数和最大互信息等评价指标的比较也表明该方法能够有效提高BCI系统的性能,具有较好的实用性。     

Design of electrode layout for motor imagery based brain?computer interface

A simple electroencephalogram (EEG) electrode layout is proposed to implement a motor imagery based brain-computer interface (BCI). The design was derived from investigation of EEG synchronisation in the motor cortex. A significant improvement in BCI performance was obtained in the new system     

Combining spatial filters for the classification of single-trial EEG in a finger movement task

Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM.     

EtherCAT总线分布式多电机控制研究

针对工业现场多电机控制需求,研究了基于现场总线的多电机控制原理,采用EtherCA工业实时以太网作为现场网络,构建现场与远程测控相结合的多电机控制系统。基于DSP和ESC实现电机控制器与数据采集从站,针对系统中周期性与非周期性控制任务,设计了基于FPGA的嵌入式主站,通过实时操作系统调度控制与采集任务。在系统内,设计授时单元基于DC分布时钟实现网络内从站同步功能,其精度＜1 μs。基于虚拟仪器技术设计配置管理软件,实现了系统运行参数配置,电机控制参数整定及控制效果图形化显示与评估。应用结果表明,该分布式多电机控制系统运行稳定可靠,且实时性好。     

基于运动想象的异步在线脑-机接口系统

目前,运动想象脑-机接口( motor imagery brain computer interface,MIBCI) 的离线分析和研究相对比较成熟,但是异步在线MIBCI始终具有挑战性。针对在线BCI系统的识别率和控制方式,提出了利用共空间模式(common spatial pattern,CSP)算法对运动想象(motor imagery,MI)进行特征提取并结合alpha波进行异步控制。构建了一种简单实用的自主控制小球运动MIBCI实验系统。有四名受试者参加了在线实验,其中有两名受试者在线运动想象识别正确率最高能达到100%。实验结果验证了本文所建系统的可行性和实用性。      

DSP在三相交流异步电动机调速系统中的应用

为了让交流电机的控制系统具有良好的调速性能,完善其功能的实时性,文中设计了基于DSP的交流电机矢量控制系统。该系统使用了TI公司的专门用于电机控制的芯片TMS320F2812,设计了硬件电路;软件采用专门用于DSP编程的CCS3.3版本。实验结果表明,该系统具有很好的速度跟踪实时性和准确性。     

Chernof f加权分类器框架在运动想象脑-机接口中的应用

针对现有脑机接口(BCI)分类器与大脑认知过程结合不够紧密的问题,该文提出一种基于Chernoff加权的分类器集成框架方法,并用于同步运动想象脑机接口中。通过对训练数据进行统计分析,获得各时刻脑电信号(EEG)的统计特性,并建立基于大脑认知过程的高斯概率模型。然后利用Chernoff边界特性得到该概率模型的最小误差,并以此确定该时刻分类器的权重,通过对各时刻分类器的加权,实现同步脑机接口的信号分类。以脑机接口竞赛数据作为测试,并与线性判决分析、支持向量机和极限学习方法分别结合构成新的集成方法。由实验结果可知,加权集成框架方法的分类性能比原独立分类方法有显著提高。

     

Research on recognition of O-MI based on CNN combined with SST and LSTM

Recognition algorithms have been widely used in brain computer interface (BCI) for neural paradigms classification. To improve the classification and recognition effect of motor imagery with motor observation (O-MI) in BCI rehabilitation technology, this paper explores the function of convolutional neural network (CNN) combined with synchrosqueezed wavelet transform (SST) and long short-term memory (LSTM) in the recognition and classification of neural activities in the brain motor area. Combining the advantages of SST in signal feature extraction in the pretreatment stage and the ability of LSTM network in time series information modeling, the purpose is to make up for CNN''s shortcomings in both aspects. This paper verifies the algorithm on the self-collected O-MI experimental datasets and the public datasets (BCI competition IV datasets 2a). The results show that the composite CNN algorithm incorporating SST and LSTM achieves higher classification accuracy than classic algorithms and the similar new method which is CNN combined with discrete wavelet transform (DWT) and power spectral density (PSD), so it is convenient for practical application in O-MI BCI system.     

Alternative majority-voting methods for real-time computing systems

Two techniques that provide a compromise between the high time overhead in maintaining synchronous voting and the difficulty of combining results in asynchronous voting are proposed. These techniques are specifically suited for real-time applications with a single-source/single-sink structure that need instantaneous error masking. They provide a compromise between a tightly synchronized system in which the synchronization overhead can be quite high, and an asynchronous system which lacks suitable algorithms for combining the output data. Both quorum-majority voting (QMV) and compare-majority voting (CMV) are most applicable to distributed real-time systems with single-source/single-sink tasks. All real-time systems eventually have to resolve their outputs into a single action at some stage. The development of the advanced information processing system (AIPS) and other similar systems serve to emphasize the importance of these techniques. Time bounds suggest that it is possible to reduce the overhead for quorum-majority voting to below that for synchronous voting. All the bounds assume that the computation phase is nonpreemptive and that there is no multitasking     

Computer simulation and analysis of a new polyphase multipole motordrive

A new configuration for a high-power-density, high-efficiency polyphase multipole permanent magnet motor and its control system are presented. The mathematical model and simulation for this motor drive are presented in detail. The motor is essentially a kind of brushless DC motor with a novel arrangement of its magnet and winding. The control system is virtually a dual closed-loop system with a current controller as the inner loop and a speed controller as the outer loop. State-space equations are used for the mathematical model of the motor, and real-time simulation is applied for the controller and switching devices. The simulation results are verified with the experimental results and shown to be very satisfactory     

Toward self-paced brain-computer communication: navigation through virtual worlds.

The self-paced control paradigm enables users to operate brain-computer interfaces (BCI) in a more natural way: no longer is the machine in control of the timing and speed of communication, but rather the user is. This is important to enhance the usability, flexibility, and response time of a BCI. In this work, we show how subjects, after performing cue-based feedback training (smiley paradigm), learned to navigate self-paced through the "freeSpace" virtual environment (VE). Similar to computer games, subjects had the task of picking up items by using the following navigation commands: rotate left, rotate right, and move forward ( three classes). Since the self-paced control paradigm allows subjects to make voluntary decisions on time, type, and duration of mental activity, no cues or routing directives were presented. The BCI was based only on three bipolar electroencephalogram channels and operated by motor imagery. Eye movements (electrooculogram) and electromyographic artifacts were reduced and detected online. The results of three able-bodied subjects are reported and problems emerging from self-paced control are discussed.     

Analysis of effect of feedback current variation in CT Delta-Sigma modulators with Gm-C integrators

NAO humanoid robots are being used in many human-robot interaction applications. One of the important existing challenges is developing an accurate real-time face recognition system which does not require to have high computational cost. In this research work a real-time face recognition system by using block processing of local binary patterns of the face images captured by NAO humanoid is proposed. Majority voting and best score ensemble approaches have been used in order to boost the recognition results obtained in different colour channels of YUV colour space, which is a default colour space provided by the camera of NAO humanoid. The proposed method has been adopted on NAO humanoid and tested under real-world conditions. The recognition results were boosted in the real-time scenario by employing majority voting on the intra-sequence decisions with window size of 5. The experimental results are showing that the proposed face recognition algorithm overcomes the conventional and state-of-the-art techniques.     

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.     

自适应脑机接口研究综述

脑机接口(BCI)不依赖于外周神经和肌肉,在大脑与外部设备之间建立起直接交流的通路。近年来,该技术在识别准确率和系统交互速率方面已取得巨大突破。然而,脑电(EEG)信号非平稳特性较强且用户主观状态波动较大,传统脑机接口技术对大脑活动的动态变化欠缺适应性,影响了脑机接口系统的控制稳定性,也限制了其智能化发展和应用。自适应脑机接口可根据大脑当前状态动态调整诱发范式和实时更新识别模型,从而增强脑控系统对非平稳大脑活动的适应性,提高其控制精度和鲁棒性,实现更加实用化的脑控系统,对推动脑机接口技术进一步发展极具意义。该文对自适应脑机接口的相关研究进行了回顾和总结,并对该技术未来发展的方向进行了展望。