融合运动想象脑电与眼电信号的机械臂控制系统开发

脑机接口(brain computer interface, BCI)旨在通过脑电信号与外部设备通信,以实现对外部设备的控制。针对目前脑机接口系统中混合多种复杂生理电信号,并且输出控制指令较少的问题,本文提出融合运动想象(motor imagery, MI)脑电与眼电信号方法扩充控制指令的轻量级机械臂控制系统。该系统分阶段融合脑电和眼电信号两种生物信号,使用双次眼电作为任务开关,运动想象脑电信号控制机械臂运动,单次眼电控制阶段切换,实现了二分类运动想象生成多种控制指令,完成了对机械臂的连续控制。其中运动想象脑电信号使用提升小波变换(lifting wavelet transform, LWT)和共空间模式(common spatial pattern, CSP)结合的方法提取特征,并采用支持向量机(support vector machines, SVM)进行分类;眼电信号通过分析无意识眼电和有意识眼电的峰值来设置阈值进行区分。为了验证系统的可行性,设计了一项脑控机械臂自主服药实验,通过在线实验测试,被试通过使用脑电信号和眼电信号实现了机械臂控制,并完成了服药流程,有利于进一步推广脑机接口技术的实际应用。     

一种基于脑电信号的眼动方向分类方法

为了提高基于眼电的眼动方向的识别准确性,文中利用包含眼电伪迹的脑电信号,提出了一种新的眼动方向分类方法。首先,在10-20国际标准导联配置下,通过脑电仪采集靠近人脑额叶处的AF7,F7,FT7,T7,AF8,F8,FT8,T8这8个通道的脑电信号;然后,通过基线移除、归一化、最小二乘法降噪等进行数据预处理;最后,采用支持向量机的方法进行眼动方向的多次二分类,并使用投票策略实现眼动方向的四分类识别。实验结果表明,所提方法进行眼动方向分类时,在上、下、左、右4个方向上的分类率分别达到了78.47%,72.22%,84.03%,79.86%,平均分类率达到了78.65%。与已有的分类方法相比,所提方法的分类准确率更高,分类算法的实现过程更简单,这进一步验证了利用脑电信号识别眼动方向的可行性和有效性。     

基于无线传感器技术的人机接口系统设计

在综合研究脑电和眼电信号采集理论和方法的基础上,研制出一种基于无线传感器技术的新型多通道人机接口系统,包括无线传感器部分、信号处理和控制电路部分.该系统实时采集受试者垂直眼电和水平眼电,将其数字化,通过短距离无线技术传送给主机,主机信号处理系统根据眼电幅值判断受试者眼球转动方向,由此控制遥控汽车做相应的移动.实验结果表...     

Reliable EOG signal-based control approach with EEG signal judgment

This article proposes a reliable EOG signal-based control approach with EEG signal judgment. In this method, raw bio-neurological signals (including EOG and EEG) are first extracted and segmented in the pre-processing stage. The processed bio-neurological signals will then be evaluated by calculating the feature parameters of these signals. Since the feature parameters in bio-neurological signals may be contaminated by various kinds of artifacts, some artifacts of bio-neurological signals can be indicated by means of the feature parameters of bio-neurological signals. Therefore, the bio-neurological signals contaminated with artifacts cannot be adopted to generate control signals or to judge the correctness of control signals. In the proposed method, in order to generate a reliable control signal based on the EOG signal, the EEG signal is adopted to assist in making a judgment about the validity of the EOG signal. With the proposed method, an EOG signal-based control software platform has been implemented. By using this platform, simulation work has been carried out to control the behavior of a robot. The simulation results verified the effectiveness of the proposed method.     

Prediction of product design decision Making: An investigation of eye movements and EEG features

Design decision making is happened in every design node and iteration, and the expert decision-making bias and personal preference will ultimately affect the success or failure of the product reaching the market. In this paper, we try to predict the design decision making by investigating the relations between design decision making and subjects’ eye movements and Electroencephalogram(EEG) response. Four different methods were applied and compared to classify the different EEG features and two methods were used for EEG feature selection to correspond the design decision making results. In this study, the authors applied a multimodal fusion strategy for design decision making recognition where the authors used eye tracking and EEG response data as input dataset. According to the experiment results, the performance of the fusion strategy combined with EEG signals and eye movement characteristics is well in fitting the expert decision making results. The multimodal fusion combining eye tracking data and EEG has a strong potential to be a new design decision method to guide the design practice and provide supportive and objective data to reduce the effects of subjectivity, one-sidedness and superficiality in decision making. These results show that it is possible to create a classifier based on features extracted from eye movements and EEG response for the design decision making behaviour.     

ANC Schemes for the Enhancement of EEG Signals in the Presence of EOG Artifacts

One of the most important applications of adaptive systems is in noise cancellation using adaptive filters. In this paper, we propose adaptive noise cancellation schemes for the enhancement of EEG signals in the presence of EOG artifacts. The effect of two reference inputs is studied on simulated as well as recorded EEG signals and it is found that one reference input is enough to get sufficient minimization of EOG artifacts. This has been verified through correlation analysis also. We use signal to noise ratio and linear prediction spectra, along with time plots, for comparing the performance of the proposed schemes for minimizing EOG artifacts from contaminated EEG signals. Results show that the proposed schemes are very effective (especially the one which employs Newton's method) in minimizing the EOG artifacts from contaminated EEG signals.     

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

To improve applicability of automatic sleep staging an efficient subject-independent method is proposed with application in sleep–wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess the method, polysomnographic (PSG) records of 40 patients from our ISRUC-Sleep dataset, which was scored by an expert clinician in the central hospital of Coimbra, are used. To find the best combination of PSG signals for automatic sleep staging, six electroencephalographic (EEG), two electrooculographic (EOG), and one electromyographic (EMG) channels are analyzed. An extensive set of feature extraction techniques are applied, covering temporal, frequency and time–frequency domains. The maximum overlap wavelet transform (MODWT), a shift invariant transform, was used to extract the features in time–frequency domain. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. The most discriminative features are selected through a two-step method composed by a manual selection step based on features’ histogram analysis followed by an automatic feature selector. The selected feature set is classified using support vector machines (SVMs). The system achieved the best performance by combining 6 channels (C3, C4, O1, left EOG (LOC), right EOG (ROC) and chin EMG (X1)) for sleep–wake detection, and 9 channels (C3, C4, O1, O2, F3, F4, LOC, ROC, X1) for multiclass sleep staging.     

Vigilance on the civil flight deck: incidence of sleepiness and sleep during long-haul flights and associated changes in physiological parameters

The study investigated sleepiness and sleep in aircrew during long-haul flights. The objectives were to identify loss of alertness and to recommend a practical approach to the design of an alerting system to be used by aircrew to prevent involuntary sleep. The flights were between London and Miami, covering both day- and night-time sectors, each with a duration of ~9 h. The subjects were 12 British Airways pilots. Various physiological variables were measured that could potentially be used to indicate the presence of drowsiness and involuntary sleep: brain electrical activity (electroencephalogram, EEG), eye movements via the electro-oculogram (EOG), wrist activity, head movements and galvanic skin resistance. The EEG and EOG identified sleepiness and sleep, as well as being potential measures on which to base an alarm system. Ten pilots either slept or showed evidence of sleepiness as assessed by the EEG and EOG. Many of the episodes of sleepiness lasted < 20 s, which could mean that the subjects were unaware of their occurrence and of the potential consequences on performance and vigilance. All physiological parameters showed changes during sleep, although only the EEG and EOG were modified by sleepiness. During sleep, skin resistance was increased, and wrist activity and head movements were absent for long periods. The study indicated that the measurement of eye movements (either alone or in combination with the EEG), wrist activity or head movement may be used as the basis of an alarm system to prevent involuntary sleep. Skin resistance is considered to be unsuitable, however, being related in a more general way to fatigue rather than to sleep episodes. The optimal way to monitor the onset of sleep would be to measure eye movements; however, this is not feasible in the flight deck environment at the present time due to the intrusive nature of the recording methodology. Wrist activity is therefore recommended as the basis of an alertness alarm. Such a device would alert the pilot after ~4–5 min of wrist inactivity, since this duration has been shown by the present study to be associated with sleep. The possibility that sleep inertia (reduced alertness immediately after awakening from sleep) could follow periods of sleep lasting 5 min needs to be considered. The findings reported here might be applicable to other occupational environments where fatigue and sleepiness are known to occur.     

Vigilance on the civil flight deck: incidence of sleepiness and sleep during long-haul flights and associated changes in physiological parameters

The study investigated sleepiness and sleep in aircrew during long-haul flights. The objectives were to identify loss of alertness and to recommend a practical approach to the design of an alerting system to be used by aircrew to prevent involuntary sleep. The flights were between London and Miami, covering both day- and night-time sectors, each with a duration of approximately 9 h. The subjects were 12 British Airways pilots. Various physiological variables were measured that could potentially be used to indicate the presence of drowsiness and involuntary sleep: brain electrical activity (electroencephalogram, EEG), eye movements via the electro-oculogram (EOG), wrist activity, head movements and galvanic skin resistance. The EEG and EOG identified sleepiness and sleep, as well as being potential measures on which to base an alarm system. Ten pilots either slept or showed evidence of sleepiness as assessed by the EEG and EOG. Many of the episodes of sleepiness lasted < 20 s, which could mean that the subjects were unaware of their occurrence and of the potential consequences on performance and vigilance. All physiological parameters showed changes during sleep, although only the EEG and EOG were modified by sleepiness. During sleep, skin resistance was increased, and wrist activity and head movements were absent for long periods. The study indicated that the measurement of eye movements (either alone or in combination with the EEG), wrist activity or head movement may be used as the basis of an alarm system to prevent involuntary sleep. Skin resistance is considered to be unsuitable, however, being related in a more general way to fatigue rather than to sleep episodes. The optimal way to monitor the onset of sleep would be to measure eye movements; however, this is not feasible in the flight deck environment at the present time due to the intrusive nature of the recording methodology. Wrist activity is therefore recommended as the basis of an alertness alarm. Such a device would alert the pilot after approximately 4-5 min of wrist inactivity, since this duration has been shown by the present study to be associated with sleep. The possibility that sleep inertia (reduced alertness immediately after awakening from sleep) could follow periods of sleep lasting 5 min needs to be considered. The findings reported here might be applicable to other occupational environments where fatigue and sleepiness are known to occur.     

基于EOG眼电信号的机器人三维定位

在控制机器人的各种方法中,采用生物电信号控制机器人的研究发展非常迅速,成为二十一世纪最热门的研究课题。眼电图EOG(electro-oculography)方法是目前唯一一种信号产生于生物电的眼运动记录技术。本文在对EOG的产生原理及扫视信号提取的方法进行分析的基础上,利用EOG信号提取侧、俯平面的视角变化来实现对机器人进行三维空间移动定位,最后通过仿真实验对整个移动定位控制的有效性进行了验证。     

基于头动与眼电信号的疲劳检测研究

针对疲劳识别率有待提高和现行疲劳检测设备不便携带的问题,提出一种以便携式眼镜为载体结合处理头动与眼电信号的疲劳检测方法.利用便携式眼镜采集头动与眼电信号并通过蓝牙将数据传输到手机终端.采用融合卡尔曼滤波算法处理头动信号并提取点头频率特征,采用Perclos算法P80原理和分段平均功率比值法处理眼电信号得到眨眼频率和低高...     

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG,EOG, AND EMG EXTRACTED FEATURES

A new system for sleep multistage level scoring by employing extracted features from twenty five polysomnographic recording is presented. For the new system, an adaptive neuro-fuzzy inference system (ANFIS) is developed for each sleep stage. Initially, three types of electrophysiological signals including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were collected from twenty five healthy subjects. The input pattern used for training the ANFIS subsystem is a set of extracted features based on the entropy measure which characterize the recorded signals. Finally an output selection subsystem is utilized to provide the appropriate sleep stage according to the ANFIS stage subsystems outputs. The developed system was able to provide an acceptable estimation for six sleep stages with an average accuracy of about 76.43% which confirmed its ability for multistage sleep level scoring based on the extracted features from the EEG, EOG and EMG signals compared to other approaches.     

脑电信号中眼电伪迹自动识别与去除方法研究

传统盲源分离算法消除眼电伪迹须用到两个眼电信号作为参考,但在采集眼电信号时易给被试带来不适产生噪声,且识别时需要人为辨别,为了解决这些问题,提出一种基于FastICA的眼电伪迹自动去除方法。该方法先计算出FastICA提取出的各独立成分与GFP（Global Field Power）值的相关系数,再比较相关系数,将其绝对值最大所对应的独立成分识别为眼电伪迹独立成分,最后把该独立成分置零重构干净的脑电信号,实现眼电伪迹的自动去除。通过自采的30例脑电数据实验结果表明：该方法能完全自动地去除眼电伪迹成分并有效保留其他脑电成分,且快速准确,适用于实时场合。     

Automatic removal of high-amplitude artefacts from single-channel electroencephalograms

In this work, we present a method to extract high-amplitude artefacts from single channel electroencephalogram (EEG) signals. The method is called local singular spectrum analysis (local SSA). It is based on a principal component analysis (PCA) applied to clusters of the multidimensional signals obtained after embedding the signals in their time-delayed coordinates. The decomposition of the multidimensional signals in each cluster is achieved by relating the largest eigenvalues with the large amplitude artefact component of the embedded signal. Then by reverting the clustering and embedding processes, the high-amplitude artefact can be extracted. Subtracting it from the original signal a corrected EEG signal results. The algorithm is applied to segments of real EEG recordings containing paroxysmal epileptiform activity contaminated by large EOG artefacts. We will show that the method can be applied also in parallel to correct all channels that present high-amplitude artefacts like ocular movement interferences or high-amplitude low frequency baseline drifts. The extracted artefacts as well as the corrected EEG will be presented.     

Head Movement Based Interaction in Mobility

The touchless techniques in human computer interaction (HCI) can effectively expand communication capabilities. In the paper we present the innovative touchless computer control method based on head movement analysis. The aim of our work was to replace the standard mouse with the movements of the user’s head. In contrast to the known solutions, our proposition does not require image recording of the user’s head and complex image analysis. The analysis of position in our solution is made using the camera worn by the user on the head. A project of such a solution has been developed and the research of it has been carried out. It has been shown that in this way it is possible to effectively move the screen cursor to the position which is identified by the user’s face orientation. Additionally, in this solution, the eye image analysis has been performed. Interpretation of blinking allowed executing system commands. Using the built prototype the experiments have been carried out in a group of 30 people. Studies have shown high efficiency and ergonomics of the proposed solution.     

A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis

ElectroEncephaloGram (EEG) gives information about the electrical characteristics of the brain. EEG can be used for various applications, such as diagnosis of diseases, neuroscience and Brain Computer Interface (BCI). Several artefacts sources can disturb the brain signals in EEG measurements. The signals caused by eye movements are the most important sources of artefacts that must be removed in order to obtain a clean EEG signal. During the removal of Ocular Artefacts (OAs), the preserve of the original EEG signal is one of the most important points to be taken into account. An ElectroOculoGram (EOG) reference signal is needed in order to remove OAs in some methods. However, long-term EOG measurements can disturb a subject. In this paper, a novel robust method is proposed in order to remove OAs automatically from EEG without EOG reference signal by combining Outlier Detection and Independent Component Analysis (OD-ICA). The OD-ICA method searches OA patterns in all components instead of a single component. Moreover, OD-ICA removes only OA patterns and preserves meaningful EEG signal. In this method, user intervention is not needed. These advantages make the method robust. The OD-ICA is tested on two real datasets. Relative Error (RE), Correlation Coefficient (CorrCoeff) and percentage of finding OA pattern are used for the performance test. Furthermore, three different methods are used as Outlier Detection (OD) methods. These are the Chauvenet Criterion, the Peirce's Criterion and the Adjusted Box Plot. The performance analysis is made between our proposed method and the method of zeroing the component with artefact. The experiment results show that the proposed OD-ICA method effectively removes OAs from EEG signals and is also successful in preserving the meaningful EEG signals during the removal of OAs.     

基于CuBICA算法的EEG伪迹去除方法

在高阶累积量和独立分量分析的基础上,提出一种基于CuBICA算法的脑电信号伪迹去除方法。针对脑电信号中常含有的眼电、心电等伪迹问题,利用小波包方法对原始脑电信号去噪,并进行中心化和白化处理,运用CuBICA算法对消噪后的脑电信号进行盲源分 离。分析分离后各信号间相关性,结果表明,CuBICA算法能成功分离脑电、眼电与心电信号,有效去除纯脑电信号中的各种伪迹。     

基于左右手运动想象的在线算法设计与应用

本文设计了基于左、右手运动想象的脑电信号预处理、共同空域模式特征提取、SVM分类在线算法,开发了无线发射、接收开关硬件模块,实现了在线脑电开关系统。受试者可以用脑电波来遥控电灯的关开,这为重症瘫痪病人拓展其与自然的直接交流开辟了新的通道。5位健康的受试者参与了训练实验和在线实验,实验结果表明：经过特定训练,受试者均可有效控制该脑电开关系统,其平均正确率达90%,单个指令输出时间平均为4秒。     

A method for evaluating head-controlled computer input devices using Fitts' law

The discrete movement task employed in this study consisted of moving a cursor from the center of a computer display screen to circular targets located 24.4 and 110.9 mm in eight radial directions. The target diameters were 2.7, 8.1, and 24.2 mm. Performance measures included movement time, cursor path distance, and root-mean-square cursor deviation. Ten subjects with no movement disabilities were studied using a conventional mouse and a lightweight ultrasonic head-controlled computer input pointing device. Average movement time was 306 ms greater (63%) for the head-controlled pointer than for the mouse. The effect of direction on movement time for the mouse was relatively small compared with the head-controlled pointer, which was lowest at 90 and 270 deg, corresponding to head extension and head flexion, respectively. Average path distance and root mean square displacement was lowest at off-diagonal directions (0, 90, 180, and 270 deg). This methodology was also shown to be useful for evaluating performance using an alternative head-controlled input device for two subjects having cerebral palsy, and measured subtle performance improvements after providing a disabled subject with lateral torso support.     

基于MEMS加速度传感器的无手鼠标

设计一种无手鼠标,使手部残疾或脊髓损伤的患者能够通过无手操作的方式使用计算机。系统以 MSP430F5529为控制芯片,控制放置在头部的 MEMS 加速度传感器测量头部倾角,并将倾角转换为光标位移量来控制光标的移动;利用声控模块和继电器模块设计声控开关,来实现鼠标按键的点击功能;利用 nRF2401无线模块实现鼠标系统与计算机的无线通信。该倾斜鼠标可以使用户通过倾斜头部来控制光标的移动,通过向吹气开关吹气实现鼠标按键的点击功能。该倾斜鼠标可以辅助手部残疾或脊髓损伤的患者通过无手操作的方式有效地控制光标移动,为其使用计算机提供了一种途径。