A deep wavelet sparse autoencoder method for online and automatic electrooculographical artifact removal

Electrooculographical (EOG) artifacts are problematic to electroencephalographical (EEG) signal analysis and degrade performance of brain–computer interfaces. A novel, robust deep wavelet sparse autoencoder (DWSAE) method is presented and validated for fully automated EOG artifact removal. DWSAE takes advantage of wavelet transform and sparse autoencoder to become a universal EOG artifact corrector. After being trained without supervision, the sparse autoencoder performs EOG correction on time–frequency coefficients collected after brain wave signal wavelet decomposition. Corrected coefficients are then used for wavelet reconstruction of uncontaminated EEG signals. DWSAE is compared with five other methods: second-order blind identification, information maximization, joint approximation diagonalization of eigen-matrices, wavelet neural network (WNN) and wavelet thresholding (WT). Experimental results on a visual attention task dataset, a mental state recognition dataset and a semi-simulated contaminated EEG dataset show that DWSAE is capable of suppressing EOG artifacts effectively, while preserving the nature of background EEG signals. The mean square error of signals before and after correction by DWSAE on a semi-simulated contaminated EEG segment of 30 s is the lowest (65.62) when compared to the results produced by WNN and WT. DWSAE addresses limitations posed by these methods in three ways. First, DWSAE can be performed automatically and online in a single channel of EEG data; this has advantages over independent component analysis-based methods. Second, its results are robust and stable in comparison with those of other wavelet-based methods. Third, as an unsupervised learning scheme, DWSAE does not require the off-line training that is necessary for WNN and other supervised learning machine learning-based methods.

     

Time perception as a workload measure in simulated car driving

In experimental studies using flight simulations subjects’ duration estimates have shown to be an effective indicator of cognitive task demands. In this study we wanted to find out whether subjective time perception could serve as a measure of cognitive workload during simulated car driving. Participants drove on a round course of a driving simulator consisting of three different environments with different levels of task demands. Drivers were required to perform a time-production task while driving the vehicle. Electrodermal activity and subjective ratings of mental workload (SWAT) were recorded simultaneously. The length of produced intervals increased significantly in more complex driving situations, as did electrodermal activity and subjective ratings of mental workload. Thus, time production is a valid indicator of cognitive involvement in simulated driving and could become a valid method to measure the current mental workload of car drivers in various traffic situations.     

Effects of different physical workload parameters on mental workload and performance

The design and evaluation of an occupational task should include an assessment of mental workload, since excessive levels of mental workload can cause errors or delayed information processing. Physically demanding work that is performed concurrently with a cognitive task may impact mental workload by impairing mental processing or decreasing performance. The primary objective of this study was to determine whether there is a differential effect of various types of physical activity on both mental workload and cognitive performance. Objective and subjective assessment tools (heart rate variability and visual analog scale) were used as indicators of mental workload, while correct responses during an arithmetic task reflected levels of performance. Thirty participants (ages 18-24 years) performed a combination of tasks inducing both physical and mental workload. Type of physical effort, frequency of movement, and force exertion level were manipulated to alter the workload associated with the physical activity. Changes in subjective ratings generally corresponded to changes in both performance on the arithmetic task and objective mental workload assessment. Some discrepancies occurred at the highest physical force exertion level as participants perceived an increase in effort to maintain the same level of performance. Further research is needed to determine the force exertion threshold, beyond which the physical effort required interferes with mental workload and/or cognitive performance.

Relevance to industry

Technological advancements have increased the requirement for many workers to execute cognitive tasks concurrently with physical activity. When designing and evaluating such situations it is important to determine the interactive effects of these activities. A simple, uni-dimensional tool is suggested as a screening tool to identify situations requiring excessive or increased mental workload that many degrade performance or place additional stress on the individual.     

基于小波包分解和遗传神经网络的癫痫识别

基于小波包分解和遗传神经网络对正常脑电和癫痫脑电进行识别。通过分析脑电数据找出信号特征;利用一维离散小波包分解提取含有识别特征的脑电信号频率段,并以脑电各频段的相对能量作为信号特征;然后建立基于遗传算法优化的BP网络,用于对癫痫脑电识别。实验结果表明,该方法可以有效提取信号特征,并且对信号进行准确的识别。     

Epileptic seizure endorsement technique using DWT power spectrum

EEG signals play significant role in the study of mental disorders. Epilepsy is one of the major mental disorders and need significant technological support in the treatment. A method proposed here is an endorsement technique for epileptic seizures using electroencephalogram (EEG) signals captured using non-invasive method. The method uses power spectrum density and discrete wavelet transformation (DWT). The impact of power spectral analysis along with the usage of EEG characteristics in endorsement of epilepsy is addressed here. A publicly available EEG epileptic dataset is processed using FIR filters along with DWT. The power spectrum density and its average were compared with specific spectrum to get the results and were compared against the standard EEG signal frequency range. It is found that the usage of DWT is more accurate and reliable to process and classify the EEG data for epilepsy endorsement.

     

Functional near-infrared spectroscopy in the evaluation of urban rail transit drivers’ mental workload under simulated driving conditions

The objective of this study is to investigate the potential of functional near-infrared spectroscopy (fNIRS) combined with heart rate variability indices, for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions, particularly during task engagement and disengagement. Experienced metro drivers wearing fNIRS monitoring systems were asked to drive for 90?min in a professional metro driving simulator. Workload stimulus tasks were added and an n-back task (n?=?3) was implemented to induce workload in the simulated driving experiment. Experimental results indicate that fNIRS are sensitive to mental workload and reliable for discriminating the degree of mental workload. Research findings demonstrate the feasibility and reliability of fNIRS as a tool for real-time evaluating and monitoring driver mental workload along with task factors from a perspective of brain activations during simulated or actual driving.

Practitioner Summary: This study provides evidence for the potential of functional near-infrared spectroscopy (fNIRS) for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions. The first fNIRS application to mental workload evaluation in the field of urban rail transportation helps companies develop reasonable shiftwork schedule and ensure operation safety.

Abbreviations: fNIRS: functional near-infrared spectroscopy; oxy-Hb: Oxy-hemoglobin; NASA-TLX: National Aeronautics and Space Administration Task Load Index; EEG: electroencephalogram; ECG: electrocardiogram; HRV: variability; LF: low-frequency power; HF: high-frequency power; PFC: prefrontal cortex; NIRS: near-infrared spectroscopy; DWT: discrete wavelet transform; EMG: electromyography; DT: determination test; TP: total power; LF_norm: standardized LF; HF_norm: standardized HF; VLF: very low frequency; deoxy-Hb: deoxy-hemoglobin.     

集成胶囊网络的脑电情绪识别

为了充分提取脑电信号多频带的时频信息和保留导联空间分布的位置信息,提出了一种基于集成胶囊网络的情绪识别模型.对预处理过的脑电信号进行小波包特征提取,并将Theta、Alpha、Beta、Gamma四个频带的小波系数能量值填充于根据导联空间分布映射的稀疏矩阵中,拼接构成多频带特征矩阵,通过胶囊网络对特征数据进行训练,对不...     

Selection of input features across subjects for classifying crewmember workload using artificial neural networks

The issue of crewmember workload is important in complex system operation because operator overload leads to decreased mission effectiveness. Psychophysiological research on mental workload uses measures such as electroencephalogram (EEG), cardiac, eye-blink, and respiration measures to identify mental workload levels. This paper reports a research effort whose primary objective was to determine if one parsimonious set of salient psychophysiological features can be identified to accurately classify mental workload levels across multiple test subjects performing a multiple task battery. To accomplish this objective, a stepwise multivariate discriminant analysis heuristic and artificial neural network feature selection with a signal-to-noise ratio (SNR) are used. In general, EEG power in the 31-40-Hz frequency range and ocular input features appeared highly salient. The second objective was to assess the feasibility of a single model to classify mental workload across different subjects. A classification accuracy of 87% was obtained for seven independent validation subjects using neural network models trained with data from other subjects. This result provides initial evidence for the potential use of generalized classification models in multitask workload assessment.     

Measuring Mental Workload Using ERPs Based on FIR,ICA, and MARA

Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman’s correlation during a simulated computer task. We found that participants responded faster and performed better in the easy task condition, followed by the medium and high-difficulty conditions, which verifies the validity of the ERP filtering. Moreover, larger P2 and P3 waveforms were evoked as the task difficulty increased, and a higher task difficulty elicited a more enhanced N300. Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings, and a positive relationship between the P3 amplitude and accuracy. The results presented in this paper demonstrate that a combination of FIR, ICA, and MARA methods can filter ERPs in the non-invasive real-time measurement of workload. Additionally, frontocentral P2, N3, and parietal P3 components showed differences between genders. The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human–computer interaction in the future.     

抑郁症静息脑电的小波包节点功率谱熵分析

头皮脑电(EEG)信号反映了大脑皮层神经元细胞群自发性节律性的电生理活动,含有丰富的生理与病理信息,是临床脑神经与精神疾病诊断的重要依据.针对抑郁症的研究和诊断中缺少客观有效的量化参数和指标的状况,提出一种基于小波包分解节点重构信号的功率谱熵值(记为W值)的脑电信号分析方法,并利用此方法对静息态的脑电信号进行计算和分析.实验和分析结果表明:抑郁症患者脑电信号S32节点(频率24~32 Hz)的熵值(置信区间[0.0129,0.0176])在部分脑区显著大于正常健康人(置信区间[0.0246,0.0303]),显示抑郁症病人快波节律的能量分布存在弥散性,符合现在关于抑郁症患者自我调节能力减弱的发病机制.对结果进行了T检验统计分析,证明了这种辨别方法的准确性和可行性,将为抑郁症疾病检测诊断提供有效的量化物理指标.     

Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing

In this work, event related potentials (ERPs) induced by visual stimuli categorized with different value of affective valence are studied. EEG signals are recorded during visualization of selected pictures belonging to International Affective Picture System (IAPS). A Morlet wavelet filter is used to transform the EEG input space to a topography-time–frequency feature space. Support vector machine-recursive feature elimination (SVM-RFE) is applied for detecting scalp spectral dynamics of interest (SSDOIs) in this feature space, allowing to identify the most relevant time intervals, frequency bands and EEG channels. This feature selection method has proven to outperform the classical t-test in the discrimination of brain cortex regions involved in affective valence processing. Furthermore, the presented combination of feature extraction and selection techniques can be applied as an alternative in other different clinical applications.     

基于小波相干算法的脑区情绪特性研究

研究了人脑不同区域情绪脑电信号的差异特性。按照国际10-20电极分布系统将大脑分成5个脑区,选用视频情绪诱发素材诱发被试产生正性、中性、负性情绪同时采集其脑电信号,设置各脑区小波相干指数为参数,研究其差异性并进行模式识别。结果显示:不同情绪状态下额叶、顶叶δ波段的小波相干指数具有显著差异(p<0.05),并且统计发现将中性情绪小波相干指数作为基准,负性情绪的小波相干指数增大,正性情绪的小波相干指数降低。实验结果验证了额叶和顶叶的小波相干指数对情绪三分类问题有较好的识别效果,顶叶情绪识别率高达96.67%,进一步证明了情绪处理时额叶、顶叶两个脑区被激活,且不同情绪状态下激活程度不同。     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5–30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8–12 Hz) frequency band is weakened, and the beta (13–30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake–fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5-30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8-12 Hz) frequency band is weakened, and the beta (13-30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake-fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

Pilot mental workload: how well do pilots really perform?

The purpose of this study was to investigate the effects of increasing mental demands on various aspects of aircrew performance. In particular, the robustness of the prioritization and allocation hierarchy of aviate–navigate–communicate was examined, a hierarchy commonly used within the aviation industry. A total of 42 trainee pilots were divided into three workload groups (low, medium, high) to complete a desktop, computer-based exercise that simulated combinations of generic flight deck activities: flight control manipulation, rule-based actions and higher level cognitive processing, in addition to Air Traffic Control instructions that varied in length from one chunk of auditory information to seven chunks. It was found that as mental workload and auditory input increased, participants experienced considerable difficulty in carrying out the primary manipulation task. A similar decline in prioritization was also observed. Moreover, when pilots were under a high mental workload their ability to comprehend more than two chunks of auditory data deteriorated rapidly.     

Pilot mental workload: how well do pilots really perform?

The purpose of this study was to investigate the effects of increasing mental demands on various aspects of aircrew performance. In particular, the robustness of the prioritization and allocation hierarchy of aviate-navigate-communicate was examined, a hierarchy commonly used within the aviation industry. A total of 42 trainee pilots were divided into three workload groups (low, medium, high) to complete a desktop, computer-based exercise that simulated combinations of generic flight deck activities: flight control manipulation, rule-based actions and higher level cognitive processing, in addition to Air Traffic Control instructions that varied in length from one chunk of auditory information to seven chunks. It was found that as mental workload and auditory input increased, participants experienced considerable difficulty in carrying out the primary manipulation task. A similar decline in prioritization was also observed. Moreover, when pilots were under a high mental workload their ability to comprehend more than two chunks of auditory data deteriorated rapidly.     

基于小波包技术的EEG信号特征波提取分析

为了更有效地提取脑电信号特征波,结合小波包技术,提出了一种脑电特征波提取方法。首先对脑电信号进行小波包分解,然后进行相关频段信号的重构,从而提取出特征波,并对其进行功率谱分析和能量计算。实验结果表明,小波包技术能有效地提取脑电信号特征波。     

基于小波变换Mallat算法的电网谐波检测方法

针对传统的傅里叶变换方法在分析非平稳运行电网的电量信号时误差较大的问题,提出了一种基于小波变换Mallat算法的电网谐波检测方法。该方法根据不同的分辨率将电量信号分解到不同的子频段,然后分别对子频段进行多次重构,得到原始信号的基波,最后将采样得到的原始信号与重构的基波信号相减,得到谐波信号。Matlab仿真结果表明,该方法能够有效地将电量信号中的基波与谐波成分分离,谐波检测精确度较高。     

Combined neural network model employing wavelet coefficients for EEG signals classification

This paper illustrates the use of combined neural network model to guide model selection for classification of electroencephalogram (EEG) signals. The EEG signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the EEG signals classification using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 94.83% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model.