Using independent component analysis to remove artifact from electroencephalographic measured during stuttered speech

The electro-encephalographic (EEG) activity of people who stutter could provide invaluable information about the association of neural processing and stuttering. However, the EEG has never been adequately studied during speech in which stuttering naturally occurs. This is owing, in part, to the masking of the EEG signal by artifact from sources such as the speech musculature and from ocular activity. The aim of this paper was to demonstrate the ability of independent component analysis (ICA) to remove artifact from the EEG of stuttering children recorded while they are speaking and stuttering. The EEG of 16 male children who stuttered and 16 who did not stutter was recorded during a reading task. The recorded EEG that contained artifact was then subjected to ICA. The results demonstrated that the EEG assessed during stuttered speech had substantially more noise than the EEG of speech that did not contain stuttering (p<0.01). Furthermore, it was shown that ICA could effectively remove this artifact in all 16 children (p<0.01). The results from one child highlight the findings that ICA can be used to remove dominant artifact that has prevented the study of EEG activity during stuttered speech in children.     

ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user‐dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact‐specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event‐related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.     

基于近似熵的思维脑电信号分类研究

采用近似熵(approximate entropy，Apen)作为脑电信号的特征参数对不同思维作业脑电信号进行了分类研究，并对近似熵算法中参数的选择以及互近似熵在思维脑电分类的应用进行了讨探。研究结果表明，近似熵特征在思维作业脑电信号的分类中取得了较好的应用效果。近似熵作为EEG的信号特征为提高思维作业脑电信号的分类正确率提供了一种新的途径，在基于思维作业BCI的应用中具有重要的实用价值。     

Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP

Eye movement artifacts in electroencephalogram (EEG) recordings can greatly distort grand mean event‐related potential (ERP) waveforms. Different techniques have been suggested to remove these artifacts prior to ERP analysis. Independent component analysis (ICA) is suggested as an alternative method to “filter” eye movement artifacts out of the EEG, preserving the brain activity of interest and preserving all trials. However, the identification of artifact components is not always straightforward. Here, we compared eye movement artifact removal by ICA compiled on 10 s of EEG, on eye movement epochs, or on the complete EEG recording to the removal of eye movement artifacts by rejecting trials or by the Gratton and Coles method. ICA performed as well as the Gratton and Coles method. By selecting only eye movement epochs for ICA compilation, we were able to facilitate the identification of components representing eye movement artifacts.     

Evaluating the efficacy of fully automated approaches for the selection of eyeblink ICA components

Independent component analysis (ICA) offers a powerful approach for the isolation and removal of eyeblink artifacts from EEG signals. Manual identification of the eyeblink ICA component by inspection of scalp map projections, however, is prone to error, particularly when nonartifactual components exhibit topographic distributions similar to the blink. The aim of the present investigation was to determine the extent to which automated approaches for selecting eyeblink‐related ICA components could be utilized to replace manual selection. We evaluated popular blink selection methods relying on spatial features (EyeCatch), combined stereotypical spatial and temporal features (ADJUST), and a novel method relying on time series features alone (icablinkmetrics) using both simulated and real EEG data. The results of this investigation suggest that all three methods of automatic component selection are able to accurately identify eyeblink‐related ICA components at or above the level of trained human observers. However, icablinkmetrics, in particular, appears to provide an effective means of automating ICA artifact rejection while at the same time eliminating human errors inevitable during manual component selection and false positive component identifications common in other automated approaches. Based upon these findings, best practices for (a) identifying artifactual components via automated means, and (b) reducing the accidental removal of signal‐related ICA components are discussed.     

基于独立分量分析的脑电噪声消除

作为一种新的多元统计处理方法，独立分量分析(ICA)是解决盲源分离(BSS)问题的一个有效手段。在简要分析ICA理论及其算法的基础上，提出将其应用到脑电中的眼电伪迹的去除任务。实际采集的生理信号大多由相互独立的成分线性迭加而成，符合ICA要求源信号统计独立的基本假设。与传统方法相比，ICA这种空间滤波器不受信号频谱混迭的限制，消噪的同时能对有用信号的细节成分做到很好的保留，很大程度上弥补了时频域方法的不足。此外解混矩阵的逆可以用来反映独立源的空间分布模式，具有重要的生理意义。     

Automatic removal of eye movement and blink artifacts from EEG data using blind component separation

Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may impact the cognitive processes under investigation. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG data. BBS is a signal-processing methodology that includes independent component analysis (ICA). In contrast to previously explored ICA-based methods for artifact removal, this method is automated. Moreover, the BSS algorithm described herein can isolate correlated electroocular components with a high degree of accuracy. Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.     

高频成分在思维作业脑电信号分类中的应用

采用独立分量分析(ICA)去除脑电伪迹,AR模型提取信号特征、BP神经网络用于模式识别,对2~5种思维作业脑电信号进行了分类研究。研究结果的重要发现是:对于经过ICA去伪迹后的EEG信号,当分类特征取自20~100 H z的高频范围时,分类准确率很高,与特征取自整个信号频段的分类结果大致相等,且大大超过利用2~35 H z的低频EEG节律进行的分类。对于这一现象的解释是,不同思维作业过程中,大脑在工频电场作用下产生了不同的节律同化反应,致使EEG信号的高频部分带有更显著的思维调制信息,从而有利于提高分类准确率。这一现象的发现,为脑电节律同化反应提供了新的证据,也为思维脑电的高准确率分类和高精度脑-机接口的实现提供了新的方法。     

基于节律同化效应的思维脑电信号分类研究

通过对正弦调制光（SML）刺激和无刺激思维脑电信号的对比分类研究，探索了思维脑电信号的节律同化现象对思维脑电信号分类的影响。研究结果表明，大脑在SML刺激下进行思维作业时，思维EEG信号携带的与刺激频率有关的节律同化信息能提高某些思维作业的分类正确率，并且SML刺激产生的局部节律同化效应能减少用于提供分类信息的EEG信号的导联数。这些结果有利于提高基于思维作业脑-计算机接口（BCI）的通信准确率和速度。     

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink‐related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college‐aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back‐projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back‐projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies.     

A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case

Eye movement artifacts represent a critical issue for quantitative electroencephalography (EEG) analysis and a number of mathematical approaches have been proposed to reduce their contribution in EEG recordings. The aim of this paper was to objectively and quantitatively evaluate the performance of ocular filtering methods with respect to spectral target variables widely used in clinical and functional EEG studies. In particular the following methods were applied: regression analysis and some blind source separation (BSS) techniques based on second-order statistics (PCA, AMUSE and SOBI) and on higher-order statistics (JADE, INFOMAX and FASTICA). Considering blind source decomposition methods, a completely automatic procedure of BSS based on logical rules related to spectral and topographical information was proposed in order to identify the components related to ocular interference. The automatic procedure was applied in different montages of simulated EEG and electrooculography (EOG) recordings: a full montage with 19 EEG and 2 EOG channels, a reduced one with only 6 EEG leads and a third one where EOG channels were not available. Time and frequency results in all of them indicated that AMUSE and SOBI algorithms preserved and recovered more brain activity than the other methods mainly at anterior regions. In the case of full montage: (i) errors were lower than 5% for all spectral variables at anterior sites; and (ii) the highest improvement in the signal-to-artifact (SAR) ratio was obtained up to 40dB at these anterior sites. Finally, we concluded that second-order BSS-based algorithms (AMUSE and SOBI) provided an effective technique for eye movement removal even when EOG recordings were not available or when data length was short.     

Feature extraction for on-line EEG classification using principal components and linear discriminants

The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.     

基于小波变换和独立分量分析相结合的癫痫脑电分析

临床上分析癫痫脑电信号非常重要。由于临床记录的癫痫脑电信号中含有大量的伪迹干扰,特别是肌电伪迹,所采集的脑电信号无法正确反映大脑的生理及病理状况。本研究利用小波变换的多分辨率特性和独立分量分析(ICA)的盲源分离特性,把用连续小波变换分解的脑电子带信号作为ICA输入,经ICA分离后,有效地消除了癫痫脑电中的肌电伪迹,并分离出了癫痫样特征波,效果理想。     

Advances in classification of EEG signals via evolving fuzzy classifiers and dependant multiple HMMs

Two novel approaches to the problem of brain signals (electroencephalogram (EEG)) classification are introduced in the paper. The first method is based on a modular probabilistic network architecture that employs multiple dependant hidden Markov models (DM-HMM-D) on the input features (channels). The second method, eClass, is based on an on-line evolvable fuzzy rule base of EEG signal prototypes that represent each class and take into consideration the spatial proximity between input signals. Both approaches use supervised learning but differ in their mode of operation. eClass is designed recursively, on-line, and has an evolvable structure, while DM-HMM-D is trained off-line, in a block-based mode, and has a fixed architecture. Both methods have been extensively tested on real EEG data that is recorded during several experimental sessions involving a single female subject who is exposed to mild pain induced by a laser beam. Experimental results illustrate the viability of the proposed approaches and their potential in solving similar classification problems.     

轻度认知障碍患者EEG动力学特征提取与分类方法研究进展

脑电图可用于对轻度认知障碍的病理性变化进行评估。近年来,脑电领域的特征提取和分类方法广泛地应用到对轻度认知障碍疾病的诊断中。首先从局部耦合与全局同步两个方面,深入分析轻度认知障碍患者脑电信号特征提取方法的应用情况及其优势和不足,而后对当前轻度认知障碍患者脑电信号特征进行分类的多种方法进行详细总结与分析,如支持向量机、k均值以及近年来应用广泛的卷积神经网络等,最后对该领域脑电动力学特征提取与分类方法的未来发展趋势进行展望。     

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 ± 22% at a specificity of 86 ± 7% (mean ± SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.     

Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies

Conventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time-frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results.     

Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies

Conventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time-frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results.