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.     

A preprocessing tool for removing artifact from cardiac RR interval recordings using three‐dimensional spatial distribution mapping

Artifact is common in cardiac RR interval data that is recorded for heart rate variability (HRV) analysis. A novel algorithm for artifact detection and interpolation in RR interval data is described. It is based on spatial distribution mapping of RR interval magnitude and relationships to adjacent values in three dimensions. The characteristics of normal physiological RR intervals and artifact intervals were established using 24‐h recordings from 20 technician‐assessed human cardiac recordings. The algorithm was incorporated into a preprocessing tool and validated using 30 artificial RR (ARR) interval data files, to which known quantities of artifact (0.5%, 1%, 2%, 3%, 5%, 7%, 10%) were added. The impact of preprocessing ARR files with 1% added artifact was also assessed using 10 time domain and frequency domain HRV metrics. The preprocessing tool was also used to preprocess 69 24‐h human cardiac recordings. The tool was able to remove artifact from technician‐assessed human cardiac recordings (sensitivity 0.84, SD = 0.09, specificity of 1.00, SD = 0.01) and artificial data files. The removal of artifact had a low impact on time domain and frequency domain HRV metrics (ranging from 0% to 2.5% change in values). This novel preprocessing tool can be used with human 24‐h cardiac recordings to remove artifact while minimally affecting physiological data and therefore having a low impact on HRV measures of that data.     

基于脑电信号的抑郁情绪倾向研究

目的对在校大学生的脑电信号(electroencephalography,EEG)进行研究,以期找到对抑郁情绪倾向预测具有可行的高识别率的情绪特征。方法首先通过14导脑电设备采集19位高校大学生(女6、男13)的脑电信号并对其进行贝克抑郁量表的测试,按测试结果将其分为实验组和正常组。然后使用Eeglab工具包对采集到的数据进行预处理,得到干净的脑电信号。最后采用事件相关频谱扰动(ERSP)对其进行时频分析,以讨论在时间-频率域内的能量变化与抑郁情绪倾向预测的关系。结果在负性图片刺激下,在Alpha、Beta波段均发现时间窗为50~150 ms、350~450 ms时正常组和实验组之间差异具有统计学意义。在正性图片刺激下,Alpha波段在时间窗为150~250 ms、350~450 ms时正常组和实验组之间差异具有统计学意义,Beta波段在时间窗为300~400 ms时差异具有统计学意义。结论在正负性图片的刺激下,Alpha、Beta波段的部分特殊时间段中存在可识别抑郁情绪倾向的特征值,可为今后研究抑郁情绪倾向性提供一定的参考依据。     

Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods.

A variety of procedures have been proposed to correct ocular artifacts in the electroencephalogram (EEG), including methods based on regression, principal components analysis (PCA) and independent component analysis (ICA). The current study compared these three methods, and it evaluated a modified regression approach using Bayesian adaptive regression splines to filter the electrooculogram (EOG) before computing correction factors. We applied each artifact correction procedure to real and simulated EEG data of varying epoch lengths and then quantified the impact of correction on spectral parameters of the EEG. We found that the adaptive filter improved regression-based artifact correction. An automated PCA method effectively reduced ocular artifacts and resulted in minimal spectral distortion, whereas ICA correction appeared to distort power between 5 and 20 Hz. In general, reducing the epoch length improved the accuracy of estimating spectral power in the alpha (7.5-12.5 Hz) and beta (12.5-19.5 Hz) bands, but it worsened the accuracy for power in the theta (3.5-7.5 Hz) band and distorted time domain features. Results supported the use of regression-based and PCA-based ocular artifact correction and suggested a need for further studies examining possible spectral distortion from ICA-based correction procedures.     

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.     

Prevalence and Methods of Control of the Cephalic Skin Potential EEG Artifact

Prevalence of the cephalic skin potential (CSP) artifact was studied in 21 Ss during EEG recording of the Contingent Negative Variation (CNV), averaged evoked response (AER), and verbal free association test. Skin potential response and electro-oculogram (EOG) were also recorded. Subdermal pin electrodes and local anesthesia infusion were employed to eliminate the CSP artifact in the EEG. Results indicated that EEG recorded from subdermal pin electrodes or from locally anesthetized scalp was free of CSP artifact. The EEG recorded from subdermal pin electrodes demonstrated spontaneous potential shifts but appeared adequate for EEG recording of the CNV or the AER. Significant CSP artifact was demonstrated in the EEG of 10 of 21 Ss, both evoked by stimuli (10 Ss) and spontaneous (3 Ss). CSP artifact significantly increased CNV amplitude. CSP artifact significantly increased the AER late positive wave (P3) to infrequent tones. Studies of CNV and AER can be confounded by CSP artifact. Above techniques appear promising for recording EEG free of CSP artifact.     

EEG coherence and power changes during a continuous movement task

EEG coherence (COH) is a mathematically derived measure of the time- and frequency-related similarities between a pair of EEG channels. In this report, COH was measured during an externally verified motor task in which the areas of cortical involvement are known, with special consideration given to procedural and artifactual issues. Fourteen right-handed women (ages 18-39, means = 26.7 years) were instructed to alternate continuously between fist-clenching and finger extension of the right hand, left hand, both hands, or neither hand (rest condition) in a counter-balanced sequence (4 one-minute trials for each condition; 16 total minutes). One minute each of intentional eye-movement (EOG) and intentional facial muscle tension (EMG) was recorded for artifact assessment. Eight channels of eyes-closed EEG were recorded from Fp1, Fp2, F3, F4, C3, C4, P3 and P4, each referenced to the ipsilateral earlobe. FFT spectral power analyses were conducted on 8 EEG channels and COH analyses (percentage of seconds/minute in which COH greater than or equal to 0.80) were performed on 16 pairs of leads: 4 interhemispheric, 6 intrahemispheric (left) and 6 intrahemispheric (right). COH measures increased during hand movement conditions, especially in the 9-12 Hz range, and were most apparent from prefrontal, premotor and motor areas. Parietal sources were essentially unchanged. Power measures were unchanged for virtually all leads and conditions. Increases in COH were not due to EOG or EMG artifact contamination. Evidence for lateralized increases was equivocal; significant bilateral increases were observed more often regardless of the hand clenched. Implications and suggested areas for future research are discussed.     

Differential Contributions of Blinks and Vertical Eye Movements as Artifacts in EEG Recording

Blinks and vertical eye movements were studied as artifacts of EEG recording. The electro-oculogram (EOG) and vertex vs joined mastoids EEG were recorded in 13 college-aged subjects. Subjects were asked to blink “normally, without excessive effort,” and move their eyes through vertical visual arcs of 5°, 10°, 20°, 30°, and 60°. The ratio EEG/EOG, the fraction of the EOG potential transmitting to the scalp EEG electrode as artifact, was calculated for potentials generated during both blinks and eye movement. Vertical eye movement scalp EEG artifact was a constant percentage of the vertical eye movement EOG across visual arcs of 10° to 60°. Mean percentage eye blink EEG artifact (9.3%) was significantly (p < .001) less than the mean percentage vertical eye movement artifact (13.9%). Thus, blink and vertical eye movement artifact fields are quantitatively different in terms of their transmission to the scalp (C_z) EEG electrode. Subtraction of a single subject specific percentage of the EOG from the EEG would correct for either artifact source, but different subtraction percentages must be used for each.     

Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.     

Detection of artifacts from high energy bursts in neonatal EEG

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.     

Is the Contralateral Delay Activity (CDA) a robust neural correlate for Visual Working Memory (VWM) tasks? A reproducibility study

Visual working memory (VWM) allows us to actively store, update, and manipulate visual information surrounding us. While the underlying neural mechanisms of VWM remain unclear, contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered, is often used to study VWM. To investigate if the CDA is a robust neural correlate for VWM tasks, we reproduced eight CDA-related studies with a publicly accessible EEG data set. We used the raw EEG data from these eight studies and analyzed all of them with the same basic pipeline to extract CDA. We were able to reproduce the results from all the studies and show that with a basic automated EEG pipeline we can extract a clear CDA signal. We share insights from the trends observed across the studies and raise some questions about the CDA decay and the CDA during the recall phase, which surprisingly, none of the eight studies did address. Finally, we also provide reproducibility recommendations based on our experience and challenges in reproducing these studies.     

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.     

Removal of ocular artifacts from electro-encephalogram by adaptive filtering

The electro-encephalogram (EEG) is useful for clinical diagnosts and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and timeconsuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M=3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive leastsquares algorithm that includes a forgetting factor (λ=0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M=3) were significantly larger than any remaining coefficients.     

Effect of signal length on the performance of independent component analysis when extracting the lambda wave

The aim of the study was to investigate the effect of signal length on the performance of a signal source separation method, independent component analysis (ICA), when extracting the visual evoked potential (EP) lambda wave from saccade-related electro-encephalogram (EEG) waveforms. A method was devised that enabled the effective length of the recorded EEG traces to be increased prior to processing by ICA. This involved abutting EEG traces from an appropriate number of successive trials (a trial was a set of waveforms recorded from 64 electrode locations in a study investigating saccade performance). ICA was applied to the saccade-related EEG and electro-oculogram (EOG) waveforms recorded from the electrode locations. One spatial and five temporal features of the lambda wave were monitored to assess the performance of ICA applied to both abutted and non-abutted waveforms. ICA applied to abutted trials managed to extract all six features across all seven subjects included in the study. This was not the case when ICA was applied to the non-abutted trials. It was quantitatively demonstrated that the process of abutting EEG waveforms was useful for ICA preprocessing when extracting lambda waves.     

时窗复杂度序列在睡眠脑电分期中的应用

运用时窗复杂度序列来分析睡眠脑电，减少了非平稳性及状态空间的不均匀性造成的脑状态信息的丢失，在一定程度上克服了复杂度自身的局限，有助于不同睡眠期状态特征的提取。另外采用独立分量分析（ICA），小波变换等方法对脑电进行预处理，实验表明它们能有效地去除脑电中的一些生理干扰，有利于提高复杂度算法在睡眠分期应用中的精确度。     

Classification of independent components of EEG into multiple artifact classes

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them.     

A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal

Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG–fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.     

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.