Time Warping Solutions for Classifying Artifacts in EEG

Abstract: Electroencephalographic (EEG)recordings are affected data-segment, butitleads toaconsiderable infor- employed inordertoinvestigate thebrainactivity inneu- mation loss. A verypowerful approach, butstill notcommon ropathological subjects. Unfortunately EEG areoften contam-inclinical practice, wasproposed inliterature someyears inated bytheartifacts, signals thathavenon-cerebral origin iccticaroposinlitratu omeyears andthatmight mimiccognitive or pathologic activity and agic onsinartact signals extraction, detection therefore distort theanalysis ofEEG.Inthis paper weproposeandcancellation. Thisapproach concentrates theartifactual amultiresolution analysis, based onEEGwavelet processing, to content oftheEEGdataset inafewsignals toberejected, so extract thecerebral EEG rhythms. We alsopresent amethod that wedonothavetocancel theentire affected datasegment basedonRenyi's entropy andkurtosis toautomatically identify(1),(2),(3),(4). theWavelet components affected byartifacts. Finally, wediscuss asthejoint useofwavelet analysis, kurtosis andRenyi's entropy Obviously, artifact rejection always involves a lossof allows foradeeper investigation ofthebrainactivity andwe information, eventhough small, anditcauses alittle BEG discuss thecapability ofthis technique tobecomeanefficient distortion. Inaddition, manyartifacts havefixed characteris- preprocessing step tooptimize artifact rejection fromEEG.This tics inthefrequency domainandtheydistort theEEGonly isthefirst technique thatexploits thepeculiarities ofEEG to inaspecific frequency range. Optimize EEG artifact detection. i pcfcfeunyrne

Abstract: Eye-blinks are known to substantially contaminate EEG signals, and thereby severely impact the decoding of EEG signals in various medical and scientific applications. In this work, we consider the problem of eye-blink detection that can then be employed to reliably remove eye-blinks from EEG signals. We propose a fully automated and unsupervised eyeblink detection algorithm, Blink that self-learns user-specific brainwave profiles for eye-blinks. Hence, Blink does away with any user training or manual inspection requirements. Blink functions on a single channel EEG, and is capable of estimating the start and end timestamps of eye-blinks in a precise manner. We collect four different eye-blink datasets and annotate 2300+ eye-blinks to evaluate the robustness performance of Blink across headsets (OpenBCI and Muse), eye-blink types (voluntary and involuntary), and various user activities (watching a video, reading an article, and attending to an external stimulation). The Blink algorithm performs consistently with an accuracy of over 98% for all the tasks with an average precision of 0.934. The source code and annotated datasets are released publicly for reproducibility and further research. To the best of our knowledge, this is the first ever annotated eye-blink EEG dataset released in the public domain.

Abstract: This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimental comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about two-thirds errors, even compared to the best conventional algorithm.

Abstract: Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection algorithms require supervision (e.g., training using canonical artifacts). Many artifact rejection methods are time consuming when applied to high-density EEG data. We describe FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection). Parameters were estimated for various aspects of data (e.g., channel variance) in both the EEG time series and in the independent components of the EEG: outliers were detected and removed. FASTER was tested on both simulated EEG (n=47) and real EEG (n=47) data on 128-, 64-, and 32-scalp electrode arrays. FASTER was compared to supervised artifact detection by experts and to a variant of the Statistical Control for Dense Arrays of Sensors (SCADS) method. FASTER had >90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise. FASTER generally had >60% sensitivity and specificity for detection of contaminated epochs, vs. 0.15% for SCADS. FASTER also aggregates the ERP across subject datasets, and detects outlier datasets. The variance in the ERP baseline, a measure of noise, was significantly lower for FASTER than either the supervised or SCADS methods. ERP amplitude did not differ significantly between FASTER and the supervised approach.

Abstract: 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.

Abstract: Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Rejecting contaminated EEG segments results in a considerable loss of information and may be impractical for clinical data. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings. Often regression in the time or frequency domain is performed on simultaneous EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. However, EOG records also contain brain signals [1, 2], so regressing out EOG activity inevitably involves subtracting a portion of the relevant EEG signal from each recording as well. Regression cannot be used to remove muscle noise or line noise, since these have no reference channels. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records. The method is based on an extended version of a previous Independent Component Analysis (ICA) algorithm [3, 4] for performing blind source separation on linear mixtures of independent source signals with either sub-Gaussian or super-Gaussian distributions. Our results show that ICA can effectively detect, separate and remove activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based methods.

Abstract: Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (< 10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.