ERPLAB Toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB’s EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB’s tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user’s guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations.

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ERPLAB: an open-source toolbox for the analysis of event-related potentials

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode/.

/pdf/the-prep-pipeline-standardized-preprocessing-for-large-scale-279mja5gmf.pdf

The PREP pipeline: standardized preprocessing for large-scale EEG analysis

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website.

This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

https://iopscience.iop.org/article/10.1088/1741-2560/12/3/031001/pdf

EEG artifact removal?state-of-the-art and guidelines

A practical guide to the selection of independent components of the electroencephalogram for artifact correction

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 eventrelated potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Descriptors: Electroencephalography, Independent component analysis, EEG artifacts, EEG artefacts, Event-related potentials, Ongoing brain activity, Automatic classification, Thresholding

/pdf/adjust-an-automatic-eeg-artifact-detector-based-on-the-joint-5ga3eb1ee9.pdf

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

Classification-based approaches for data analysis are provoking wide interest and increasing adoption within the neuroscience community. Topics like "brain decoding", "multi-voxel patternanalysis" and "brain-computer interface" are prominent examples of this trend. The core problem of these investigations is hypothesis testing, i.e., finding evidence of some effect produced by the stimulation protocol within neural correlates. A classification algorithm is trained on the recorded data to learn how to discriminate between different stimuli. Then the misclassification rate of the predictions is estimated to answer the statistical test. This generic classification problem can be implemented in several ways depending on the exact neuroscientific question under investigation. However some implementations produce biased estimates due to circular analysis issues that could invalidate the conclusion of the scientific study. Therefore the most suited implementation of the classification problem must be used in order to avoid biases, to detect weak stimulus-related information within noise and to give the proper answer to the neuroscientific question at hand. In this work we propose different implementations of the classification-based approach in the case it comprises a variable selection step together with a classification step. For each different implementation we investigate the associated bias. Analyses are conducted on synthetic data and MEG data from a covert spatial attention task. The effects of different implementations of the classification algorithm are quantified by means of expected misclassification rate. Results prove the importance of adopting a proper error rate estimation process.

Andrea Mognon

Papers

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

Brain Decoding: Biases in Error Estimation