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.

High-frequency oscillations in the beta range (10-45 Hz) are most active in motor cortex during motor preparation and are postulated to reflect the steady postural state or global attentive state of the animal. By simultaneously recording multiple local field potential signals across the primary motor and dorsal premotor cortices of monkeys (Macaca mulatta) trained to perform an instructed-delay reaching task, we found that these oscillations propagated as waves across the surface of the motor cortex along dominant spatial axes characteristic of the local circuitry of the motor cortex. Moreover, we found that information about the visual target to be reached was encoded in terms of both latency and amplitude of evoked waves at a time when the field phase-locked with respect to the target onset. These findings suggest that high-frequency oscillations may subserve intra- and inter-cortical information transfer during movement preparation and execution.

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We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals.

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