We will use the terms “artifact” and “noise” interchangeably as the distinction between them is not well defined. In EEG they include slow drifts at the electrode/gel/skin interface ( Huigen et al., 2002, Kappenman and Luck, 2010), and in MEG the large amplitude steps that result from a slip in the flux-lock loop ( Gross et al., 2013), as well as various other glitches of diverse nature. Of particular concern are electrode-specific or sensor-specific sources, because they cannot be suppressed by combining channels linearly as in ICA, beamforming or other linear techniques ( Parra et al., 2005, Debener et al., 2010). The very weak brain signals picked up by electroencephalography (EEG) or magnetoencephalography (MEG) have to compete with multiple sources of noise and artifact within the body, the environment, and the sensors or electrodes. These methods, which are mainly automatic and require little tuning, can greatly improve the quality of the data. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Outlier detection allows the corrupt parts to be identified. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Robust rereferencing reduces the impact of artifacts on the reference. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. Problem with installation of parallel 2.0.4 package in octave 3.0.Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. Re: detrend() in Matlab and Octave, Ben Abbott, 3.Re: detrend() in Matlab and Octave, Søren Hauberg, 3.Re: detrend() in Matlab and Octave, Martin Helm, 3.Re: detrend() in Matlab and Octave, H W Borchers, 3.Re: detrend() in Matlab and Octave, Sergei Steshenko, 2.Re: detrend() in Matlab and Octave, Ben Abbott, 2.detrend() in Matlab and Octave, H W Borchers, 2.Interoperability is an optional feature that can be used if desired. In short, a Matlab clone isn't the objective. Personally enjoy, like operators +=, *= or comment charactersĬonsistent with other scripting languages, #, which allows you to give Octave? There's plenty of Octave-specific syntax, much of which I If it's invalid Matlab syntax, why not have an added feature in > there is a special reason to deviate in this way. > I never said this is good or bad, I was just interested to know whether On 23 December 2010 08:57, H W Borchers wrote:
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