Objective EEG topographies could be distorted by electrode bridges due to

Objective EEG topographies could be distorted by electrode bridges due to electrolyte growing between adjacent electrodes typically. determine electrode bridges in datasets with different montages (22-64 stations) and sound properties. Outcomes The degree of bridging varied substantially across datasets: 54% of EEG recording sessions contained an electrode bridge and the mean percentage of bridged electrodes in a montage was as high as 18% in one of the datasets. Furthermore over 40% of the recording channels were bridged in 9 of 203 sessions. These findings were independently validated by visual inspection. Conclusions The new algorithm conveniently efficiently and reliably identified electrode bridges across different datasets and recording conditions. Electrode bridging may constitute a substantial problem for some datasets. Significance Given the extent of the electrode bridging across datasets this problem may be more common than generally thought. However when used as an automatic screening routine the new algorithm will TH 237A prevent pitfalls stemming from unrecognized TH 237A electrode bridges. of channels and (temporal variance; Tenke and Kayser 2001 defined as:


The ED measure was originally integrated in NeuroScan 3.0 (Neuroscan Inc. 1993 1995 as a power analogue of length for the Hjorth Laplacian montage (“intrinsic Hjorth”; cf. footnote 2 in Kayser and Tenke 2001 yielding topographies Gfap sharpened more than electrical instead of spatial length. The intrinsic Hjorth recognizes electric bridges in averaged waveforms as level (i.e. near-zero ED) waveforms for bridged stations (cf. Fig. 1 in Tenke and Kayser 2001 This process has been effectively applied in various clinical tests (e.g. Greischar et al. 2004 Tenke et al. 2008 Knight et al. 2010 Hamm et al. TH 237A 2012 Kayser et al. 2012 and industrial software items (e.g. Electrical Geodesics Inc. 2003 to recognize and address complications stemming TH 237A from bridged documenting stations1. EEG Data Pieces of EEG recordings had been attained via an Search on the internet limited to those openly designed for immediate download or upon demand. Datasets had been utilized if: 1) these were connected with a publication; 2) the EEG montage contains at least 20 stations; and 3) at least 15 periods had been available. A program was thought as an individual TH 237A time frame where an electrode cover was used and EEG data had been obtained for at least 50 secs. Five datasets a few of which were found in several publication fulfilled these selection requirements and had been accordingly utilized for this study (BCI2000; TH 237A Goldberger et al. 2000 Delorme et al. 2002 Makeig and Delorme 2004 Delorme et al. 2004 Schalk et al. 2004 Naeem et al. 2006 Savran et al. 2006 Koelstra et al. 2012 These datasets had been randomly labeled A-E with a subscript to indicate the number of channels in the montage (Table 1). Three sessions were excluded from your analyses due to smooth EEG amplifier saturation or non-physiological artifacts. Units A32 (100 Hz low-pass 50 Hz notch) B64 (0.1 Hz high-pass 60 Hz low-pass) and C22 (0.5-50 Hz band-pass 50 Hz notch) had already been filtered at the acquisition sites. Data were acquired using a variety of reference techniques including acquisition- or system-specific recommendations but all sessions were re-referenced to vertex (Cz) during preprocessing. However it should be noted that EDs are computed from pairwise difference waveforms and that any approach that utilizes EDs is usually therefore inherently reference-free and unaffected by the choice of EEG reference. Table 1 Core characteristics of EEG data included in current study. Preprocessing All data from a given session were imported into MATLAB R2010a (The MathWorks Inc. 2010 with Transmission Processing Toolbox v6.13) using EEGLAB.