A fully automated correction method of EOG artifacts in EEG recordings

Schlögl, Alois; Keinrath, Claudia; Zimmermann, Doris; Scherer, Reinhold; Leeb, Robert; Pfurtscheller, Gert

doi:10.1016/j.clinph.2006.09.003

Cited by 515 publications

(348 citation statements)

References 14 publications

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“…In order to monitor fixation, we recorded the electrooculogram (EOG) using the setup of Schlögl and colleagues (Schlögl et al 2007). EOG artifacts were identified in a semiautomatic procedure using the EOG artifact detection function of the Fieldtrip toolbox (Oostenveld et al 2011).…”

Section: Global Motion Experimentsmentioning

confidence: 99%

Right Temporoparietal Gray Matter Predicts Accuracy of Social Perception in the Autism Spectrum

David

Schultz

Milne

et al. 2013

J Autism Dev Disord

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Individuals with an autism spectrum disorder (ASD) show hallmark deficits in social perception. These difficulties might also reflect fundamental deficits in integrating visual signals. We contrasted predictions of a social perception and a spatialndash;temporal integration deficit account. Participants with ASD and matched controls performed two tasks: the first required spatiotemporal integration of global motion signals without social meaning, the second required processing of socially relevant local motion. The ASD group only showed differences to controls in social motion evaluation. In addition, gray matter volume in the temporalndash;parietal junction correlated positively with accuracy in social motion perception in the ASD group. Our findings suggest that socialndash;perceptual difficulties in ASD cannot be reduced to deficits in spatialndash;temporal integration

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Section: Global Motion Experimentsmentioning

confidence: 99%

Right Temporoparietal Gray Matter Predicts Accuracy of Social Perception in the Autism Spectrum

David

Schultz

Milne

et al. 2013

J Autism Dev Disord

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“…Off-line analysis was performed using EMEGS (freely available at http://www.emegs.org/) (Peyk et al, 2011) and included filtering (0.1 Hz high-pass and 40 Hz low-pass), removal of eye movement artifacts (Schlögl et al, 2007), artifact detection and sensor interpolation, and average reference. A baseline correction based on the 100 ms before stimulus onset was performed.…”

Section: Methodsmentioning

confidence: 99%

Scene Identification and Emotional Response: Which Spatial Frequencies Are Critical?

Cesarei

Codispoti²

2011

J. Neurosci.

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Emotional responses are regulated by basic motivational systems (appetitive and defensive), which allow for adaptive behavior when opportunities and threats are detected. It has been suggested that specific ranges of spatial frequencies of the visual input may contain information that is diagnostic for discriminating emotionally relevant from less relevant contents, and that specialized neural modules may analyze these spatial frequencies to allow efficient detection and response to life-threatening or life-sustaining stimuli. However, there is no evidence supporting this possibility regarding natural scenes, which are highly varied in terms of pictorial composition. The present study examines how low and high spatial frequency filtering affects the understanding of natural scene contents and modulates the amplitude of the late positive potential (LPP), a well-known component of the event-related potentials that reflects motivational significance. The content of an initially degraded (low-or high-passed) picture was progressively revealed in a sequence of steps by adding high or low spatial frequencies. At each step, human participants reported whether they identified the gist of the image. The results showed that the affective modulation of the LPP varied with picture identification similarly for low-passed and high-passed pictures. The engagement of corticolimbic appetitive and defensive systems, reflected in the LPP affective modulation, varied with picture identification, and did not critically or preferentially depend on either low or high spatial frequencies.

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“…EEG and EOG signals were digitized with a sampling frequency of 256 Hz, powerline notch filtered at 50 Hz and high-pass filtered at 0.5 Hz. Additionally, the horizontal, vertical, and radial EOG were computed to remove the EOG from the EEG using a regression algorithm [16]. The data acquisition and on-line processing was developed under a self-made BCI platform.…”

Section: A Data Recordingmentioning

confidence: 99%

Brain connectivity in continuous error tasks

Omedes

Iturrate

Montesano

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Error-related potentials (ErrP) have been recently incorporated in brain-machine interfaces (BMIs) due to its ability to adapt and correct both the output of the BMI or the behavior of the machine. Most of these applications rely on synchronous tasks with different user's evaluations associated to correct and wrong events. Asynchronous detection during the continuous evaluation of the task, however, has to cope with background noise and an increased number of misdetections common in event-related potential detection. This paper studies a different characteristic that may carry additional information to be exploited by asynchronous ErrP detectors: brain connectivity coherence patterns appearing while the user monitors the continuous operation of a device. The results obtained with five subject revealed the presence of an error potential in an asynchronous reaching task an showed an increase in the coherency within the theta band.

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A fully automated correction method of EOG artifacts in EEG recordings

Cited by 515 publications

References 14 publications

Right Temporoparietal Gray Matter Predicts Accuracy of Social Perception in the Autism Spectrum

Right Temporoparietal Gray Matter Predicts Accuracy of Social Perception in the Autism Spectrum

Scene Identification and Emotional Response: Which Spatial Frequencies Are Critical?

Brain connectivity in continuous error tasks

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