Biofeedback-produced hemispheric asymmetry of slow cortical potentials and its behavioural effects
References (67)
- et al.
EEG and slow cortical potentials in anticipation of mental tasks with different hemispheric involvement
Biol. Psychol.
(1981) - et al.
Spontaneous cortical slow potential shifts and choice reaction time performance
Electroencephalogr. Clin. Neurophysiol.
(1982) - et al.
Asymmetrics in the CNV over left and right hemisphere while subjects await numerical information
Biol. Psychol.
(1974) - et al.
Biofeedback of slow cortical potentials. I
J. Electroencephalogr. Clin. Neurophysiol.
(1980) - et al.
Removal of ocular artifacts from the EEG-A biophysical approach to the EOG
J. Electroencephalogr. Clin. Neurophysiol.
(1985) - et al.
Methodological problems in the investigation of cerebral potentials precedings speech: determining the onset and suppressing the articles caused by speech
Neuropsychologia
(1975) - et al.
EEG investigation of hemispheric asymmetries preceding speech: the R-wave
- et al.
The Bereitschaftspotential preceding the act of speaking. Also an analysis of artifacts
- et al.
Preparation to respond as manifested by movement-related brain potential
Brain Res.
(1980) - et al.
The lateral distribution of event-related potentials during sentence processing
Neuropsychologia
(1982)
Event-related potential asymmetry during the reading of sentences
J. Electroencephalogr. Clin. Neurophysiol.
Biofeedback of slow cortical potentials and its effects on the performance in mental arithmetic tasks
Biol. Psychol.
Asymmetry of brain potentials related to sensorimotor tasks
Int. J. Psychophysiol.
The assessment and analysis of handedness: the Edinburgh inventory
Neuropsychologia
Hirnelektrische Korrelate der cerebralen Musikverarbeitung bein Menschen
Eur. Arch. Psychiat. Neurol. Sci.
Electrophysiological evidence of hemispheric dominance for language, calculation and music
Pfügers Arch. Eur. J. Physiol.
Electrophysiological evidence of right parietal dominance of visual-spatial processing tasks
Regulation of slow brain potentials affects task performance
Imagery affects slow cortical potentials
Int. J. Neurosci.
Slow potentials at the cerebral cortex and behavior
Physiol. Rev.
A simple feedback system for self-control of blood pressure
Percept. Motor Skills
An instrument for producing deep muscle relaxation by means of analog information feedback
J. Appl. Behav. Anal.
Modern mind reading: psychophysiology and cognition
Psychophysiology
Detecting Early Communication: Using measures of Movement-related Potentials to Illuminate Human Information Processing
Effects of binary and proportional feedback on bidirectional control of heart rate
Psychophysiology
Cortical slow-wave and cardiac rate responses in stimulus orientation and reaction time conditions
J. Exp. Psychol.
Biofeedback langsamer kortikaler Potentiale
Patterns of stimulus- and self-induced slow brain potentials — a sign of task specific preparation
Human Neurobiol
The influence of low-level transcortical DC-currents on response speed in humans
Int. J. Neurosci.
Threshold regulation — a key for the understanding of the combined dynamicsof EEG and the event-related potentials
J. Psychophysiol.
Slow Brain Potentials Preceding Task Performance
Response priming and components of the event-related brain potential
Psychophysiology
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Electrophysiological CNS-processes related to associative learning in humans
2016, Behavioural Brain ResearchCitation Excerpt :Also, some trials without feedback from the rocket ship or the target score were placed in between the feedback trials and in these trails, the ability to self-regulate the SCP was maintained in spite of the lacking feedback. Reports on SCP conditioning obtained through feedback have been reviewed [297,343], and include cases in which subjects were trained to increase the negativity of SPC in one hemisphere while suppressing it in the other [344,345]. The possibility that SCP control could be mediated via eye movement artifacts or scalp skin conductance had been excluded in an early work on operantly trained positive or negative SCP shifts [346].
Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI
2013, NeuroImageCitation Excerpt :Intuitively, one might assume that continuous feedback is superior over intermittent feedback, because reinforcement is more directly linked to efforts of the participant, because it provides more opportunities to evaluate training success, and because it keeps the task engagement high. In the field of EEG-based feedback, a few studies indeed reported that a shorter temporal contiguity facilitates learning (Rockstroh et al., 1990; Travis et al., 1974). However, because of the different temporal resolutions of EEG and fMRI, these findings might not be transferable.
Real-time fMRI and its application to neurofeedback
2012, NeuroImageCitation Excerpt :For example, it was shown that lateralized SCP regulation induced lateralized reaction time differences (Rockstroh et al., 1990). At the University of Tübingen, Niels Birbaumer and his lab had done pioneering work and established EEG feedback for various applications in both neuroscience and clinics (Birbaumer et al., 1999; Kotchoubey et al., 2001; Rockstroh et al., 1990). However, he realized that the lack of precise localization and limited coverage of EEG would severely limit the progress in neurofeedback and that fundamental methodological advances were overdue.
Computing moment-to-moment BOLD activation for real-time neurofeedback
2011, NeuroImageCitation Excerpt :Despite widespread interest, neurofeedback training based on fMRI has grown slowly in terms of number of publications, due at least partly to methodological challenges associated with data quality. Existing methods for real-time fMRI either do not compute moment-to-moment changes in activation (Cox et al., 1995; Yoo et al., 1999; Gembris et al., 2000), which is crucial in learning to control brain activation (Rockstroh et al., 1990), or provide a real-time neurofeedback signal (Goebel, 2001; deCharms et al., 2004) computed without accounting for the substantial noise corrupting fMRI data (Friston et al., 1994). Here we present a new method for computing fMRI-based neurofeedback that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to the non-random fMRI noise, resulting in a feedback signal more reflective of neural activation.