Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena

By Christoph S. Herrmann. Experimental Brain Research, 2001

1,138 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/991ee6dae5f42909d027114ac388371c0ee9735c

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Natural Frequencies of Human Corticothalamic Circuits (2009, background): “…the existence of clear-cut resonance frequencies in specific parts of the human corticothalamic system, 10 Hz (Narici and Romani, 1989; Regan, 1989; Silberstein, 1995; Rager and Singer, 1998; Herrmann, 2001) in visual cortex and 40 Hz in auditory cortex (Galambos et al., 1981; Pastor et al., 2002).”
Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces (2007, background): “The strongest response is obtained for stimulation frequencies in the range 5–20 Hz [2], [3].”
Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. (2010, methodology): “This model might be extendable up to at least 100 Hz (Herrmann, 2001); there might be also more components yet to be discovered at higher frequencies.”
High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations (2013, background): “While fast oscillations were originally thought to occur only in a small proportion of patients ( 5%), several studies report detection rates of focal epilepsy that are significantly higher (Andrade-Valenca et al., 2011).”
A Survey of Stimulation Methods Used in SSVEP-Based BCIs (2010, neutral): “SSVEPs can be elicited by repetitive visual stimuli at frequencies in the 1 to 100 Hz range [4].”
Visual and Auditory Brain–Computer Interfaces (2014, neutral): “and subharmonic to the stimulus frequency [111].”
Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components (2005, background): “The SSVEPs have the same fundamental frequency (ﬁrst harmonic) as the stimulating frequency, but usually they also include higher (Regan 1989) and / or sub-harmonics (Herrmann 2001).”
A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces (2017, background): “3 illustrates the amplitude spectrum and SNR images as functions of stimulation frequency and response frequency [25].”
P300 brain computer interface: current challenges and emerging trends (2012, background): “Both MI projects used the BCI system for the activation of the sensorimotor cortex for stroke rehabilitation, which is not possible with N200/P300-or SSVEP-based BCI systems.”
The steady-state visual evoked potential in vision research: A review. (2015, background): “, 2002; see also Regan & Regan, 1988), while SSVEP components at fundamental frequencies up to 100 Hz have been reported (Herrmann, 2001).”
A practical VEP-based brain-computer interface. (2006, background): “According to research done in the area of human brain electrophysiology, the amplitude of the SSVEP varies in a complex manner with the frequency of stimulation [12], [14]–[16].”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, methodology): “More speci ﬁ cally, SSVEPs consist of brain responses at frequencies identical, harmonic, and sub-har-monic to the stimulation frequency (Herrmann 2001).”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, neutral): “Herrmann [118] investigated the correlation between frequency of stimulus presentation and the firing rates of neurons.”
Human EEG gamma oscillations in neuropsychiatric disorders. (2005, background): “…within different contexts and with different temporal dynamics and relations to sensory and cognitive events, there is evidence that spontaneous, steady-state, evoked and induced gamma oscillations might be generated by the same neural circuits (Basar, 1980; Herrmann, 2001; Pantev et al., 1991).”
Rhythmic visual stimulation as a window into early brain development: A systematic review (2023, methodology): “While research in adults suggests that different frequencies elicit different network dynamics (Herrmann, 2001) and that targeting specific frequencies, such as the theta rhythm, can lead to differential behavioral outcomes (Hanslmayr et al., 2019), research into this direction is still scarce.”
Stimulus-evoked and resting-state alpha oscillations show a linked dependence on patterned visual experience for development (2023, background): “Second, visual stimulation in the alpha range caused greater entrainment (Thut et al., 2011) of neural oscillations than at any other frequencies (Başar et al., 1997; Herrmann, 2001).”