Electro-Encephalography: II. Visual Stimulation and the After-Image as Affecting the Occipital Alpha Rhythm

1937 ◽  
Vol 17 (1) ◽  
pp. 29-48 ◽  
Author(s):  
Herbert H. Jasper ◽  
Ruth M. Cruikshank
Keyword(s):  
Alpha Rhythm ◽  
Visual Stimulation

scholarly journals No changes in parieto-occipital alpha during neural phase locking to visual quasi-periodic theta-, alpha-, and beta-band stimulation

2017 ◽  
Author(s):  
Christian Keitel ◽  
Christopher SY Benwell ◽  
Gregor Thut ◽  
Joachim Gross
Keyword(s):  
Alpha Rhythm ◽  
Visual Stimulation ◽  
Phase Locking ◽  
Stimulation Frequency ◽  
Alpha Band ◽  
Human Visual Perception ◽  
Beta Band ◽  
Frequency Bands ◽  
Low Intensity ◽  
Increased Sensitivity

ABSTRACTRecent studies have probed the role of the parieto-occipital alpha rhythm (8 – 12 Hz) in human visual perception through attempts to drive its neural generators. To that end, paradigms have used high-intensity strictly-periodic visual stimulation that created strong predictions about future stimulus occurrences and repeatedly demonstrated perceptual consequences in line with an entrainment of parieto-occipital alpha. Our study, in turn, examined the case of alpha entrainment by non-predictive low-intensity quasi-periodic visual stimulation within theta-(4 – 7 Hz), alpha-(8 – 13 Hz) and beta (14 – 20 Hz) frequency bands, i.e. a class of stimuli that resemble the temporal characteristics of naturally occurring visual input more closely. We have previously reported substantial neural phase-locking in EEG recording during all three stimulation conditions. Here, we studied to what extent this phase-locking reflected an entrainment of intrinsic alpha rhythms in the same dataset. Specifically, we tested whether quasi-periodic visual stimulation affected several properties of parieto-occipital alpha generators. Speaking against an entrainment of intrinsic alpha rhythms by non-predictive low-intensity quasi-periodic visual stimulation, we found none of these properties to show differences between stimulation frequency bands. In particular, alpha band generators did not show increased sensitivity to alpha band stimulation and Bayesian inference corroborated evidence against an influence of stimulation frequency. Our results set boundary conditions for when and how to expect effects of entrainment of alpha generators and suggest that the parieto-occipital alpha rhythm may be more inert to external influences than previously thought.

Factors Influencing Electrophysiologic Responsivity in Normal Adults

1965 ◽  
Vol 8 (4) ◽  
pp. 323-347
Author(s):  
Robert Goldstein ◽  
Benjamin RosenblÜt
Keyword(s):  
Conditioned Stimulus ◽  
White Light ◽  
Pure Tone ◽  
Alpha Rhythm ◽  
White Women ◽  
White Men ◽  
Inverse Relation ◽  
Factors Influencing ◽  
Electrodermal Responses ◽  
Normal Adults

Electrodermal and electroencephalic responsivity to sound and to light was studied in 96 normal-hearing adults in three separate sessions. The subjects were subdivided into equal groups of white men, white women, colored men, and colored women. A 1 000 cps pure tone was the conditioned stimulus in two sessions and white light was used in a third session. Heat was the unconditioned stimulus in all sessions. Previously, an inverse relation had been found in white men between the prominence of alpha rhythm in the EEG and the ease with which electrodermal responses could be elicited. This relation did not hold true for white women. The main purpose of the present study was to answer the following questions: (1) are the previous findings on white subjects applicable to colored subjects? (2) are subjects who are most (or least) responsive electrophysiologically on one day equally responsive (or unresponsive) on another day? and (3) are subjects who are most (or least) responsive to sound equally responsive (or unresponsive) to light? In general, each question was answered affirmatively. Other factors influencing responsivity were also studied.

Age development of cortical rhythmics in the EEG depending on the formation of the child’s personal psychical characteristics

2019 ◽  
Vol 1 (8) ◽  
pp. 42-50
Author(s):  
A. V. Budkevich ◽  
L. B. Ivanov ◽  
G. R. Novikova ◽  
G. M. Dzhanumova
Keyword(s):  
Alpha Rhythm ◽  
Age Groups ◽  
Healthy Children ◽  
Brain Organization ◽  
Age Related ◽  
Mathematical Processing ◽  
Functional Brain Organization ◽  
Child's Brain ◽  
Attention Function ◽  
Apparently Healthy

According to the authors, rationing the age-related EEG parameters in children should be based on personal psychical characteristics. A comparative analysis of personal psychical characteristics and electroencephalographic data was carried out in 300 apparently healthy children aged 3-15 years. According to this principle, two subgroups of conditionally healthy children in each age group were singled out: 1) with an immature attention function and 2) with an increased anxious background that do not reach the pathological level. Registration and analysis of EEG was performed by the Neurokariograf computer complex (MBN, Moscow) using mathematical processing methods.The EEG interpretation was based on the principle of assessing the functional state of a child's brain using a three-component model according to: 1) wakefulness level and its dissociation, 2) severity of signs of the EEG neurotic pattern, 3) directionality of formation of traits of the system-functional brain organization (severity of signs functional hypofrontality).lt was found the presence of EEG signs was indicative of a lower level of wakefulness in children with an immature function of attention in all age groups, compared with the indicators of the average population of group and children with an increased background of anxiety. Children with an increased background of anxiety have a tendency to prevalence and excessive spatial synchronization of the alpha rhythm. ln healthy children, the fact of a decrease in wakefulness and the presence of signs of anxiety in the clinic and in EEG patterns indicates individual personalities and should not be considered as pathology.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

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