Quantitative EEG Signatures through Amplitude and Phase Modulation Patterns

Cortical spatiotemporal signal patterns based on object recognition can be discerned from visual stimulation. These are in the form of amplitude modulation (AM) and phase modulation (PM) patterns, which contain perceptual information gathered from sensory input. A high-density Electroencephalograph (EEG) device consisting of 48 electrodes with a spacing of 5 mm was utilized to measure frontal lobe activity in order to capture event-related potentials from visual stimuli. Four randomized stimuli representing different levels of salient responsiveness were measured to determine if mild stimuli can be discerned from more extreme stimuli. AM/PM response patterns were detected between mild and more salient stimuli across participants. AM patterns presented distinct signatures for each stimulus. AM patterns had the highest number of incidents detected in the middle of the frontal lobe. Through this work, we can expand our encyclopedia of neural signatures to object recognition, and provide a broader understanding of quantitative neural responses to external stimuli. The results provide a quantitative approach utilizing spatiotemporal patterns to analyze where distinct AM patterns can be linked to object perception.

[1]  G. Edelman Neural Darwinism: The Theory Of Neuronal Group Selection , 1989 .

[2]  Robert Kozma,et al.  Spatio-temporal EEG pattern extraction using high-density scalp arrays , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[3]  J. Martinerie,et al.  Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony , 2001, Journal of Neuroscience Methods.

[4]  W. Freeman,et al.  Aperiodic phase re‐setting in scalp EEG of beta–gamma oscillations by state transitions at alpha–theta rates , 2003, Human brain mapping.

[5]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Karl J. Friston,et al.  Neural responses to salient visual stimuli , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[7]  R. Maddock The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain , 1999, Trends in Neurosciences.

[8]  E. Miller,et al.  Memory fields of neurons in the primate prefrontal cortex. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Neural Darwinism. The Theory of Neuronal Group Selection. Gerald M. Edelman. Basic Books, New York, 1987. xxii, 371 pp., illus. $29.95. , 1988, Science.

[10]  Walter J. Freeman,et al.  Origin, structure, and role of background EEG activity. Part 4: Neural frame simulation , 2006, Clinical Neurophysiology.

[11]  W. Freeman,et al.  Spatial patterns of visual cortical fast EEG during conditioned reflex in a rhesus monkey , 1987, Brain Research.

[12]  Tzyy-Ping Jung,et al.  Biosensor Technologies for Augmented Brain–Computer Interfaces in the Next Decades , 2012, Proceedings of the IEEE.

[13]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[14]  J. Allman,et al.  Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. , 1985, Annual review of neuroscience.

[15]  M. Steriade The Electroencephalogram: Its Patterns and Origins by John S. Barlow, MIT Press, 1993. $95.00 (456 pages) ISBN 0 262023547 , 1994, Trends in Neurosciences.

[16]  Ziad M Hafed,et al.  How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[17]  R. Lane,et al.  Neural Correlates of Levels of Emotional Awareness: Evidence of an Interaction between Emotion and Attention in the Anterior Cingulate Cortex , 1998, Journal of Cognitive Neuroscience.

[18]  Walter J. Freeman,et al.  Origin, structure, and role of background EEG activity. Part 3. Neural frame classification , 2005, Clinical Neurophysiology.

[19]  W. Freeman,et al.  Spatiotemporal analysis of prepyriform, visual, auditory, and somesthetic surface EEGs in trained rabbits. , 1996, Journal of neurophysiology.

[20]  Walter J. Freeman,et al.  A Neurobiological Theory of Meaning in Perception Part V: Multicortical Patterns of Phase Modulation in Gamma EEG , 2003, Int. J. Bifurc. Chaos.

[21]  Earl K. Miller,et al.  Selective representation of relevant information by neurons in the primate prefrontal cortex , 1998, Nature.

[22]  W. Freeman,et al.  Change in pattern of ongoing cortical activity with auditory category learning , 2001, Nature.

[23]  Gilles Pourtois,et al.  Motivational Salience Modulates Early Visual Cortex Responses across Task Sets* , 2017, Journal of Cognitive Neuroscience.

[24]  T. Greenberg,et al.  Glucocorticoid Administration Improves Aberrant Fear-Processing Networks in Spider Phobia , 2017, Neuropsychopharmacology.

[25]  W. Freeman,et al.  Analysis of spatial patterns of phase in neocortical gamma EEGs in rabbit. , 2000, Journal of neurophysiology.

[26]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[27]  Cooper A. Smout,et al.  Spatial Attention Enhances the Neural Representation of Invisible Signals Embedded in Noise , 2018, Journal of Cognitive Neuroscience.

[28]  W. Freeman Origin, structure, and role of background EEG activity. Part 2. Analytic phase , 2004, Clinical Neurophysiology.

[29]  Bruno A Olshausen,et al.  Timecourse of neural signatures of object recognition. , 2003, Journal of vision.

[30]  Walter J. Freeman,et al.  Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude , 2004, Clinical Neurophysiology.