Functionally independent components of early event-related potentials in a visual spatial attention task.

Spatial visual attention modulates the first negative-going deflection in the human averaged event-related potential (ERP) in response to visual target and non-target stimuli (the N1 complex). Here we demonstrate a decomposition of N1 into functionally independent subcomponents with functionally distinct relations to task and stimulus conditions. ERPs were collected from 20 subjects in response to visual target and non-target stimuli presented at five attended and non-attended screen locations. Independent component analysis, a new method for blind source separation, was trained simultaneously on 500 ms grand average responses from all 25 stimulus-attention conditions and decomposed the non-target N1 complexes into five spatially fixed, temporally independent and physiologically plausible components. Activity of an early, laterally symmetrical component pair (N1aR and N1aL) was evoked by the left and right visual field stimuli, respectively. Component N1aR peaked ca. 9 ms earlier than N1aL. Central stimuli evoked both components with the same peak latency difference, producing a bilateral scalp distribution. The amplitudes of these components were no reliably augmented by spatial attention. Stimuli in the right visual field evoked activity in a spatio-temporally overlapping bilateral component (N1b) that peaked at ca. 180 ms and was strongly enhanced by attention. Stimuli presented at unattended locations evoked a fourth component (P2a) peaking near 240 ms. A fifth component (P3f) was evoked only by targets presented in either visual field. The distinct response patterns of these components across the array of stimulus and attention conditions suggest that they reflect activity in functionally independent brain systems involved in processing attended and unattended visuospatial events.

[1]  T. Picton,et al.  The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. , 1987, Psychophysiology.

[2]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[3]  M. Rader,et al.  A computer model , 1996, IEEE Conference Record - Abstracts. 1996 IEEE International Conference on Plasma Science.

[4]  J. Nadal.  Non linear neurons in the low noise limit : a factorial code maximizes information transferJean , 1994 .

[5]  M. Jeeves,et al.  Directional asymmetries in interhemispheric transmission time: Evidence from visual evoked potentials , 1994, Neuropsychologia.

[6]  J. Nadal,et al.  Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .

[7]  M. Corbetta,et al.  A PET study of visuospatial attention , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  Eric Courchesne,et al.  Recent advances in autism , 1990 .

[9]  S. Makeig.  Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. , 1993, Electroencephalography and clinical neurophysiology.

[10]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[11]  S. Hillyard,et al.  Event-related brain potentials in the study of visual selective attention. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[12]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .

[13]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[14]  C Miniussi,et al.  Interhemispheric transfer of visual information in humans: the role of different callosal channels. , 1997, Archives italiennes de biologie.

[15]  M. Mesulam.  A cortical network for directed attention and unilateral neglect , 1981, Annals of neurology.

[16]  Tzyy-Ping Jung,et al.  Extended ICA Removes Artifacts from Electroencephalographic Recordings , 1997, NIPS.

[17]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[18]  R. Davidson,et al.  Visual evoked potential measures of interhemispheric transfer time in humans. , 1989, Behavioral neuroscience.

[19]  Helen J. Neville,et al.  Attention to central and peripheral visual space in a movement detection task: an event-related potential and behavioral study. I. Normal hearing adults , 1987, Brain Research.

[20]  C. Gilbert.  Adult cortical dynamics. , 1998, Physiological reviews.

[21]  S. Hillyard,et al.  Selective attention to the color and direction of moving stimuli: Electrophysiological correlates of hierarchical feature selection , 1996, Perception & psychophysics.

[22]  L. Kempe.  Handbook of Physiology. Section I. The Nervous System , 1982 .

[23]  Tzyy-Ping Jung,et al.  Analyzing and Visualizing Single-Trial Event-Related Potentials , 1998, NIPS.

[24]  S. Amari.  Natural Gradient Works Eciently in Learning , 2022 .

[25]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[26]  T. Sejnowski,et al.  Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention , 1999, The Journal of Neuroscience.

[27]  E. Donchin,et al.  A multivariate approach to the analysis of average evoked potentials. , 1966, IEEE transactions on bio-medical engineering.

[28]  Eric Courchesne,et al.  Parietal Damage and Narrow Spotlight Spatial Attention , 1994, Journal of Cognitive Neuroscience.

[29]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Ralph Linsker,et al.  Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network , 1992, Neural Computation.

[31]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources , 1999, Neural Comput..

[32]  E. Wascher,et al.  Shifting attention between global features and small details: an event-related potential study , 1997, Biological Psychology.

[33]  S. Hillyard,et al.  Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming. , 1991, Journal of experimental psychology. Human perception and performance.

[34]  M. Valdés-Sosa,et al.  Switching Attention without Shifting the Spotlight: Object-Based Attentional Modulation of Brain Potentials , 1998, Journal of Cognitive Neuroscience.

[35]  T J Sejnowski,et al.  Learning the higher-order structure of a natural sound. , 1996, Network.

[36]  S. Hillyard,et al.  Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming. , 1991, Journal of experimental psychology. Human perception and performance.

[37]  M. Corbetta,et al.  A Common Network of Functional Areas for Attention and Eye Movements , 1998, Neuron.

[38]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.