Decoding of visual information from human brain activity: A review of fMRI and EEG studies.

Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.

[1]  Srivas Chennu,et al.  Bedside detection of awareness in the vegetative state: a cohort study , 2011, The Lancet.

[2]  Mark H. Johnson.  Functional brain development in humans , 2001, Nature Reviews Neuroscience.

[3]  O. D. L. Nehmad.  The end in sight: a look at the occipital lobe , 1998 .

[4]  Leslie G. Ungerleider,et al.  Visual Imagery of Famous Faces: Effects of Memory and Attention Revealed by fMRI , 2002, NeuroImage.

[5]  Leslie G. Ungerleider,et al.  Distributed Neural Systems for the Generation of Visual Images , 2000, Neuron.

[6]  N. Kanwisher,et al.  Mental Imagery of Faces and Places Activates Corresponding Stimulus-Specific Brain Regions , 2000, Journal of Cognitive Neuroscience.

[7]  Alex R. Wade,et al.  Visual areas and spatial summation in human visual cortex , 2001, Vision Research.

[8]  S. Edelman,et al.  Human Brain Mapping 6:316–328(1998) � A Sequence of Object-Processing Stages Revealed by fMRI in the Human Occipital Lobe , 2022 .

[9]  Mohammad Reza Daliri,et al.  Decoding Objects of Basic Categories from Electroencephalographic Signals Using Wavelet Transform and Support Vector Machines , 2014, Brain Topography.

[10]  G B Stanley,et al.  Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus , 1999, The Journal of Neuroscience.

[11]  S. Kosslyn,et al.  Topographical representations of mental images in primary visual cortex , 1995, Nature.

[12]  Tom Michael Mitchell,et al.  Predicting Human Brain Activity Associated with the Meanings of Nouns , 2008, Science.

[13]  John J. Foxe,et al.  Attention-dependent suppression of distracter visual input can be cross-modally cued as indexed by anticipatory parieto-occipital alpha-band oscillations. , 2001, Brain research. Cognitive brain research.

[14]  Stephen José Hanson,et al.  Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? , 2004, NeuroImage.

[15]  Marcel Bastiaansen,et al.  Oscillatory brain dynamics associated with the automatic processing of emotion in words , 2014, Brain and Language.

[16]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[17]  Keiji Tanaka,et al.  Object category structure in response patterns of neuronal population in monkey inferior temporal cortex. , 2007, Journal of neurophysiology.

[18]  J. Duncan,et al.  Top-Down Activation of Shape-Specific Population Codes in Visual Cortex during Mental Imagery , 2009, The Journal of Neuroscience.

[19]  David D. Cox,et al.  Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.

[20]  G. Glover,et al.  Retinotopic organization in human visual cortex and the spatial precision of functional MRI. , 1997, Cerebral cortex.

[21]  S. Kosslyn,et al.  The role of area 17 in visual imagery: convergent evidence from PET and rTMS. , 1999, Science.

[22]  J. Gallant,et al.  Identifying natural images from human brain activity , 2008, Nature.

[23]  Torkel Klingberg,et al.  Structural Maturation and Brain Activity Predict Future Working Memory Capacity during Childhood Development , 2014, The Journal of Neuroscience.

[24]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[25]  Y Kamitani,et al.  Neural Decoding of Visual Imagery During Sleep , 2013, Science.

[26]  S. Kosslyn,et al.  Neural foundations of imagery , 2001, Nature Reviews Neuroscience.

[27]  Masa-aki Sato,et al.  Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders , 2008, Neuron.

[28]  Karl J. Friston,et al.  Where bottom-up meets top-down: neuronal interactions during perception and imagery. , 2004, Cerebral cortex.

[29]  J. Gallant,et al.  Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies , 2011, Current Biology.

[30]  Yasumasa Okamoto,et al.  Visual imagery while reading concrete and abstract Japanese kanji words: An fMRI study , 2014, Neuroscience Research.

[31]  Tom Heskes,et al.  Linear reconstruction of perceived images from human brain activity , 2013, NeuroImage.

[32]  Thomas Serre,et al.  Reading the mind's eye: Decoding category information during mental imagery , 2010, NeuroImage.

[33]  Ryan J. Prenger,et al.  Bayesian Reconstruction of Natural Images from Human Brain Activity , 2009, Neuron.

[34]  Jean-Baptiste Poline,et al.  Inverse retinotopy: Inferring the visual content of images from brain activation patterns , 2006, NeuroImage.

[35]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.