in visual decoding

in visual decoding, the fMRI is an effective tool for decoding the brain activity. Most of the studies have implemented the stimulus category classification, dreams, memories and imagination using multivoxel pattern analysis, Cognitive neuroscience is an interdisciplinary study area of psychology and neuroscience. An interesting research field in this domain is building mathematical model on how the psychological activities are correlated to the physiological neural circuitry of human.

EEG is a traditional and non-invasive way of monitoring electrical activity of the brain by following Intl.10-20 system. The EEG signals normally recorded using special EEG sensors EMOTIV wireless Kit, Cognionics wearable EEG Cap, Bio Semi with essential temporal resolutions in terms of data sampling rate (min. 128 – 512 samples/sec) and positioning of minimum 2 to 256 electrodes as the EEG recording channels. Each electrode placement site has a letter to identify the lobe, or area of the brain it is reading from: Pre-frontal (Pf), Frontal (F), Temporal (T), Parietal (P), Occipital (O), and Central (C).

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Considerable efforts have been devoted by the researchers working with EEG data to model the affect domain, Cognitive Neuro-feedback system and solving motor imagery related tasks. Their works mainly focus the human emotion analysis, cognitive / brain disorders, linguistic modeling, etc.

In addition to that, thefunctional magnetic resonance imaging (fMRI) measures the brain activity profile by changing the blood flow in the form of voxels, to reconstruct the perceived stimuli directly from the fMRI activity.

2. Related work

A multiscale local images with predefined shapes were used to reconstruct the lower order information of binary contrast pattern1.The handwritten characters were constructed by straightforward linear Gaussian approach2.In the proposal of reconstruction model, the visual image reconstruction has limited representation power. It acts as a linear observation model for visual image and it’s evaluated by Bayesian canonical correlation analysis (BCCA)3.

To improve the reconstruction accuracy of this process, the posterior regularization is helping to constrain the testing instances and are close to their neighbors from the training set4. A nonlinear extension of the BCAA was formulated by means of a deep generative multi-view model (DGMM)5.The technical innovations of deep neural networks are helping to know about the hierarchical visual processing in computational neuroscience 6.The fMRI activity patterns to the DNN features of viewed images are predicted by the developed decoders 7.Encoding and decoding models are the basic approach for reconstructing the image (low base image or

exemplar image) from the human brain activity. It is not suitable for combined the multiple hierarchical level features even though sophisticated decoding and encoding models. So its need to develop 8.

Instead of hierarchical neural representations of human visual system the DNN visual features are used in reconstructing an image from the human brain activity. In this process fMRI pattern is decoded into DNN features and it also produces the similar output 9.Early visual cortex of lower BOLD signal is the response to faces the dissension view had been already presented than for the novel faces 10.fMRI is used to localize regions in the monkey brain and its produced the stronger response to face compared to other objects, so this region preferred for the electrophysiological analysis 11.

The right ATL and the fusiform gyrus is the set of ventral stream regions identified by the bold response (same face with difference expression) after averaging together. It have the information about individual images of faces12.Investigations of face identification by the functional magnetic resonance imaging it’s a homologous investigation so it’s the main reason for the cortical source of this information attributed to fusiform gyrus. Fusiform base face space visual features are used for facial image reconstruction. And these processes are not considered as a temporal aspect of a face processing 13.