مقاله انگلیسی رایگان در مورد مشخص کردن سیگنال های الکتروانسفالوگرافی در فیلم ها – الزویر 2018

Currently, there is still not enough understanding of how the human brain perceives the information input from the outside world and how neurons react correspondingly in a conscious and/or unconscious manner of perception. Non-invasive brain activity recording technologies, like electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have been widely introduced to record human brain dynamics under certain circumstances. EEG measures brain activities over time with a high temporal resolution of milliseconds, while fMRI mainly identifies which area of the brain is in use with a high spatial resolution of millimeters. There is a convergent evidence that suggests that brain activity recordings play a vital role to boost the development of new biometric technologies and precede studies of brain functions like attention and memory (Han et al., 2015), motor control (Heimann, Umiltà, Guerra, & Gallese, 2014), and emotions (Alarcao & Fonseca, 2017). In the fields of brain encoding and decoding, there has been a number of studies over the past decades addressing one of the basic questions of how information is represented in the brain (Naselaris, Kay, Nishimoto, & Gallant, 2011). As a consequence, the connections between brain activities in the visual cortex and lowlevel visual features such as orientation (Haynes & Rees, 2005), color (Brouwer & Heeger, 2009), and position (Thirion et al., 2006) have been intensively studied. To measure the brain responses while watching natural images, Kay, Naselaris, Prenger, and Gallant (2008) proposed an fMRI-based decoding system to apply a receptive field-based model to represent individual fMRI voxels. They modeled a generation process of fMRI signals in particular in visual areas V1, V2 and V3, and further tested the performance in an application of image identification. A high identification accuracy was obtained from two participants, suggesting the feasibility to predict novel natural images by using the proposed general visual decoder. Following this previous study, Naselaris, Prenger, Kay, Oliver, and Gallant (2009) proposed a Bayesian framework to model fMRI signals, and were successful in reconstructing natural images based on fMRI. According to their method, individual voxels were modeled in two different approaches, namely the Gabor wavelet-based structural encoding model and semantic-based encoding model, thus characterizing the fMRI responses in the early visual areas and anterior visual areas, respectively. Instead of using static images as visual stimuli, Nishimoto et al. (2011) presented a motion energy-based encoding model to represent the fMRI signal patterns in the early visual areas while watching natural movies, and demonstrated the validity and temporal specificity of this encoding model. Furthermore, they applied a Bayesian decoder to test the reconstruction accuracy of using brain activity measurements to reconstruct the dynamic visual information in movies. Similarly, Han, Zhao, Hu, Guo, and Liu (2014) introduced an fMRI-based encoding model to predict the brain network response while the participants were free-viewing video clips. They pointed out that a successful brain encoding technique could benefit the evaluation and guidance of visual feature extraction in applications of visual attention and image processing, and could also boost the development of cognitive neuroscience studies.