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Full-Text Articles in Neuroscience and Neurobiology
Deep-Learning-Based Multivariate Pattern Analysis (Dmvpa): A Tutorial And A Toolbox, Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashtok Samal, Prahalada K. Rao, Matthew R. Johnson
Deep-Learning-Based Multivariate Pattern Analysis (Dmvpa): A Tutorial And A Toolbox, Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashtok Samal, Prahalada K. Rao, Matthew R. Johnson
Center for Brain, Biology, and Behavior: Faculty and Staff Publications
In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on …
Eeg Study Of The Featural And Configural Components Of Face Perception, Heather Rose Stegman
Eeg Study Of The Featural And Configural Components Of Face Perception, Heather Rose Stegman
Summer Research
Prior research using functional magnetic resonance imaging (fMRI) suggests that facial features (i.e. eyes, nose, and mouth) and their configuration (i.e. T-shaped arrangement of features) are processed in different face-specific brain regions. However, precise response time of featural and configural face processing is unknown. Featural processing may occur before configural processing, or configural processing may occur before featural processing; conversely, they may occur simultaneously. Here, using the electroencephalography (EEG), we will examine the face-specific event related potential (ERP), the N170, to analyze temporal differences between featural and configural face processing.