http:\/\/neuroimage.usc.edu\/brainstorm\/Tutorials\/Epileptogenicity<\/a>.<\/span><\/p>\n<\/span><\/p>\nTeaching material available<\/span><\/p>\n\u00a0<\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ background_color=”#00659d” _builder_version=”3.0.97″ custom_padding=”54px|0px|53px|0px”][et_pb_row custom_padding=”||0px|” _builder_version=”3.0.97″][et_pb_column type=”4_4″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_blurb title=” Session 2: Monday Afternoon – 2:00 to 5:00 PM” use_icon=”on” font_icon=”%%36%%” icon_color=”#0db7dd” icon_placement=”left” background_layout=”dark” content_max_width=”1100px” _builder_version=”3.0.97″ header_level=”h2″ header_font=”|||on|||||” header_text_color=”#000000″ custom_margin=”||0px|” custom_padding=”||0px|”][\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.0.97″][et_pb_column type=”1_2″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_text background_layout=”dark” _builder_version=”3.0.97″ text_font=”||||||||” text_text_color=”#ededed” header_font=”||||||||” header_3_font=”Comfortaa||||||||” header_3_font_size=”20px” header_3_text_color=”#ffffff” header_4_font=”||on||||||” header_4_text_color=”#0fe7ff”]<\/p>\n
Frequency Tagging (steady state analysis) in EEG<\/strong>
<\/b><\/h3>\nMolly Henry<\/em><\/span><\/i><\/h4>\n\u201cFrequency-tagging\u201d, or steady-state analysis, traditionally refers to the practice of providing a repetitive sensory stimulus (a flashing light or a tone sequence, for example), and then analyzing the brain\u2019s representation of that stimulus in the frequency domain. This technique has been fruitful in the past for determining hearing thresholds, especially of individuals incapable of providing a behavioral response (e.g., infants) and for determining a person\u2019s ability to selectively attend to one stimulus at the expense of another. More recently, the technique has been applied in the domain of neural entrainment, whereby neural oscillations become synchronized with rhythmic environmental stimuli. I will give a theoretical background comparing these approaches. Then, I will demonstrate how frequency-tagging analysis can be taken further, relating high-dimensional neural dynamics to moment-to-moment variations in perception, providing a powerful tool for inferring the neural \u201cstates\u201d that underlie human perception.<\/p>\n
Teaching material available<\/p>\n
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Combining eye-tracking & EEG
<\/b><\/h3>\nOlaf Dimigen<\/span><\/i><\/h4>\nDuring every waking hour, we move our eyes about 10,000 times. The combination of EEG recordings with eye-tracking is a promising approach to study visual cognition in such natural situations. This workshop will introduce students and researchers to this relatively new technique and its advantages, with a focus on data analysis. It will cover the following topics: Properties of saccade- and fixation-related brain potentials, building a suitable laboratory setup, data synchronization and integration, optimal strategies for removing eye movement artifacts from the data, and the use advanced linear deconvolution models to control for overlapping potentials and other confounds during natural vision. In hands-on exercises, we will analyze a combined dataset, using the EYE-EEG toolbox and the brand-new unfold toolbox.<\/span><\/p>\n<\/span><\/p>\n\u00a0<\/span><\/p>\n\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.0.97″][et_pb_column type=”1_2″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_text background_layout=”dark” _builder_version=”3.0.97″ text_font=”||||||||” text_text_color=”#ededed” header_font=”||||||||” header_3_font=”Comfortaa||||||||” header_3_font_size=”20px” header_3_text_color=”#ffffff” header_4_font=”||on||||||” header_4_text_color=”#0fe7ff”]<\/p>\n
Human Neocortical Neurosolver: A New Tool for Cellular and Circuit Level Interpretation of EEG
<\/b><\/h3>\nStephanie Jones, Sam Neymotin, Dylan Daniels
<\/em><\/span><\/i><\/h4>\nWe developed the Human Neocortical Neurosolver (HNN), an open-source modeling tool designed to help researchers interpret cellular and circuit origins of EEG\/MEG. HNN presents a user-friendly GUI to a biophysically principled model of a neocortical circuit, under thalamic and cortical drive, that simulates the primary electrical currents underlying EEG\/MEG recordings. We will describe the model and teach participants how to study the origins of commonly measured signals, including event related potentials and low frequency rhythms (alpha\/beta\/gamma). Participants will learn how to compare model results to recorded data and to adjust parameters to develop and test hypotheses on circuit-level mechanisms.<\/span><\/p>\nTeaching material available<\/span><\/p>\n\n
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Inverted encoding models of EEG signals<\/strong>
<\/b><\/h3>\nThomas C Sprague<\/em><\/span><\/i><\/h4>\nIn cognitive neuroscience, we\u2019re often interested in understanding how cognitive operations impact mental representations. For example, how are neural representations transformed by visual attention, or how are they updated when manipulating the contents of working memory?\u00a0 I will walk through a recently-developed analysis procedure I call an \u201cinverted encoding model\u201d that enables us to reconstruct representations of feature values (like spatial position or visual orientation) given single neural activity patterns, including fMRI activation from individual regions of interest and evoked and induced scalp potentials measured with EEG.<\/p>\n
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[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.0.47″][et_pb_row custom_padding=”||0px|” _builder_version=”3.0.97″][et_pb_column type=”4_4″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_blurb title=” Session 3: wednesday morning – 9:00 to 12:00 AM” use_icon=”on” font_icon=”%%36%%” icon_color=”#0dc7e8″ icon_placement=”left” content_max_width=”1100px” _builder_version=”3.0.97″ header_level=”h2″ header_font=”|||on|||||” custom_margin=”||0px|” custom_padding=”||0px|”][\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.0.97″][et_pb_column type=”1_2″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.0.97″ text_font=”||||||||” header_font=”||||||||” header_3_font=”Comfortaa||||||||” header_3_font_size=”20px” header_3_text_color=”#00659d” header_4_font=”||on||||||” header_4_text_color=”#000000″]<\/p>\n
Modeling EEG-behavior relationships<\/strong>
<\/b><\/h3>\nValentin Wyart, Aur\u00e9lien Weiss<\/em><\/span><\/i><\/h4>\nThis hands-on tutorial will present methods and code for relating EEG activity to behavior, a key analysis for characterizing the role of a neural signal of interest in cognition. The parametric modeling framework on which these brain-behavior analyses rest will be first presented, and then applied to EEG datasets collected in different perceptual decision-making contexts. The goal of the tutorial is to outline the explanatory power of these methods, and to show their generalizability to various fields of research in cognition (perception, decision-making, learning, memory). Participants are expected to have at least minimal experience with programming and basic knowledge of statistics.<\/p>\n
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Separating different alpha sources <\/strong>
<\/b><\/h3>\nRasa Gulbinaite<\/em><\/h4>\nAlthough the M\/EEG community tends to agree on the existence of multiple generators of alpha-band (7-13 Hz) rhythm, there is little agreement on ways to separate them. In this tutorial, you will learn analytical and experimental approaches that will allow you to isolate different alpha sources using: (1) independent component analysis (ICA), a multivariate source separation technique that combines information from all the electrodes, and allows to determine independent sources of activity based on the statistical structure of the data; (2) resonance responses to rhythmic visual stimulation. You will also learn how to determine spectral properties of multiple alpha generators (peak frequency, width of the alpha-band etc.).<\/p>\n
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[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.0.97″][et_pb_column type=”1_2″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.0.97″ text_font=”||||||||” header_font=”||||||||” header_3_font=”Comfortaa||||||||” header_3_font_size=”20px” header_3_text_color=”#00659d” header_4_font=”||on||||||” header_4_text_color=”#000000″]<\/p>\n
Temporal response functions \u2013 Extraction of the neural response to continuous stimuli<\/strong>
<\/b><\/h3>\nLorenz Fiedler<\/em><\/h4>\nGoing beyond conventional ERP designs (i.e., multi-trial averaging), encoding models allow the extraction of the neural response to continuously varying stimulus features, such as luminance or the speech envelope. In this tutorial, we will implement and discuss the extraction of the neural response to continuous stimulus features. First, we will obtain relevant stimulus features. Second, we will discuss how to preprocess the EEG data. Third, we will extract the neural response using several methods. Finally, we will test how well the extracted response predicts unknown data. I will prepare some data and code, but the participants are welcome to bring their own datasets.<\/p>\n
Teaching material available<\/span><\/p>\n\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.0.97″ text_font=”||||||||” header_font=”||||||||” header_3_font=”Comfortaa||||||||” header_3_font_size=”20px” header_3_text_color=”#00659d” header_4_font=”||on||||||” header_4_text_color=”#000000″]<\/p>\n
Making sense of (large amounts of) human intracranial EEG data<\/strong>
<\/b><\/h3>\n\u00a0Jean-Philippe Lachaux<\/em><\/h4>\nIn this workshop, we will learn how to apprehend large-scale cortical dynamics supporting cognitive functions using intracranial EEG data from epileptic patients. I will rely on a novel iEEG visualization software (HiBoP) and a large iEEG dataset which will be freely distributed within the Human Brain Project (including visual and auditory perception, attention, language and memory tasks). The demonstration will mostly focus on the issue of comparative timing (relative latencies of activation across cortical sites) and functional connectivity, as revealed by amplitude-amplitude co-fluctuations.\u00a0<\/span><\/p>\nTeaching material available soon, we are debugging
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[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"
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