Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision Both sides next revision
tutorial:human_ecog [2018/10/21 15:02]
42.49.180.224 [Analysis of human ECoG and sEEG recordings]
tutorial:human_ecog [2018/07/13 19:56] (current)
arjen [Introduction]
Line 2: Line 2:
  
 ====== Analysis of human ECoG and sEEG recordings ====== ====== Analysis of human ECoG and sEEG recordings ======
-====== Analysis of human ECoG and sEEG recordings ======+===== Introduction ​===== 
 + 
 +Intracranial EEG (iEEG) allows simultaneous recordings from tens to hundreds of electrodes placed directly on the neocortex (electrocorticography,​ ECoG), or intracortically (stereoelectroencephalography,​ SEEG). These recordings are known for known for their high spatiotemporal precision. In humans, the most common implementation of iEEG is when non-invasive techniques such as scalp-EEG and MRI do not provide sufficient information to guide surgery in medication refractory epilepsy patients. This tutorial illustrates how to deal with the multitude of raw anatomical and electrophysiological data files in order to get to integrated neural observations. 
 + 
 +Before we start, it is important to emphasize that human iEEG datasets are solely acquired for clinical purposes and come in different shapes and sizes. Some medical institutes use photography or X-ray (e.g., see [[tutorial:​monkey_ecog|Analysis of monkey ECoG recordings)]] for including anatomy in the analysis of the functional recordings, whilst others use CT (3D image from a series of X-rays) and/or MR, or combinations thereof. The example iEEG dataset used in this tutorial is not representative for all the datasets obtained in the field but it is meant to serve as a platform for thinking and dealing with the challenges associated with analyzing this type of data. 
 + 
 +The tutorial demonstrates the analysis of task-related high-frequency-band activity (~70 to 150 Hz), a prominent neural signature in intracranial data that has been associated with neuron population level firing rate. Many other supported analyses such as event-related potential analysis, connectivity analysis, and statistical analysis have been described in detail elsewhere (Oostenveld et al., 2011; Maris & Oostenveld, 2007; Bastos & Schoffelen, 2016). You will need the iEEG data of SubjectUCI29,​ which can be obtained from [[https://​doi.org/​10.5281/​zenodo.1201560|here]]. If you are getting started with FieldTrip, download the most recent version from its homepage or GitHub and [[faq:​should_i_add_fieldtrip_with_all_subdirectories_to_my_matlab_path|set up your MATLAB path]]. 
 + 
 + 
 +<note important>​ 
 +The information on this page originates from the human intracranial data analysis protocol described in Stolk, Griffin et al., **[[https://​www.nature.com/​articles/​s41596-018-0009-6|Integrated analysis of anatomical and electrophysiological human intracranial data]]”**,​ Nature Protocols, 2018. Please cite that paper when you use the methods described here.  
 +</​note> ​
 ===== Background ===== ===== Background =====