Pre-trained 2D convolutional neural network in the task of full-head segmentation into 7 classes (gray matter, white matter, CSF, bone, air-cavities, skin and background), this model does not rely on a tissue probability map and will be a stand alone segmenter. Code also available for training model for new tasks.

The Datasets, includes, 4 (healthy) T1-weighted MRIs with corresponding full-head segmentations,54 (4 healthy, 50 stroke) defaced T1-weighted MRIs with corresponding defaced full-head segmentations. Labels are split into 7 classes (gray matter, white matter, CSF, bone, air-cavities, skin and background).

Pre-trained volumetric deep convolutional neural network on MRIs of 43 stroke patients and 4 healthy individuals for the task of segmentation into 7 classes (gray matter, white matter, CSF, bone, air-cavities, skin and background), surpassing human performance. Code also available for training model for new tasks.

A large dataset of EEG, heart rate, pupil, eye movements and more, collected while subjects watched short education videos. Includes data from 5 different experiments with a total of 178 study participants.

A fully automated Realistic vOlumetric-Approach to Simulate Transcranial electric stimulation. This is an open-source tool that runs on MATLAB and calls open-source C software packages such as iso2mesh and getDP. Starting from an MRI structural image, it segments the full head, places virtual electrodes, generates an FEM mesh and solves for voltage and electric field distribution -- at 1 mm resolution all this in about 15 minutes.

Complex systems like brains, markets, and societies exhibit internal dynamics influenced by external factors. In these systems it is often not clear how to disentangle the delayed external effects from the internal dynamic. We propose to capture delayed interactions between internal and external variables with an Vector Autoregressive model with eXogenous input (VARX). We combined this with classic Granger analysis to determine significance of effects. Code is available in matlab, python and R.

We measures inter-subject correlationto (ISC) analyze stimulus responses in EEG in the absence of regressors or time markers. We use this to understand the responses to video and auditory narratives.

We developed Correlated Component Analysis (CorrCA) for the purpose of identifying components in the EEG with high inter-subject correlation. The technique is useful in any context where one would like to identify linear components with high reproducibility (across subjects, repeats, raters, etc).

This methods can identify what the brain encodes about the stimulus while simultaneously decoding the corresponding brain activity. It is a component extraction technique that works in the absence of precise time markers. We use this to understand the EEG responses to unique experiences, such as video games.

This is an anatomically detailed segmentation of the MNI standard head extended to include structures relevant to current-flow models (CSF, skull, scalp, neck). As we show in a recent paper, the resulting lead field is identical to the TES forward model and can thus be used for TES targeting as well as EEG source localization.

Individualized current-flow modeling starts with the MRI of an individual subject. The first task is to accurately segment the anatomy. These are a few tools based on SPM8, which we have developed for fully automated whole-head segmentation (not just brain).

Whole-head Tissue Probability Map and fast processing routines to improve SPM results.

Adds neighborhood priors to the ananatomical priors.

Collaborating with Dr. Anli Liu at NYU Medical Center, we measured electric fields in vivo intracranially during TES on 10 subjects (~1300 electrodes). These data are used to validate and calibrate the computational models of TES. You can download these data to benchmark your own models.

This is a small software compiled from Matlab that can be used to quickly simulate tDCS on a sphere. Users can adjust the thickness of brain, CSF, skull and scalp, as well as their conductivities to see how these parameters affect the current-flow patterns inside the brain.