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Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention
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README.md

README.md

Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention.

This repository contains code for a study comparing sensor- and source-based BCIs. Specifically, it shows the theoretical and quantitative advantages (using both simulated and real data) associated with using the source space instead of the sensor space in a BCI context. We demonstrate this by classifying when subjects switched spatial auditory attention with data from a previous task.

Reference

  • Wronkiewicz, M., Larson, E., and Lee, A. KC (2016). Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention. Journal of Neural Engineering

Code

The code makes use of at least these specialized libraries:

  • MNE-Python v0.11
  • Freesurfer
  • Pysurfer
  • Statsmodels

Raw data is processed with process_SoP.py and process_createStcs.py

Figures 1 and 2 were created using meshes obtained via MRI scans and Blender. The activation simulation in Figure 4 is created with plot_topoDifference.py. The synthetic data for Figure 5 was created using switchPredSim.py, reorganized to link into previously written plotting code with reformulate_sim_scores.py, and plotted with plot_switchPredSim.py.

The script for computing spherical inverse models is makeSphModels.py. Code for sensor and source based classification are in switchPredSensLoop_all.py and switchPredSrcLoop_all.py, respectively. The script used for the statistics and plotting of Figure 6 is plot_switchPredLoop.py. General functions and parameters are in switchPredFun.py and config.py, respectively.

Unfortunately, the raw data are not included because:

  1. it contains (HIPAA-violating) identifying information and
  2. the raw data are many tens of gigabytes
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