Machine Learning SIG

Objectives

Advance general knowledge of machine learning techniques within the MEG community. Discuss journal articles, replicate techniques on NIH data, develop new ML techniques at NIH.

Format

1. Specific Projects (Weekly)
   1. Code Review
   2. Project updates
   3. Questions and Answers Clinic for users
2. General (Monthly)
   1. Journal Club
   2. Hackathons – implement novel technique from JC with provided data
   3. Tutorial Workshops - instruct worked out examples with provided code/data
   4. General ML training
      1. Parameter tuning
      2. Model optimization
      3. Techniques
      4. Toolbox tutorials (Scikit-learn / keras)

Analysis Types

1. Decoding
   1. Multivariate time series classification between conditions
      1. Filtering Issues
   2. Realtime - Brain computer interface / neurofeedback
2. Subject classification –
   1. eg. Healthy Control vs Major Depressive Disorder
   2. What are the significant features (brain regions, Hz)
   3. Automated diagnosis of TBI from MEG low frequency signals
3. Prediction of future condition / Biomarkers
   1. What signals predict conversion from mild cognitive impairment to Alzheimer
   2. What signals predict recovery from traumatic brain injury
   3. What signals predict poor outcome for epilepsy surgery
4. Multimodal Integration
   1. Timing derived from MEG / localization from fMRI – joint ICA (??)
   2. Signal comparison between naturalistic viewing data MEG/fMRI
5. Signal classification
   1. Artifact
   2. Signal of interest – Spike detection for epilepsy
   3. Signal/sequence predicts correct response
6. Temporal learning models – RNN DL / markov model
   1. Inferences from deep learning models
   2. CNN on images vs. CNN of brain responses to same images

Preliminary Resources

MNE Python Decoding MNE Python Decoding at Source
SciKit Learn
Keras

Relevant Papers

Intro1

Intro2

Language Decoding