Interpretable artificial intelligence for detection of psychiatric disorders based on multi-modal neuroimaging data

Project: FDCRGP

Project Details

Grant Program

Faculty-development competitive research grants program for 2020-2022 (batch 2)

Project Description

The medical field, in particular, must provide a reliable interpretation for their decision making since groundless decisions can lead to irreparable results.
In this study, we propose highly accurate, subject-independent, and explainable AI systems for the diagnosis of psychiatric disorders. This project includes three purposes: 1) An optimization algorithm to extract subject-invariant features in spatial, frequency, and temporal domains from the large size and multi-modal EEG/NIRS dataset, 2) Advanced interpretable interface based on the layer-wise decomposition technique where one can visually explain the output of the prediction, 3) Finally, we will collect a large neuronal dataset based on multi-modal setting (EEG, NIRS, PPG, diagnostic reports, etc.), that will greatly contribute to the related research field.
Short titleXAI for diagnosis of psychiatric disorders
Effective start/end date1/1/2012/31/22


  • Neuroscience
  • Brain-Computer Interface
  • Medical Diagnosis
  • explainable AI
  • multi-modal neuroimaging
  • EEG
  • NIRS


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