Lin, Phone

Distinguished Professor
Graduate Institute of
Biomedical Electronics and Bioinformatics
National Taiwan University

[email protected]

Dept. and Web

https://www.csie.ntu.edu.tw/~plin/

Project Description

To develop an advanced diagnostic platform for schizophrenia (SZ) and major depressive disorder (MDD) that combines artificial intelligence (AI) with electroencephalography (EEG) data analysis.

This platform aims to address the limitations of traditional diagnostic methods—such as reliance on clinician observation and patient self-reports, which can be time-consuming and subjective—by leveraging machine learning algorithms to analyze EEG signals.

Through extracting specific features from EEG data, this AI-driven technology aspires to assist clinicians in delivering faster, more objective, and accurate diagnoses of SZ and MDD, ultimately improving diagnostic reliability and patient care.

Requirement

  1. Machine Learning and AI: Understanding of machine learning algorithms, particularly those relevant to biomedical signal processing;
  2. Signal Processing: Knowledge of EEG signal processing techniques and familiarity with feature extraction from EEG data;
  3. Programming: Proficiency in programming languages such as Python or MATLAB, with experience in data analysis libraries (e.g., NumPy, pandas, scikit-learn) and ML frameworks (e.g., TensorFlow, PyTorch);
  4. Statistics and Data Analysis: Foundational skills in statistical methods for analyzing experimental data and model evaluation.