Instructors: René Luna García – Blanca Tovar Corona – Santiago I. Flores Alonso
CIC Artificial Intelligence Laboratory.
Python V3.X, TensorFlow (be aware of the versions’ compatibility with your
Python) and ANACONDA or any other IDE.
Title: Introduction to biological signal analysis
Artificial Intelligence techniques has proven to be an auxiliar tool aiming to prevent subjectivity during the classification of electrophysiological recordings (EEG, ECG, EMG, PCG).
This workshop will address specific topics, from the instrumentation of the electrophysiological recording acquisition and feature extraction techniques to their identification using supervised and unsupervised methods.
The tutorial will be carried out in 2 sessions of 3 hours each, as follows:
Session 1 (4 hrs):
- INSTRUMENTATION (1 hrs):
- Signal conditioning
- Sampling Theorem
- FEATURE EXTRACTION TECHNIQUES (3 hrs):
- Fourier Transform
- Short Time Fourier Transform (STFT)
- Wavelet Transform
- Mel Frequency Cepstral Coefficients (MFCCs)
Session2 (4 hrs):
- PATERN RECOGNITION & AI (3 hrs):
- K-Nearest Neighbours
- Artificial Neural Networks
- Unsupervised: K-Means
- ADVANCE METHODS (1 hrs):
- Nonlinear Architectures
- Deep Learning
- Transfer Learning
- Perspectives into scientific computing