Full EEG workshop
IN OFFICE SESSION
(LYON - FRANCE)
Day 1: From neuroanatomy to EEG signals
- Fundamentals of neuroanatomy: brain cells and brain organization
- Fundamental of Physical: electric and magnetic fields
- Fundamentals of Bio-Physical: neurons and electric activity from cells to scalp
- Amplifiers and signal
- Fundamentals of electronics
- Analogical and digital signal
- Hardware and Software
- Differential amplification
- Ground and reference
- Pre-processing basics step
- ERP / EFP
- Spectral analysis method
- Phase and connectivity
- Deep source localization methods
- ICA methods
- Multimodal recording set up and time synchronization
- Triggering and markers
- Latency & jitter
- Synchronization strategies
- Sampling rate, Nyquist theorem and aliasing
- Sample quantification and resolution
- Codification
Basics of neurophysiology
General overview of EEG amplifier technologies
General overview of analysis technics of EEG data
Multimodal signal and synchronization techniques
Day 2: Fundamentals of EEG signal processing and acquisition environment
Fundamentals of EEG signal processing
- EEG analysis techniques (event-locked and block design)
- Noise in ERP analysis (endogenous and exogenous artifacts)
- Artifact reduction during acquisition
- Attenuation and artifact removal techniques (filtering - criteria - methods)
- FFT vs Wavelets
- Measures for ERP analysis (trial-by-trial amplitude, average amplitude, latency and grand average)
- Amplifier configuration (ActiveAmp - LiveAmp - actiCHamp)
- EEG electrode technology (active - passive)
- Noise source identification
- Protocol feasibility in the environment
The acquisition environment (hand-on session)
Day 3: Acquiring EEG data from subjects
- Data acquisition with various types of networks and electrodes
- Data acquisition with various types of networks and electrodes
- FFT analysis to determine sources of contamination
Comparative of different protocol combinations and possible layouts
Comparative data collection between referenced and non-referenced amplifiers.
Electromagnetics Contamination of the signal
Day 4: analyzing real acquired data using BRAIN VISION ANALYZER
- Data inspection (interpolation or exclusion of channels, manual rejection)
- Artifact rejection using criteria (gradient, amplitude, low activity or max-min)
- Filtering
- Referencing
- Independent Component Analysis (ICA) component rejection
- Data segmentation
- Calculation of averages
- FFT vs Wavelet analysis
- Exporting data for statistical analysis
- Connectivity analysis
Pre-processing and analysis of EEG data
Day 5: BCI-NFB Loop and AI session
BCI-NFB loop
- Fundamental principles of brain-computer interfaces (BCI)
- Definition and principles of neurofeedback
- Applications of neurofeedback in enhancing cognitive performance, treating disorders, etc.
- Strategies for designing BCI-NFB systems: selecting neurofeedback protocols, defining goals and feedback.
- Discussion on real challenges and limitations of BCI-NFB systems
- Hardware and software for BCI design (OpenBCI, OpenVibe)
- Introduction to AI
- AI in neuroscience
- Explanatory example of AI application
Tracks and examples of AI applications to EEG data
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