Table of Contents
- 1. We focus on studying non-invasive electroencephalogram-based brain-computer interfaces.
- 2. EEG: Electroencephalogram (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain.
- 3. Non-invasive brain-computer interfaces (BCIs) are categorized into two types: evoked vs. spontaneous BCIs.
- 4. For more details, please read Ch. 1 in a book, “EEG Signal Processing.”
- Importantly, please organize read papers in Google spreadsheet.
2. Conventional Methods
- 1. Lotte et al. “A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10 Year Update”
- 2. Motor Imagery Classification Methods
- 3. SSVEP Classification Methods
- 3.1. Canonical Correlation Analysis (CCA)
- 4. Further, please read Ch. 11.3.3 in a book, “Signal Processing and Machine Learning for Brain-Machine Interfaces.”
3. Deep and Hierarchical Methods
- 1. For MI-based BCI, Schirrmeister et al. proposed various methods.
- 1-1. Shallow ConvNet
- 1-2. Deep ConvNet
- 2. For looking deeper, read:
Brain-Computer Interfaces Research Starter Guideline