Brain-Computer Interface: P300 Signal Analytics and Classification
Brain-Computer Interface: P300 Signal Analytics and Classification is a Moderate-level, 3 Days (60-90 Minutes Each Day) online program by NSTC.
- ModeRecorded Lectures
- LevelIntermediate
- Duration3 Days (60-90 Minutes Each Day)
| Attribute | Detail |
|---|---|
| Format | Recorded Lectures |
| Level | Intermediate |
| Duration | 3 Days (60-90 Minutes Each Day) |
| Certification | e-Certification + e-Marksheet |
| Fee | Free |
| Tools | Python Google Colab EEG toolkits |
About the Brain-Computer Interface: P300 Signal Analytics and Classification Course
Explore the world of Brain‑Computer Interfaces through hands‑on analysis and classification of P300 signals, empowering participants to decode neural responses and apply advanced signal analytics in BCI applications.
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Program Highlights
• Comprehensive coverage of Brain from fundamentals to advanced applications
• Hands-on projects and real-world case studies in ai
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Python, Google Colab, EEG toolkits
• Career-oriented training for academic and professional growth in ai
Course Curriculum
Module 1: Day 1 – Introduction to BCI and P300 Signals
- Discover fundamentals of Brain‑Computer Interfaces and real‑world applications
- Examine EEG signal basics and neural response patterns
- Visualize raw EEG data and identify P300 components in Google Colab
Module 2: Day 2 – P300 Signal Preprocessing & Feature Extraction
- Apply filtering, artifact removal, and normalization techniques
- Extract time‑domain and frequency‑domain features for P300 detection
- Prepare clean datasets ready for classification modeling
Module 3: Day 3 – P300 Classification & BCI Applications
- Implement classification algorithms (LDA, SVM, Random Forest, Deep Learning)
- Evaluate model performance using accuracy, precision, recall, and confusion matrices
- Explore real‑world BCI use cases such as assistive devices and cognitive research
