CluSem

CluSem implements a clustering-based ensemble method to improve classification of motor imagery electroencephalogram (MI-EEG) signals for brain–computer interface (BCI) applications.

Key Features:

• Clustering-based Ensemble Technique: Integrates clustering algorithms with ensemble learning to group similar MI-EEG signal patterns and aggregate predictions from multiple models.
• Enhanced Classification Performance: Demonstrates 5%–15% improvement in classification accuracy over existing methods, validated on proprietary and publicly available EEG datasets.
• Real-time Processing for BCI: Optimized for real-time BCI applications to process high-dimensional, dynamic EEG signals.
• CluGame Evaluation and Multithreading: Includes CluGame as an evaluation platform that uses threads to manage prediction tabulation and animation control for real-time testing.

Scientific Applications:

• Brain-Computer Interfaces (BCIs): Improves interpretation of motor imagery EEG for communication and control in BCI systems.
• Neurofeedback Systems: Enhances EEG signal classification accuracy for neurofeedback-based rehabilitation and cognitive training.
• Research and Development: Provides a framework for researchers in neuroscience and bioengineering to explore MI-EEG classification and real-time BCI methodologies.

Methodology:

Combines clustering algorithms with ensemble learning to group similar MI-EEG patterns and aggregate model predictions, optimized for real-time processing; the CluGame evaluation uses multithreading for prediction tabulation and animation control.