EEG Signal Analysis is a research-driven service designed for project-ready integration and scientific exploration. It includes feature extraction, classification, and data visualizations using Python or MATLAB, focused on enabling research outcomes and technical applications.
Welcome to EEG Signal Analysis, a specialized service by Nibras Abo Alzahab that bridges biomedical research with advanced AI techniques. Whether you’re a researcher, engineer, or healthcare innovator, this offering is crafted to help you decode brain signals and derive meaningful insights from EEG data.
From raw signal processing to advanced classification pipelines, the service enables you to build robust EEG-based applications in cognitive science, neuromarketing, mental health, and biometric authentication.
Key Features
- Feature Extraction: Apply spatial, temporal, and frequency-domain techniques (e.g., PSD, ERP, wavelets) for rich EEG feature sets.
- Signal Preprocessing: Filter, segment, and denoise signals with ICA, bandpass filtering, and artifact removal pipelines.
- Classification Models: Use machine learning models (SVM, Random Forest, CNNs) tailored to your research or clinical goals.
- Data Visualization: Generate clean, insightful plots and topographic maps for publications or internal validation.
- Python & MATLAB Workflows: Delivered as reproducible scripts or Jupyter Notebooks, ready for academic or project use.
- Application-Aware Analysis: Support for BCI, mental workload detection, emotion recognition, and patient monitoring scenarios.
