The Practical Value of Quantum Machine Learning for Accelerating EEG Signal Analysis
Overview
The field of quantum computing, initially inspired by Richard Feynman and developed by David Deutsch, has led to rapid advancements in quantum algorithms and quantum machine learning (QML). This interdisciplinary field aims to accelerate machine learning processes compared to classical methods, with practical applications in EEG signal processing.
EEG Signal Processing
Automated EEG analysis is vital for understanding neural processes and diagnosing disorders due to the volume of data. Feature extraction, such as sample entropy and power spectra, plays a crucial role in brain mapping. The integration of quantum mechanics with EEG signal processing allows for expedited computation and robust classification methods.
Quantum State Preparation and Feature Extraction
A quantum state preparation procedure is devised to encode EEG signals into quantum states, facilitating multi-channel scenarios. The Quantum Wavelet Packet Transformation (QWPT) is used to extract critical features from the EEG signal, which are then inputted into a Quantum Machine Learning (QML) classifier for efficient classification.
Classification and Experimental Results
A universal nonlinear kernel is efficiently implemented for quantum SVM classification, enabling nonlinear data separation. Experimental validation on real-world data confirms the feasibility and efficacy of the proposed framework, showcasing exponential acceleration over classical methods in complexity.
