Quantum Machine Learning for Accelerating EEG Signal Analysis

Quantum machine learning (QML) offers practical value for accelerating EEG signal analysis. By combining quantum computing with EEG signal processing, we can expedite computation and enhance classification methods. In EEG signal processing, automated analysis is crucial for understanding neural processes and diagnosing disorders. Quantum state preparation encodes EEG signals into quantum states, allowing for expedited computation in 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. The integration of quantum mechanics with EEG signal processing allows for expedited computation and robust classification methods. This results in exponential acceleration over classical methods in complexity, as confirmed by experimental validation on real-world data. In conclusion, the practical application of quantum machine learning in EEG signal analysis offers accelerated computation and robust classification methods, providing valuable insights into neural processes and disorder diagnosis.