Convolutional Neural Networks with Quantum Inspiration: An Approach to Improved EEG Signal Processing

Abstract

The research now proceeds to handle the problems of traditional methods with noise and integrity issues of EEG signals by implementing a quantum-inspired CNN for developing signal processing. The model provides enhanced performance in feature extraction due to the intrinsic manipulation and processing of the sinusoidal signals, made possible with the help of specialized quantum simula- tion layers: Quantum Entanglement and Quantum Calculation. If tested on a tailored dataset, the model will show considerable improvements compared to tra- ditional signal processing techniques. It can turn out to be useful for biomedical engineering, audio processing, and telecommunications. It will support improve- ments in the quality of signal processing while furthering research into how quantum computing elements could be implemented within established neural architectures.