Exploring the Usability of Quantum Machine Learning for EEG Signal Classification

The classification of Electroencephalogram (EEG) signals into distinct frequency bands is a critical task in understanding brain function and diagnosing neurological disorders. The information obtained from frequency-specific classification has multiple applications, such as frequency-based wheelchair control, frequency-based 36-stroke brain operated keyboard for paralysed patients etc. In this work, a method based on machine learning to develop the frequency-based classification of EEG signals is proposed. The performance of Classical Machine Learning (CML) algorithms and Quantum Machine Learning (QML) techniques for the classification of EEG signals across four frequency bands are investigated. The primary objective is to evaluate the performance of QML models against traditional CML models in terms of computational efficiency, time efficiency and accuracy and uncover potential benefits offered by quantum computing for a particular task of classifying EEG signals. The goal is to assess the advantages of using quantum algorithms for classifying EEG signals. This includes improving accuracy and enhancing efficiency. These findings add to the existing knowledge about how quantum machine learning can benefit neuroscience in terms of enhancing methods that rely on EEG data.