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IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)

Exploring Quantum Machine Learning for Electroencephalogram Classification

Raymond H. Ho, Kevin Hung

Published May 20, 2023• 2 citations
Publisher

Abstract

Quantum machine learning (QML) is a relatively new discipline emerging from the concepts of machine learning and quantum computing, whereby quantum algorithms are used to solve machine learning tasks. This paper explores the use of quantum machine learning for electroencephalogram (EEG) classification. In particular, a previously proposed EEG feature extraction method and classification framework for classifying dementia subjects were followed in this study. A quantum classifier replaced the classical classifier component of the framework, and the classification accuracies between the quantum and classical classifiers were compared. This study has demonstrated that applying QML in healthy-dementia classification can be implemented using near-term quantum devices or quantum simulators with moderate performance. The quantum classifier achieved an overall classification accuracy of 81.67% and 79.17% in a train-test split performance test and an n $\times$ k-fold cross-validation test, respectively. However, the quantum approach did not produce higher classification accuracies than the classical classifier. Despite the promise of quantum advantages, further investigation and optimization are required to improve its effectiveness.

DOI
10.1109/iscaie57739.2023.10165407

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Quantum machine learning (QML) is a relatively new discipline emerging from the concepts of machine learning and quantum computing, whereby quantum algorithms are used to solve machine learning tasks. This paper explores the use of quantum machine learning for electroencephalogram (EEG) classification. In particular, a previously proposed EEG feature extraction method and classification framework for classifying dementia subjects were followed in this study. A quantum classifier replaced the classical classifier component of the framework, and the classification accuracies between the quantum and classical classifiers were compared. This study has demonstrated that applying QML in healthy-dementia classification can be implemented using near-term quantum devices or quantum simulators with moderate performance. The quantum classifier achieved an overall classification accuracy of 81.67% and 79.17% in a train-test split performance test and an n ×\times× k-fold cross-validation test, respectively. However, the quantum approach did not produce higher classification accuracies than the classical classifier. Despite the promise of quantum advantages, further investigation and optimization are required to improve its effectiveness.
Quantum machine learning (QML) is an emerging research field, which is devoted to devising and implementing quantum algorithms that could enable machine learning faster than that of classical computers. In this article, a hierarchic quantum mechanics-based framework is investigated to implement both the feature extraction and classification in the electroencephalogram (EEG) signal. First, the classical EEG signal dataset is prepared as a quantum state while the sign of the data point is preserved. The prepared quantum state is then evolved with the quantum wavelet packet transformation (QWPT) and the wavelet packet energy entropy (WPEE) feature is extracted as the input of the subsequent quantum classifier. We finally propose the improved quantum support vector machine with the arbitrary nonlinear kernel, which is employed to predict the label of the EEG signal. The complexity analysis indicates that the proposed framework provides exponential speedup over the same structured classical counterpart. Besides, the quantitative experimental results verify the feasibility and validity.
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Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as EEGNet have shown success in decoding EEG signals, they often struggle with data complexity, inter-subject variability, and noise robustness. Recent advancements in quantum machine learning (QML) offer new opportunities to enhance EEG analysis by leveraging quantum computing’s unique properties. In this study, we extend the previously proposed Quantum-EEGNet (QEEGNet), a hybrid neural network incorporating quantum layers into EEGNet, to investigate its generalization ability across multiple EEG datasets. Our evaluation spans a diverse set of cognitive and motor task datasets, assessing QEEGNet’s performance in different learning scenarios. Experimental results reveal that while QEEGNet demonstrates competitive performance and maintains robustness in certain datasets, its improvements over traditional deep learning methods remain inconsistent. These findings suggest that hybrid quantum-classical architectures require further optimization to fully leverage quantum advantages in EEG processing. Despite these limitations, our study provides new insights into the applicability of QML in EEG research and highlights challenges that must be addressed for future advancements.