Comparison of QSVM with Other Machine Learning Algorithms on EEG Signals - Aksoy, Gamzepelin; Karabatak, Murat (Institute of Electrical and Electronics Engineers Inc., 2023)
In the Brain-Computer Interfaces where Electroencephalogram (EEG) signals are used, fast and accurate analysis of data is one of the important steps. For this aim, various machine learning methods are being developed. In this study, we compared the performance of the Quantum Powered Support Vector Machine (QSVM) with classical machine learning algorithms for classifying EEG data. Analyzes were performed in the quantum simulation environment and due to the inadequacies in the quantum environment, the number of samples in the data set was reduced by using Principal Component Analysis. The maximum performance of QSVM was found to be 99.47% in the experiments. When all the results in the analyzes are examined, it is seen that the QSVM has achieved good performance. By removing the limitations of quantum computers and making changes in the hyperparameters of quantum-based algorithms, the use of quantum-based machine learning methods in the classification of EEG data can provide better results in terms of accuracy and runtime. © 2023 IEEE.
11th International Symposium on Digital Forensics and Security, ISDFS 2023 -- 11 May 2023 through 12 May 2023 -- TN -- 189042
Keywords: EEG; machine learning algorithm; quantum machine learning; quantum support vector machine