Abstract
Time-series EEG signals in a raw form are challenging to analyze, train, and compute. Several feature extraction methods, such as fast Fourier transform, wavelet transform, and time-frequency distributions, are commonly employed for this purpose. However, when applied to different datasets, the alignment between the method and machine learning algorithms varies significantly. Through an EEG experiment, we test a simultaneous analysis and unsupervised learning application that can effectively determine what feature extraction method will potentially lead to a higher prediction precision when the ground truth is provided by the participants at a later stage.
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Almarri, B., Huang, CH. (2018). Simultaneous EEG Analysis and Feature Extraction Selection Based on Unsupervised Learning. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_25
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DOI: https://doi.org/10.1007/978-3-030-05587-5_25
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