Abstract
Event-related potential signal classification is a really difficult challenge due to the low signal-to-noise ratio. Deep neural networks (DNN), which have been employed in different machine learning areas, are suitable for this type of classification. UNet (a convolutional neural network) is a classification algorithm proposed to improve the classification accuracy of P300 electroencephalogram (EEG) signals in a non-invasive brain-computer interface. The proposed UNet classification accuracy and precision were 64.5% for single-trial classification using a large P300 dataset of school-aged children, including 138 males and 112 females. We compare our results with the related literature and discuss limitations and future directions. Our proposed method performed better than traditional methods.
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Acknowledgment
This work was supported by the University specific research project SGS-2022–016 Advanced Methods of Data Processing and Analysis (project SGS-2022–016).
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Titkanlou, M.K., Mouček, R. (2023). Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_14
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