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• W3037607538 abstract "Decoding brain signals has gained many attention and has found much applications in recent years such as Brain Computer Interfaces, communicating with controlling external devices using the user's intentions, occupies an emerging field with the potential of changing the world, with diverse applications from rehabilitation to human augmentation. This being said brain signal analysis, EEG brain signal analysis in particular, is a challenging task. With the advances and achievements in the field of deep learning in problem solving with using only raw data, few attempts has been carried in recent years, to apply deep learning to tackle EEG among other types of brain signals. In this study, we propose a novel loss function, called DeepCSP to extend the classical Common Spatial Patterns to a non linear, differentiable module to serve as the loss function to enforce linearly separable latent representations of EEG signals belonging to different classes in an end to end manner on raw signals without the need to perform extensive feature engineering. With recent generalizations of deep learning methods to work on arbitrarily structured graphs and the introduced loss we have proposed two light weight models to decode EEG signals and carried experiments to show their performance." @default.
• W3037607538 created "2020-07-02" @default.
• W3037607538 creator A5052051065 @default.
• W3037607538 creator A5078558164 @default.
• W3037607538 date "2020-06-16" @default.
• W3037607538 modified "2023-09-27" @default.
• W3037607538 title "Graph Convolutional Neural Networks for analysis of EEG signals, BCI application" @default.
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• W3037607538 hasPublicationYear "2020" @default.
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