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A Data Augmentation Scheme for Geometric Deep Learning in Personalized Brain-Computer Interfaces
2020
IEEE Access
Electroencephalography signals inherently deviate from the notion of regular spatial sampling, as they reflect the coordinated action from multiple distributed overlapping cortical networks. Hence, the observed brain dynamics are influenced both by the topology of the sensor array and the underlying functional connectivity. Neural engineers are currently exploiting the advances in the domain of graph signal processing in an attempt to create robust and reliable brain decoding systems. In this
doi:10.1109/access.2020.3021580
fatcat:pb45skxbtfbadnm3ef7gxq65ke