DS-MTNet uses frequency-constrained source decomposition and reusable information slots to jointly decode three EEG-based cognitive readouts in a driving task, outperforming single-task and multi-task baselines.
Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX,
2 Pith papers cite this work, alongside 96 external citations. Polarity classification is still indexing.
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Analytical expressions and existence criteria for higher-order topological corner and edge states in two-dimensional kagome and square grid-like beam frames are presented.
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DS-MTNet:Structured Multi-Task EEG Decoding for Human-Machine Collaboration
DS-MTNet uses frequency-constrained source decomposition and reusable information slots to jointly decode three EEG-based cognitive readouts in a driving task, outperforming single-task and multi-task baselines.
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Higher-order topological corner states and edge states in grid-like frames
Analytical expressions and existence criteria for higher-order topological corner and edge states in two-dimensional kagome and square grid-like beam frames are presented.