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Achieving R虏 scores above 0.8 for grasp force decoding demonstrates a breakthrough in cross-subject generalisation using hybrid EEG representations.
SwitchBraidNet achieves hybrid BCI performance with an INT8 model size of only 3.03 KB, making high-dimensional neural decoding feasible on low-power devices.
RL-ACRGNet achieves significant performance boosts in radiology report generation, setting new benchmarks in both accuracy and clinical relevance.
