Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
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ICA-based artifact removal does not consistently improve deep network decoding performance on EEG data across three BCI tasks and multiple models.
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Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
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I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks
ICA-based artifact removal does not consistently improve deep network decoding performance on EEG data across three BCI tasks and multiple models.