Jul 3, 2024 · Our experimental results show that the EDPNet outperforms state-of-the-art models with superior classification accuracy and kappa values (84.11% ...
Jul 3, 2024 · Motor imagery electroencephalogram (MI-EEG) decoding plays a crucial role in developing motor imagery brain-computer interfaces (MI-BCIs).
Jul 4, 2024 · EDPNet: An Efficient Dual Prototype Network for Motor Imagery EEG Decoding Can Hana, Chen Liua, Crystal Caia, Jun Wangb,∗, Dahong Qiana,∗
Jul 4, 2024 · Motor imagery electroencephalogram (MI-EEG) decoding plays a crucial role in developing motor imagery brain-computer interfaces (MI-BCIs).
This is the official repository to the paper "A Spatial-Spectral and Temporal Dual Prototype Network for Motor Imagery Brain-Computer Interface". Abstract.
In this paper, we propose an Efficient Dual Prototype Network (EDPNet) to enable accurate and fast MI decoding. EDPNet employs a lightweight adaptive spatial- ...
1 day ago · In this paper, we propose an Efficient Dual Prototype Network (EDPNet) to enable accurate and fast MI decoding. EDPNet ... [Show full abstract] ...
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然而，由于EEG信号的内在复杂性和小样本量，从MI中解码意图仍然具有挑战性。因此，本文提出了一种高效的双重原型网络（EDPNet），以实现准确和快速的MI解码。EDPNet采用轻量级自 ...
Jun 20, 2024 · Proposes an efficient deep learning model called EDPNet for decoding motor imagery from electroencephalogram (EEG) signals Introduces a novel ...
In this work we introduce a novel subject-independent meta-learning framework for the purpose of improving deep learning decoding performance for EEG-based ...