Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four...
The proposed MultiResUNet3+ can effectively denoise EOG, EMG, and concurrent EOG and EMG artifacts from corrupted EEG waveforms. We have created a diverse and representative semi-synthetic EEG dataset closely resembling real-world corrupted EEG signals. The proposed 1D-segmentation model was trained ...
sarah-antillia / Tensorflow-Image-Segmentation-API Public Notifications You must be signed in to change notification settings Fork 0 Star 0 API for TensorflowSlightlyFlexibleUNet, TensorflowSwinUNet, TensorflowTransUNet, TensorflowMultiResUNet, TensorflowAttentionUNet, TensorflowUNet3Plus, Tenso...