update

app.py

@@ -62,6 +62,11 @@ icunet = """
 ### Abstract
 Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain–computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end-to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting.
 """
+unetpp = """
+# IC-U-Net++
+### Abstract
+Electroencephalographic (EEG) data is considered contaminated with various types of artifacts. Deep learning has been successfully applied to developing EEG artifact removal techniques to increase the signal-to-noise ratio (SNR) and enhance brain-computer interface performance. Recently, our research team has proposed an end-to-end UNet-based EEG artifact removal technique, IC-U-Net, which can reconstruct signals against various artifacts. However, this model suffers from being prone to overfitting with a limited training dataset size and demanding a high computational cost. To address these issues, this study attempted to leverage the architecture of UNet++ to improve the practicability of IC-U-Net by introducing dense skip connections in the encoder-decoder architecture. Results showed that this proposed model obtained superior SNR to the original model with half the number of parameters. Also, this proposed model achieved comparable convergency using a quarter of the training data size.
+"""
 
 chkbox_js = """
 (state_json) => {
@@ -146,8 +151,8 @@ with gr.Blocks() as demo:
 				"""
 			)
 			with gr.Row():
-				in_model_name = gr.Dropdown(choices=["ICUNet", "UNetpp", "AttUnet", "EEGART", "(mapped data)"],
-									value="ICUNet",
+				in_model_name = gr.Dropdown(choices=["IC-U-Net", "IC-U-Net++", "IC-U-Net-Attn", "ART", "(mapped data)"],
+									value="IC-U-Net",
 									label="Model",
 									scale=2)
 				run_btn = gr.Button(scale=1, interactive=False)
@@ -155,12 +160,12 @@ with gr.Blocks() as demo:
 	
 				
 	with gr.Row():
-		with gr.Tab("EEGART"):
+		with gr.Tab("ART"):
 			gr.Markdown()
 		with gr.Tab("IC-U-Net"):
 			gr.Markdown(icunet)
 		with gr.Tab("IC-U-Net++"):
-			gr.Markdown()
+			gr.Markdown(unetpp)
 		with gr.Tab("IC-U-Net-Att"):
 			gr.Markdown()
 		with gr.Tab("QuickStart"):