Update prepare_PhysioNetP300.py

datasets/downstream/prepare_PhysioNetP300.py

@@ -14,7 +14,8 @@
 tmax=2
 for sub in [2,3,4,5,6,7,9,11]:
 
-    path = "erp-based-brain-computer-interface-recordings-1.0.0/files/s{:02d}".format(sub)
+    #path = "erp-based-brain-computer-interface-recordings-1.0.0/files/s{:02d}".format(sub)
+    path="erp-based-brain-computer-interface-recordings-1.0.0/files/erpbci/1.0.0/s{:02d}".format(sub) # based on the current state of the dataset
     for file in os.listdir(path):
         if not file.endswith(".edf"):continue
         raw = mne.io.read_raw_edf(os.path.join(path, file))
@@ -44,7 +45,7 @@
             # -- save
             x = torch.tensor(d*1e3)
             y = label
-            spath = dataset_fold+f'{y}/'
-            os.makedirs(path,exist_ok=True)
-            spath = spath + f'{i}.sub{sub}'
+            spath = os.path.join(dataset_fold, f'{y}')
+            os.makedirs(spath, exist_ok=True)  # create the correct folder
+            spath = os.path.join(spath, f'{i}.sub{sub}.pt')  # add .pt for PyTorch files
             torch.save(x, spath)

datasets/downstream/readme.md

@@ -60,9 +60,9 @@ datasets/downstream/KaggleERN/test/Data_S25_Sess05.csv
 
 PhysioP300 datasets can be downloaded from https://physionet.org/content/erpbci/1.0.0/ and save into the `datasets/downstream/erp-based-brain-computer-interface-recordings-1.0.0` folder, which organized as:
 ```
-datasets/downstream/erp-based-brain-computer-interface-recordings-1.0.0/files/s01/rc01.edf
+datasets/downstream/erp-based-brain-computer-interface-recordings-1.0.0/files/erpbci/1.0.0/s01/rc01.edf
 ...
-datasets/downstream/erp-based-brain-computer-interface-recordings-1.0.0/files/s11/rc01.edf
+datasets/downstream/erp-based-brain-computer-interface-recordings-1.0.0/files/erpbci/1.0.0/s11/rc01.edf
 ...
 Then run the following command to preprocess the data:
 ```preprocess
@@ -74,4 +74,4 @@ For preparing Sleep-EDF dataset, you can run the following command to preprocess
 ```preprocess
 cd datasets/downstream
 python prepare_sleep.py
-```
+```

downstream/finetune_BENDR_PhysioP300.py

@@ -177,21 +177,43 @@ def configure_optimizers(self):
 global steps_per_epoch
 global max_lr
 
+class P300SubjectDataset(Dataset):
+    def __init__(self, root, subjects):
+        """
+        root: path to PhysioNetP300 folder
+        subjects: list of subject numbers to include, e.g. [1,2,3]
+        """
+        self.files = []
+        self.labels = []
+
+        for label in ['0','1']:
+            folder = os.path.join(root, label)
+            for f in os.listdir(folder):
+                if f.endswith('.pt') and any(f".sub{s}" in f for s in subjects):
+                    self.files.append(os.path.join(folder, f))
+                    self.labels.append(int(label))
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, idx):
+        x = torch.load(self.files[idx])
+        y = self.labels[idx]
+        x = x.float()  # Convert to float32
+        return x, y
+
 batch_size=64
 max_epochs = 100
 
 
 all_subjects = [1,2,3,4,5,6,7,9,11]
-for i,sub in enumerate(all_subjects):
-    sub_train = [f".sub{x}" for x in all_subjects if x!=sub]
-    sub_valid = [f".sub{sub}"]
-    print(sub_train, sub_valid)
-    train_dataset = torchvision.datasets.DatasetFolder(root="../datasets/downstream/PhysioNetP300", loader=torch.load, extensions=sub_train)
-    valid_dataset = torchvision.datasets.DatasetFolder(root="../datasets/downstream/PhysioNetP300", loader=torch.load, extensions=sub_valid)
+for val_sub in (all_subjects):
 
+    train_subs = [s for s in all_subjects if s != val_sub] # all except the current
+    train_dataset = P300SubjectDataset(root="../datasets/downstream/PhysioNetP300", subjects=train_subs)
+    val_dataset = P300SubjectDataset(root="../datasets/downstream/PhysioNetP300", subjects=[val_sub])
     train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=8, shuffle=True)
-    valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=batch_size, num_workers=8, shuffle=False)
-
+    valid_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, num_workers=8, shuffle=False)
     steps_per_epoch = math.ceil(len(train_loader))
 
     # init model
@@ -204,7 +226,7 @@ def configure_optimizers(self):
     trainer = pl.Trainer(accelerator='cuda',
                         max_epochs=max_epochs, 
                         callbacks=callbacks,
-                        logger=[pl_loggers.TensorBoardLogger('./logs/', name="BENDR_PhysioP300_tb", version=f"subject{sub}"), 
+                        logger=[pl_loggers.TensorBoardLogger('./logs/', name="BENDR_PhysioP300_tb", version=f"subject{val_sub}"), 
                                 pl_loggers.CSVLogger('./logs/', name="BENDR_PhysioP300_csv")])
 
-    trainer.fit(model, train_loader, valid_loader, ckpt_path='last')
+    trainer.fit(model, train_loader, valid_loader, ckpt_path='last')