Upload load_dataset.py

load_dataset.py

@@ -1,62 +1,49 @@
 import json
-import pandas as pd
-from datasets import Dataset, DatasetDict
+import datasets
 
-def load_bci_fps_dataset(data_dir):
-    """
-    Load BCI-FPS dataset for Hugging Face.
-    
-    Args:
-        data_dir (str): Path to dataset directory
-    
-    Returns:
-        DatasetDict: Hugging Face dataset
-    """
-    # Load neural data
-    neural_data = []
-    with open(f"{data_dir}/neural_data.jsonl", 'r') as f:
-        for line in f:
-            if line.strip():
-                neural_data.append(json.loads(line))
-    
-    # Load intent stream
-    intent_stream = []
-    with open(f"{data_dir}/intent_stream.jsonl", 'r') as f:
-        for line in f:
-            if line.strip():
-                intent_stream.append(json.loads(line))
-    
-    # Create datasets
-    datasets = {
-        "neural_data": Dataset.from_list(neural_data),
-        "intent_stream": Dataset.from_list(intent_stream)
-    }
-    
-    # Load handwriting samples if exists
-    try:
-        with open(f"{data_dir}/handwriting_samples.json", 'r') as f:
-            handwriting = json.load(f)
-        datasets["handwriting"] = Dataset.from_list(handwriting)
-    except:
-        pass
-    
-    # Load metadata
-    with open(f"{data_dir}/metadata.json", 'r') as f:
-        metadata = json.load(f)
-    
-    dataset_dict = DatasetDict(datasets)
-    dataset_dict.info.metadata = metadata
-    
-    return dataset_dict
+_DESCRIPTION = "Neural data with intent context"
+_CITATION = ""
+_HOMEPAGE = ""
+_LICENSE = ""
 
-# Example usage for Neuralink research
-if __name__ == "__main__":
-    dataset = load_bci_fps_dataset("./bci_data")
-    
-    print(f"Dataset keys: {list(dataset.keys())}")
-    print(f"Neural data samples: {len(dataset['neural_data'])}")
-    print(f"Intent stream samples: {len(dataset['intent_stream'])}")
-    
-    # Example: Extract motor imagery trials
-    motor_trials = [d for d in dataset['neural_data'] if d.get('type') == 'motor_imagery_trial']
-    print(f"Motor imagery trials: {len(motor_trials)}")
+class NeuralData(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "timestamp": datasets.Value("int64"),
+                "session_time": datasets.Value("int64"),
+                "channels": {
+                    f"channel_{i}": datasets.Value("float64") for i in range(32)
+                },
+                "intent_context": {
+                    "mouse_movement": datasets.Sequence(datasets.Value("int64")),
+                    "keyboard_state": {
+                        "mouse": datasets.Value("bool")
+                    },
+                    "camera_rotation": datasets.Sequence(datasets.Value("float64")),
+                    "active_targets": datasets.Value("int64")
+                }
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+    
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": "neural_data.txt"}
+            )
+        ]
+    
+    def _generate_examples(self, filepath):
+        with open(filepath, "r") as f:
+            for idx, line in enumerate(f):
+                if line.strip():
+                    data = json.loads(line)
+                    yield idx, data