Implement core functionality for PyMoBI library including configurati…

…on, data handling, and preprocessing pipeline

__init__.py

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+
+from .core.config import PyMoBIConfig
+from .core.data import PyMoBIData
+from .preprocessing.basic import create_mobile_pipeline
+
+__all__ = ['PyMoBIConfig', 'PyMoBIData', 'create_mobile_pipeline']

core/config.py

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+# pymobi/core/config.py
+
+from dataclasses import dataclass
+from typing import Dict, Any
+
+@dataclass
+class PyMoBIConfig:
+    """Configuration management for mobile EEG processing."""
+    mne_preprocessing: Dict[str, Any]
+    motion_preprocessing: Dict[str, Any]
+    custom_processing: Dict[str, Any]
+    visualization: Dict[str, Any]

core/data.py

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+# pymobi/core/data.py
+
+import mne
+
+class PyMoBIData:
+    """Data container for EEG and motion data."""
+
+    def __init__(self, raw_mne: mne.io.Raw, motion_data=None):
+        self.mne_raw = raw_mne  # MNE Raw object for EEG data.
+        self.motion_data = motion_data  # Optional motion data.
+        self.processing_history = []  # Track all processing steps.
+
+    def add_processing_step(self, step_name: str, params: dict):
+        """Record a processing step."""
+        self.processing_history.append({"step": step_name, "params": params})
+
+    def to_mne(self):
+        """Return the MNE Raw object."""
+        return self.mne_raw
+
+    def sync_motion_events(self):
+        """Synchronize motion events with EEG data."""
+        if self.motion_data:
+            # Implement synchronization logic here.
+            pass

core/pipeline.py

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+# pymobi/core/pipeline.py
+
+from abc import ABC, abstractmethod
+
+class ProcessingStep(ABC):
+    """Abstract base class for all processing steps."""
+
+    @abstractmethod
+    def run(self, data, config):
+        pass
+
+class PreprocessingPipeline:
+    """Pipeline to manage and run preprocessing steps."""
+
+    def __init__(self):
+        self.steps = []
+
+    def add_step(self, step: ProcessingStep):
+        """Add a processing step to the pipeline."""
+        self.steps.append(step)
+
+    def run(self, data, config):
+        """Run all steps in the pipeline."""
+        for step in self.steps:
+            data = step.run(data, config)
+        return data

examples/basic_preprocessing.py

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+# examples/basic_preprocessing.py
+
+import mne
+from pymobi.core.config import PyMoBIConfig
+from pymobi.core.data import PyMoBIData
+from pymobi.preprocessing.basic import create_mobile_pipeline
+
+def main():
+    # Load sample EEG data using MNE.
+    raw = mne.io.read_raw_fif('sample_data.fif')
+
+    # Create PyMoBIData container.
+    eeg_data = PyMoBIData(raw)
+
+    # Define configuration for preprocessing.
+    config = PyMoBIConfig(
+        mne_preprocessing={'l_freq': 1.0, 'h_freq': 40.0},
+        motion_preprocessing={},
+        custom_processing={},
+        visualization={}
+    )
+
+    # Create and run the preprocessing pipeline.
+    pipeline = create_mobile_pipeline(config)
+    processed_data = pipeline.run(eeg_data)
+
+    # Save or visualize the processed data.
+    processed_data.to_mne().save('processed_eeg.fif')
+
+if __name__ == "__main__":
+    main()

preprocessing/basic.py

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+# pymobi/preprocessing/basic.py
+
+import mne
+
+class BasicPreprocessing:
+    """Basic EEG preprocessing using MNE functions."""
+
+    def __init__(self, l_freq=1.0, h_freq=40.0):
+        self.l_freq = l_freq  # Low frequency cut-off.
+        self.h_freq = h_freq  # High frequency cut-off.
+
+    def run(self, data, config):
+        """Run basic preprocessing (filtering)."""
+        data.mne_raw.filter(l_freq=self.l_freq, h_freq=self.h_freq)
+        return data
+
+
+def create_mobile_pipeline(config):
+    """Create a mobile EEG preprocessing pipeline."""
+    pipeline = PreprocessingPipeline()
+
+    # Add basic preprocessing step.
+    pipeline.add_step(BasicPreprocessing(
+        l_freq=config.mne_preprocessing['l_freq'], 
+        h_freq=config.mne_preprocessing['h_freq']
+    ))
+
+    return pipeline

preprocessing/pyamica.py

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+# pymobi/preprocessing/pyamica.py
+
+class PyAMICA:
+    """Python implementation of AMICA (Adaptive Mixture ICA)."""
+
+    def run(self, data, config):
+        """Run AMICA for ICA decomposition."""
+        # Implement AMICA logic here.
+        return data

preprocessing/pyasr.py

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+# pymobi/preprocessing/pyasr.py
+
+class PyASR:
+    """Python implementation of Artifact Subspace Reconstruction (ASR)."""
+
+    def run(self, data, config):
+        """Run ASR to clean EEG artifacts."""
+        # Implement ASR logic here.
+        return data

preprocessing/pyzapline.py

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+# pymobi/preprocessing/pyzapline.py
+
+class PyZaplinePlus:
+    """Python implementation of the Zapline+ algorithm."""
+
+    def run(self, data, config):
+        """Run Zapline+ on the EEG data to remove line noise."""
+        # Implement Zapline+ logic here.
+        return data

setup.py

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+from setuptools import setup, find_packages
+
+setup(
+    name='PyMoBI',
+    version='0.1.0',
+    author='Your Name',
+    author_email='[email protected]',
+    description='A Python library for mobile EEG data analysis',
+    long_description=open('README.md').read(),
+    long_description_content_type='text/markdown',
+    url='https://github.com/yourusername/PyMoBI',
+    packages=find_packages(),
+    install_requires=[
+        'mne>=0.23.0',
+        'numpy>=1.19.0',
+        'scipy>=1.5.0',
+        'matplotlib>=3.2.0',
+        'pandas>=1.0.0'
+    ],
+    classifiers=[
+        'Programming Language :: Python :: 3',
+        'Intended Audience :: Science/Research',
+        'Topic :: Scientific/Engineering :: Information Analysis',
+        'License :: OSI Approved :: MIT License',
+        'Operating System :: OS Independent',
+    ],
+    python_requires='>=3.8',
+)