""" Multi-Modal Neural Recording System Integrates EEG, fMRI, and electrophysiology recordings """ import math import json import datetime import hashlib from typing import Dict, List, Optional, Any, Union from dataclasses import dataclass import itertools @dataclass class RecordingConfig: """Configuration for neural recording systems""" sample_rate: int # Hz channels: List[str] duration: float # seconds species: str class MultiModalRecorder: """Multi-modal neural recording system""" def __init__(self): self.recording_active = False self.data_buffer = [] self.configs = { 'eeg': RecordingConfig(1000, ['Fz', 'Cz', 'Pz'], 60.0, 'human'), 'fmri': RecordingConfig(1, ['BOLD'], 300.0, 'human'), 'electrophys': RecordingConfig(30000, ['ch1', 'ch2', 'ch3'], 30.0, 'rodent') } self.quantum_state = self._initialize_quantum_state() def _initialize_quantum_state(self) -> Dict[str, float]: """Initialize quantum measurement state for quantum sensing""" return { 'coherence': 0.95, 'entanglement': 0.87, 'superposition': 0.92 } def record_eeg(self, config: RecordingConfig) -> List[Dict]: """Generate simulated EEG data""" samples = int(config.sample_rate * config.duration) eeg_data = [] for t in range(samples): timestamp = t / config.sample_rate sample = {} for channel in config.channels: # Simulate neural oscillations alpha = 10 * math.sin(2 * math.pi * 10 * timestamp) # Alpha band beta = 5 * math.sin(2 * math.pi * 20 * timestamp) # Beta band gamma = 2 * math.sin(2 * math.pi * 40 * timestamp) # Gamma band noise = 0.5 * (hash(str(t)) % 100 - 50) / 100 sample[channel] = alpha + beta + gamma + noise sample['timestamp'] = timestamp sample['quantum_coherence'] = self.quantum_state['coherence'] eeg_data.append(sample) return eeg_data def record_fmri(self, config: RecordingConfig) -> List[Dict]: """Generate simulated fMRI BOLD signal""" samples = int(config.sample_rate * config.duration) fmri_data = [] for t in range(samples): timestamp = t / config.sample_rate # Hemodynamic response function simulation hrf = math.exp(-((timestamp - 5) ** 2) / 8) + 0.1 * math.sin(2 * math.pi * 0.1 * timestamp) sample = { 'BOLD': hrf + 0.05 * (hash(str(t)) % 100 - 50) / 100, 'timestamp': timestamp, 'quantum_entanglement': self.quantum_state['entanglement'] } fmri_data.append(sample) return fmri_data def record_electrophys(self, config: RecordingConfig) -> List[Dict]: """Generate simulated electrophysiology data""" samples = int(config.sample_rate * config.duration) electrophys_data = [] spike_times = sorted([hash(f'spike_{i}') % samples for i in range(100)]) for t in range(samples): timestamp = t / config.sample_rate sample = {} for i, channel in enumerate(config.channels): baseline_noise = 0.1 * (hash(f'{t}_{channel}') % 100 - 50) / 100 if t in spike_times[:20 + i * 10]: # Action potential spike = 5 * math.exp(-((timestamp % 0.002) ** 2) / 0.0001) else: spike = 0 sample[channel] = baseline_noise + spike sample['timestamp'] = timestamp sample['quantum_superposition'] = self.quantum_state['superposition'] electrophys_data.append(sample) return electrophys_data def record_multi_modal(self) -> Dict[str, List[Dict]]: """Simultaneous multi-modal recording""" recordings = {} # Start all recordings recordings['eeg'] = self.record_eeg(self.configs['eeg']) recordings['fmri'] = self.record_fmri(self.configs['fmri']) recordings['electrophysiology'] = self.record_electrophys(self.configs['electrophys']) return recordings def apply_quantum_filter(self, data: List[Dict], modality: str) -> List[Dict]: """Apply quantum-inspired filtering to enhance signal quality""" filtered_data = [] for sample in data: enhanced_sample = sample.copy() if modality == 'eeg': # Quantum coherence enhancement coherence_factor = self.quantum_state['coherence'] for channel in ['Fz', 'Cz', 'Pz']: if channel in sample: enhanced_sample[channel] *= coherence_factor elif modality == 'fmri': # Quantum entanglement enhancement entanglement_factor = self.quantum_state['entanglement'] if 'BOLD' in sample: enhanced_sample['BOLD'] *= entanglement_factor elif modality == 'electrophysiology': # Quantum superposition enhancement superposition_factor = self.quantum_state['superposition'] for channel in ['ch1', 'ch2', 'ch3']: if channel in sample: enhanced_sample[channel] *= superposition_factor filtered_data.append(enhanced_sample) return filtered_data def save_recording(self, data: Dict, filename: str): """Save recording data to file""" recording_metadata = { 'timestamp': datetime.datetime.now().isoformat(), 'modalities': list(data.keys()), 'total_samples': sum(len(modality_data) for modality_data in data.values()), 'quantum_state': self.quantum_state, 'data_hash': hashlib.sha256(json.dumps(data).encode()).hexdigest()[:16] } output = { 'metadata': recording_metadata, 'recordings': data } # In sandbox, we'll just return the structured data return output def test_multimodal_recording(): """Test the multi-modal recording system""" recorder = MultiModalRecorder() # Test individual modalities print("Testing individual modalities...") eeg_data = recorder.record_eeg(recorder.configs['eeg']) print(f"EEG: {len(eeg_data)} samples, channels: {list(eeg_data[0].keys())}") fmri_data = recorder.record_fmri(recorder.configs['fmri']) print(f"fMRI: {len(fmri_data)} samples") electrophys_data = recorder.record_electrophys(recorder.configs['electrophys']) print(f"Electrophysiology: {len(electrophys_data)} samples") # Test multi-modal recording print("\nTesting multi-modal recording...") multimodal_data = recorder.record_multi_modal() for modality, data in multimodal_data.items(): print(f"{modality}: {len(data)} samples") # Test quantum filtering print("\nTesting quantum filtering...") filtered_eeg = recorder.apply_quantum_filter(eeg_data, 'eeg') print(f"Filtered EEG: {len(filtered_eeg)} samples") # Save recording print("\nSaving recording...") saved_data = recorder.save_recording(multimodal_data, "test_recording.json") print(f"Recording saved with hash: {saved_data['metadata']['data_hash']}") return True if __name__ == "__main__": test_multimodal_recording()