""" Integrated Multi-Modal Neural Recording System Combines recording hardware, real-time analysis, cross-species protocols, and database management """ import math import json import datetime import hashlib from typing import Dict, List, Optional, Any, Union, Tuple from dataclasses import dataclass import itertools import collections # Import system components from neural_recording_system import MultiModalRecorder from realtime_analysis import RealTimeAnalyzer, AnalysisConfig from cross_species_protocols import CrossSpeciesProtocols from neural_database import NeuralDatabase, RecordingMetadata, DataQualityMetrics @dataclass class SystemConfiguration: """Configuration for the integrated neural recording system""" enable_quantum_enhancement: bool real_time_processing: bool auto_quality_assessment: bool cross_species_mode: bool database_backup_interval: float # hours class IntegratedNeuralSystem: """Integrated multi-modal neural recording system""" def __init__(self, config: SystemConfiguration): self.config = config self.recorder = MultiModalRecorder() self.analysis_config = AnalysisConfig( window_size=1.0, overlap=0.5, ml_model_type='quantum_nn' if config.enable_quantum_enhancement else 'classical', processing_delay=0.1 ) self.analyzer = RealTimeAnalyzer(self.analysis_config) self.protocols = CrossSpeciesProtocols() self.database = NeuralDatabase() self.active_recordings = {} self.system_metrics = { 'total_recordings': 0, 'successful_analyses': 0, 'quality_issues': 0, 'system_uptime': datetime.datetime.now().isoformat() } def setup_recording_session(self, species: str, modalities: List[str], experiment_type: str, researcher_id: str, ethical_approval: str) -> Dict[str, Any]: """Setup a new recording session with cross-species protocols""" session_id = hashlib.sha256(f"{species}_{experiment_type}_{researcher_id}_{datetime.datetime.now().isoformat()}".encode()).hexdigest()[:12] session_info = { 'session_id': session_id, 'species': species, 'modalities': modalities, 'experiment_type': experiment_type, 'researcher_id': researcher_id, 'ethical_approval': ethical_approval, 'setup_timestamp': datetime.datetime.now().isoformat(), 'protocols': {} } # Generate protocols for each modality for modality in modalities: try: protocol = self.protocols.generate_recording_protocol(species, modality, experiment_type) validation = self.protocols.validate_protocol(protocol) session_info['protocols'][modality] = { 'protocol': protocol, 'validation': validation } except Exception as e: session_info['protocols'][modality] = {'error': str(e)} self.active_recordings[session_id] = session_info return session_info def execute_recording_session(self, session_id: str) -> Dict[str, Any]: """Execute a configured recording session""" if session_id not in self.active_recordings: return {'error': 'Invalid session ID'} session = self.active_recordings[session_id] execution_results = { 'session_id': session_id, 'execution_start': datetime.datetime.now().isoformat(), 'recordings': {}, 'analyses': {}, 'database_entries': [] } # Execute recordings for each modality for modality, protocol_info in session['protocols'].items(): if 'error' in protocol_info: continue try: # Record data if modality == 'eeg': data = self.recorder.record_eeg(self.recorder.configs['eeg']) elif modality == 'electrophysiology': data = self.recorder.record_electrophys(self.recorder.configs['electrophys']) elif modality == 'fmri': data = self.recorder.record_fmri(self.recorder.configs['fmri']) else: continue execution_results['recordings'][modality] = { 'data': data, 'sample_count': len(data), 'protocol': protocol_info['protocol'] } # Apply quantum enhancement if enabled if self.config.enable_quantum_enhancement: data = self.recorder.apply_quantum_filter(data, modality) execution_results['recordings'][modality]['quantum_enhanced'] = True # Real-time analysis if enabled if self.config.real_time_processing: sample_rates = {'eeg': 1000, 'electrophysiology': 30000, 'fmri': 1} sample_rate = sample_rates.get(modality, 1000) analysis_results = self.analyzer.real_time_processing(data[:10000], modality, sample_rate) execution_results['analyses'][modality] = analysis_results # Update system metrics successful_analyses = sum(1 for r in analysis_results if not r.get('real_time_violation', True)) self.system_metrics['successful_analyses'] += successful_analyses # Auto quality assessment if enabled if self.config.auto_quality_assessment: quality_metrics = self._assess_data_quality(data, modality) # Create metadata and add to database metadata = RecordingMetadata( recording_id='', species=session['species'], modality=modality, timestamp=session['setup_timestamp'], duration=protocol_info['protocol']['recording_parameters']['duration'] if isinstance(protocol_info['protocol']['recording_parameters']['duration'], (int, float)) else 60.0, sample_rate=protocol_info['protocol']['recording_parameters']['sampling_rate'], channel_count=32 if modality == 'eeg' else 64, data_size=len(str(data)), experiment_type=session['experiment_type'], researcher_id=session['researcher_id'], ethical_approval=session['ethical_approval'], quantum_enhanced=self.config.enable_quantum_enhancement ) recording_id = self.database.add_recording(metadata, data, quality_metrics) execution_results['database_entries'].append(recording_id) # Track quality issues if quality_metrics.signal_to_noise_ratio < 10: self.system_metrics['quality_issues'] += 1 except Exception as e: execution_results['recordings'][modality] = {'error': str(e)} execution_results['execution_end'] = datetime.datetime.now().isoformat() self.system_metrics['total_recordings'] += len(execution_results['recordings']) return execution_results def _assess_data_quality(self, data: List[Dict], modality: str) -> DataQualityMetrics: """Assess quality of recorded data""" if not data: return DataQualityMetrics(0, 100, 0, 0, 0, 0) # Extract signal data (exclude metadata fields) signal_channels = [] for sample in data: for key, value in sample.items(): if key not in ['timestamp', 'quantum_coherence', 'quantum_entanglement', 'quantum_superposition']: signal_channels.append(value) if not signal_channels: return DataQualityMetrics(0, 100, 0, 0, 0, 0) # Calculate SNR (simplified) signal_power = sum(x**2 for x in signal_channels) / len(signal_channels) noise_power = signal_power * 0.1 # Assume 10% noise snr = 10 * math.log10(signal_power / noise_power) if noise_power > 0 else 20.0 # Calculate artifact percentage (simplified) threshold = 3 * math.sqrt(noise_power) if noise_power > 0 else 0.1 artifacts = sum(1 for x in signal_channels if abs(x) > threshold) artifact_percentage = (artifacts / len(signal_channels)) * 100 # Data completeness (assume complete) data_completeness = 100.0 # Noise floor noise_floor = math.sqrt(noise_power) if noise_power > 0 else 0.1 # Impedance stability (simulated) impedance_stability = 95.0 + (hash(modality) % 10) # Quantum coherence (if available) quantum_coherence = 0.9 if data and 'quantum_coherence' in data[0]: quantum_coherence = data[0]['quantum_coherence'] elif self.config.enable_quantum_enhancement: quantum_coherence = 0.85 + (hash(modality) % 15) / 100 return DataQualityMetrics( signal_to_noise_ratio=snr, artifact_percentage=artifact_percentage, data_completeness=data_completeness, noise_floor=noise_floor, impedance_stability=impedance_stability, quantum_coherence=quantum_coherence ) def generate_comprehensive_report(self) -> Dict[str, Any]: """Generate comprehensive system report""" report = { 'report_timestamp': datetime.datetime.now().isoformat(), 'system_configuration': { 'quantum_enhancement': self.config.enable_quantum_enhancement, 'real_time_processing': self.config.real_time_processing, 'auto_quality_assessment': self.config.auto_quality_assessment, 'cross_species_mode': self.config.cross_species_mode }, 'system_metrics': self.system_metrics, 'database_statistics': self.database.stats_cache, 'database_report': self.database.generate_database_report(), 'cross_species_summary': self._generate_cross_species_summary(), 'recommendations': self._generate_system_recommendations() } return report def _generate_cross_species_summary(self) -> Dict[str, Any]: """Generate cross-species summary statistics""" summary = { 'species_recorded': list(self.database.metadata_index.keys()), 'modality_coverage': {}, 'species_modality_matrix': {} } # Calculate modality coverage all_modalities = set() for species_data in self.database.metadata_index.values(): all_modalities.update(species_data.keys()) for modality in all_modalities: species_with_modality = [] for species, modalities in self.database.metadata_index.items(): if modality in modalities: species_with_modality.append(species) summary['modality_coverage'][modality] = species_with_modality # Generate species-modality matrix for species in summary['species_recorded']: summary['species_modality_matrix'][species] = {} for modality in all_modalities: count = len(self.database.metadata_index.get(species, {}).get(modality, [])) summary['species_modality_matrix'][species][modality] = count return summary def _generate_system_recommendations(self) -> List[str]: """Generate system-level recommendations""" recommendations = [] # System performance recommendations if self.system_metrics['quality_issues'] > self.system_metrics['total_recordings'] * 0.2: recommendations.append("High quality issue rate detected. Review recording setup and environment") if self.system_metrics['successful_analyses'] < self.system_metrics['total_recordings'] * 0.8: recommendations.append("Low real-time analysis success rate. Consider adjusting processing parameters") # Database recommendations db_report = self.database.generate_database_report() recommendations.extend(db_report['recommendations']) # Configuration recommendations if not self.config.enable_quantum_enhancement: recommendations.append("Consider enabling quantum enhancement for improved data quality") if not self.config.auto_quality_assessment: recommendations.append("Enable auto quality assessment for automated monitoring") return recommendations def export_system_data(self, format_type: str = 'comprehensive') -> str: """Export system data in various formats""" if format_type == 'comprehensive': report = self.generate_comprehensive_report() return json.dumps(report, indent=2) elif format_type == 'database_only': return self.database.export_data_summary('json') elif format_type == 'summary': summary = { 'timestamp': datetime.datetime.now().isoformat(), 'total_recordings': self.system_metrics['total_recordings'], 'successful_analyses': self.system_metrics['successful_analyses'], 'quality_issues': self.system_metrics['quality_issues'], 'database_stats': self.database.stats_cache } return json.dumps(summary, indent=2) else: return json.dumps({"error": "Unknown export format"}, indent=2) def test_integrated_system(): """Test the integrated neural recording system""" print("Testing Integrated Multi-Modal Neural Recording System...") # Initialize system config = SystemConfiguration( enable_quantum_enhancement=True, real_time_processing=True, auto_quality_assessment=True, cross_species_mode=True, database_backup_interval=24.0 ) system = IntegratedNeuralSystem(config) # Test 1: Setup recording sessions print("\n1. Testing recording session setup...") # Human EEG session human_session = system.setup_recording_session( species='human', modalities=['eeg', 'fmri'], experiment_type='cognitive_task', researcher_id='researcher_001', ethical_approval='IRB_2024_001' ) # Mouse electrophysiology session mouse_session = system.setup_recording_session( species='mouse', modalities=['electrophysiology'], experiment_type='behavioral_task', researcher_id='researcher_002', ethical_approval='IACUC_2024_001' ) print(f"Human session ID: {human_session['session_id']}") print(f"Mouse session ID: {mouse_session['session_id']}") # Test 2: Execute recording sessions print("\n2. Testing recording execution...") human_results = system.execute_recording_session(human_session['session_id']) mouse_results = system.execute_recording_session(mouse_session['session_id']) print(f"Human recording results: {len(human_results['recordings'])} modalities") print(f"Mouse recording results: {len(mouse_results['recordings'])} modalities") print(f"Database entries created: {len(human_results['database_entries']) + len(mouse_results['database_entries'])}") # Test 3: System metrics print("\n3. Testing system metrics...") print(f"Total recordings: {system.system_metrics['total_recordings']}") print(f"Successful analyses: {system.system_metrics['successful_analyses']}") print(f"Quality issues: {system.system_metrics['quality_issues']}") # Test 4: Database functionality print("\n4. Testing database functionality...") # Search recordings human_recordings = system.database.search_recordings(species='human') print(f"Found {len(human_recordings)} human recordings") quantum_recordings = system.database.search_recordings(quantum_only=True) print(f"Found {len(quantum_recordings)} quantum-enhanced recordings") # Cross-species comparison eeg_comparison = system.database.get_cross_species_comparison('eeg') print(f"EEG cross-species data: {list(eeg_comparison['species_data'].keys())}") # Test 5: Report generation print("\n5. Testing report generation...") comprehensive_report = system.generate_comprehensive_report() print(f"Report keys: {list(comprehensive_report.keys())}") print(f"Recommendations: {comprehensive_report['recommendations']}") # Test 6: Data export print("\n6. Testing data export...") summary_export = system.export_system_data('summary') print(f"Summary export length: {len(summary_export)} characters") return True if __name__ == "__main__": test_integrated_system()