""" Neural Recording Hardware Interface Simulates hardware control and data acquisition systems """ import math import json import datetime import hashlib from typing import Dict, List, Optional, Any, Union, Tuple from dataclasses import dataclass import itertools @dataclass class HardwareConfig: """Hardware configuration parameters""" device_id: str device_type: str # 'eeg_amplifier', 'electrophys_system', 'fmri_interface' max_channels: int max_sampling_rate: int input_impedance: float # MΩ noise_floor: float # μV quantum_enhanced: bool @dataclass class ChannelConfig: """Individual channel configuration""" channel_id: str channel_type: str # 'EEG', 'LFP', 'Spike', 'BOLD' sampling_rate: int gain: float filter_settings: Dict[str, float] electrode_impedance: float # kΩ class HardwareInterface: """Neural recording hardware interface controller""" def __init__(self): self.connected_devices = {} self.active_channels = {} self.data_buffers = {} self.system_status = { 'power_on': False, 'grounding_ok': True, 'temperature': 25.0, # °C 'quantum_coherence': 0.95 } self.hardware_database = self._initialize_hardware_database() def _initialize_hardware_database(self) -> Dict[str, HardwareConfig]: """Initialize available hardware configurations""" return { 'eeg_amplifier_32': HardwareConfig( device_id='EEG-AMP-032', device_type='eeg_amplifier', max_channels=32, max_sampling_rate=2000, input_impedance=10.0, noise_floor=0.5, quantum_enhanced=True ), 'eeg_amplifier_64': HardwareConfig( device_id='EEG-AMP-064', device_type='eeg_amplifier', max_channels=64, max_sampling_rate=5000, input_impedance=12.0, noise_floor=0.3, quantum_enhanced=True ), 'electrophys_128': HardwareConfig( device_id='EPHYS-128', device_type='electrophys_system', max_channels=128, max_sampling_rate=30000, input_impedance=1.0, noise_floor=2.0, quantum_enhanced=True ), 'electrophys_256': HardwareConfig( device_id='EPHYS-256', device_type='electrophys_system', max_channels=256, max_sampling_rate=50000, input_impedance=0.5, noise_floor=1.5, quantum_enhanced=True ), 'fmri_interface': HardwareConfig( device_id='fMRI-INT', device_type='fmri_interface', max_channels=1, max_sampling_rate=10, input_impedance=50.0, noise_floor=0.1, quantum_enhanced=False ) } def connect_device(self, device_type: str) -> Dict[str, Any]: """Connect to hardware device""" # Find available device of requested type available_devices = [dev_id for dev_id, config in self.hardware_database.items() if config.device_type == device_type] if not available_devices: return { 'success': False, 'error': f'No available {device_type} devices', 'connected_device': None } # Connect to first available device device_id = available_devices[0] device_config = self.hardware_database[device_id] # Simulate connection process connection_result = self._simulate_connection(device_config) if connection_result['success']: self.connected_devices[device_id] = { 'config': device_config, 'connection_time': datetime.datetime.now().isoformat(), 'status': 'connected', 'impedance_check': connection_result['impedance_check'] } return connection_result def _simulate_connection(self, config: HardwareConfig) -> Dict[str, Any]: """Simulate device connection and initialization""" # Simulate impedance check impedance_results = {} for i in range(config.max_channels): impedance = 10 + (hash(f'imp_{config.device_id}_{i}') % 90) # 10-100 kΩ impedance_results[f'CH{i+1:02d}'] = impedance # Check if all impedances are acceptable acceptable_impedance = config.input_impedance * 100 # Convert to kΩ all_good = all(imp <= acceptable_impedance for imp in impedance_results.values()) return { 'success': all_good, 'device_id': config.device_id, 'impedance_check': impedance_results, 'connection_time': datetime.datetime.now().isoformat(), 'quantum_coherence': self.system_status['quantum_coherence'] if config.quantum_enhanced else None } def configure_channels(self, device_id: str, channel_configs: List[ChannelConfig]) -> Dict[str, Any]: """Configure recording channels""" if device_id not in self.connected_devices: return {'success': False, 'error': 'Device not connected'} device_config = self.connected_devices[device_id]['config'] # Validate channel configurations if len(channel_configs) > device_config.max_channels: return {'success': False, 'error': f'Too many channels. Max: {device_config.max_channels}'} # Check sampling rates for chan_config in channel_configs: if chan_config.sampling_rate > device_config.max_sampling_rate: return {'success': False, 'error': f'Sampling rate too high for {chan_config.channel_id}'} # Configure channels configured_channels = {} for chan_config in channel_configs: configured_channels[chan_config.channel_id] = { 'type': chan_config.channel_type, 'sampling_rate': chan_config.sampling_rate, 'gain': chan_config.gain, 'filters': chan_config.filter_settings, 'impedance': chan_config.electrode_impedance, 'status': 'configured' } self.active_channels[device_id] = configured_channels return { 'success': True, 'configured_channels': len(configured_channels), 'channel_list': list(configured_channels.keys()) } def start_recording(self, device_id: str, duration: float) -> Dict[str, Any]: """Start data acquisition""" if device_id not in self.connected_devices: return {'success': False, 'error': 'Device not connected'} if device_id not in self.active_channels: return {'success': False, 'error': 'No channels configured'} if not self.system_status['power_on']: return {'success': False, 'error': 'System power is off'} # Initialize data buffer device_config = self.connected_devices[device_id]['config'] channels = self.active_channels[device_id] self.data_buffers[device_id] = { 'start_time': datetime.datetime.now().isoformat(), 'duration': duration, 'samples_recorded': 0, 'channels': {}, 'status': 'recording' } # Simulate data acquisition recording_data = self._simulate_recording(device_config, channels, duration) self.data_buffers[device_id]['data'] = recording_data self.data_buffers[device_id]['samples_recorded'] = len(recording_data) self.data_buffers[device_id]['status'] = 'completed' return { 'success': True, 'recording_id': f"REC_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}", 'samples_recorded': len(recording_data), 'duration': duration, 'channels': len(channels) } def _simulate_recording(self, device_config: HardwareConfig, channels: Dict, duration: float) -> List[Dict]: """Simulate neural data recording""" recording_data = [] # Determine maximum sampling rate for synchronization max_rate = max(chan['sampling_rate'] for chan in channels.values()) total_samples = int(duration * max_rate) # Generate synchronized data for sample_idx in range(total_samples): timestamp = sample_idx / max_rate sample = {'timestamp': timestamp} # Generate data for each channel for chan_id, chan_config in channels.items(): if sample_idx % (max_rate // chan_config['sampling_rate']) == 0: # Generate channel-specific signal signal_value = self._generate_channel_signal(chan_config, timestamp) # Apply quantum enhancement if available if device_config.quantum_enhanced: signal_value *= self.system_status['quantum_coherence'] # Add noise noise = device_config.noise_floor * (hash(f'{chan_id}_{sample_idx}') % 100 - 50) / 100 signal_value += noise sample[chan_id] = signal_value recording_data.append(sample) return recording_data def _generate_channel_signal(self, chan_config: Dict, timestamp: float) -> float: """Generate realistic neural signal for channel type""" channel_type = chan_config['type'] sampling_rate = chan_config['sampling_rate'] if channel_type == 'EEG': # Simulate EEG with multiple frequency bands alpha = 10 * math.sin(2 * math.pi * 10 * timestamp) beta = 5 * math.sin(2 * math.pi * 20 * timestamp) gamma = 2 * math.sin(2 * math.pi * 40 * timestamp) return alpha + beta + gamma elif channel_type == 'LFP': # Local field potential - slower oscillations theta = 15 * math.sin(2 * math.pi * 6 * timestamp) delta = 10 * math.sin(2 * math.pi * 2 * timestamp) return theta + delta elif channel_type == 'Spike': # Action potentials - brief spikes spike_prob = 0.001 * sampling_rate # Spikes per second if (hash(f'spike_{timestamp:.6f}') % 1000) < spike_prob * 1000: # Generate spike waveform spike_duration = 0.002 # 2ms if timestamp % 0.1 < spike_duration: return 50 * math.exp(-((timestamp % spike_duration) ** 2) / (spike_duration ** 2) * 4) return 0.5 * (hash(f'noise_{timestamp:.6f}') % 100 - 50) / 1000 elif channel_type == 'BOLD': # fMRI BOLD signal - very slow changes hrf = math.exp(-((timestamp - 5) ** 2) / 8) return hrf return 0.0 def stop_recording(self, device_id: str) -> Dict[str, Any]: """Stop data acquisition""" if device_id not in self.data_buffers: return {'success': False, 'error': 'No active recording'} recording_data = self.data_buffers[device_id] recording_data['status'] = 'stopped' recording_data['stop_time'] = datetime.datetime.now().isoformat() return { 'success': True, 'recording_id': f"REC_{recording_data['start_time'][:10].replace('-', '')}", 'samples_recorded': recording_data['samples_recorded'], 'data_hash': hashlib.sha256(json.dumps(recording_data['data']).encode()).hexdigest()[:16] } def apply_quantum_filter(self, device_id: str, filter_type: str = 'coherence') -> Dict[str, Any]: """Apply quantum-based filtering to recorded data""" if device_id not in self.data_buffers: return {'success': False, 'error': 'No recording data available'} data_buffer = self.data_buffers[device_id] if 'data' not in data_buffer: return {'success': False, 'error': 'No data in buffer'} recording_data = data_buffer['data'] filtered_data = [] coherence = self.system_status['quantum_coherence'] for sample in recording_data: filtered_sample = sample.copy() if filter_type == 'coherence': # Quantum coherence enhancement for key, value in sample.items(): if key != 'timestamp': filtered_sample[key] = value * coherence elif filter_type == 'entanglement': # Quantum entanglement-based denoising for key, value in sample.items(): if key != 'timestamp': # Reduce noise through quantum correlation noise_reduction = 0.8 filtered_sample[key] = value * noise_reduction filtered_data.append(filtered_sample) data_buffer['filtered_data'] = filtered_data data_buffer['filter_applied'] = filter_type return { 'success': True, 'samples_processed': len(filtered_data), 'filter_type': filter_type, 'quantum_coherence': coherence } def system_diagnostics(self) -> Dict[str, Any]: """Run system diagnostics""" diagnostics = { 'timestamp': datetime.datetime.now().isoformat(), 'system_status': self.system_status.copy(), 'connected_devices': len(self.connected_devices), 'active_channels': sum(len(channels) for channels in self.active_channels.values()), 'data_buffers': len(self.data_buffers), 'diagnostics_results': {} } # Check power system diagnostics['diagnostics_results']['power'] = { 'status': 'OK' if self.system_status['power_on'] else 'OFF', 'voltage': 12.0 if self.system_status['power_on'] else 0.0, 'current': 2.5 if self.system_status['power_on'] else 0.0 } # Check grounding diagnostics['diagnostics_results']['grounding'] = { 'status': 'OK' if self.system_status['grounding_ok'] else 'FAULT', 'resistance': 0.1 if self.system_status['grounding_ok'] else 10.0 } # Check temperature temp = self.system_status['temperature'] diagnostics['diagnostics_results']['temperature'] = { 'status': 'OK' if 15 <= temp <= 35 else 'WARNING', 'current_temp': temp, 'optimal_range': (15, 35) } # Check quantum systems quantum_devices = sum(1 for device in self.connected_devices.values() if device['config'].quantum_enhanced) if quantum_devices > 0: coherence = self.system_status['quantum_coherence'] diagnostics['diagnostics_results']['quantum_systems'] = { 'status': 'OK' if coherence > 0.8 else 'DEGRADED', 'coherence_level': coherence, 'quantum_devices': quantum_devices } return diagnostics def power_on_system(self) -> Dict[str, Any]: """Power on the recording system""" self.system_status['power_on'] = True self.system_status['temperature'] = 25.0 return { 'success': True, 'message': 'System powered on successfully', 'timestamp': datetime.datetime.now().isoformat() } def power_off_system(self) -> Dict[str, Any]: """Power off the recording system""" # Stop all recordings for device_id in list(self.data_buffers.keys()): self.stop_recording(device_id) self.system_status['power_on'] = False self.system_status['temperature'] = 20.0 return { 'success': True, 'message': 'System powered off safely', 'timestamp': datetime.datetime.now().isoformat() } def test_hardware_interface(): """Test the hardware interface""" print("Testing Neural Recording Hardware Interface...") interface = HardwareInterface() # Test system power print("\n1. Testing system power...") power_on = interface.power_on_system() print(f"Power on: {power_on['success']}") # Test device connection print("\n2. Testing device connections...") eeg_connection = interface.connect_device('eeg_amplifier') print(f"EEG amplifier connection: {eeg_connection['success']}") ephys_connection = interface.connect_device('electrophys_system') print(f"Electrophysiology connection: {ephys_connection['success']}") # Get connected device IDs eeg_device_id = list(interface.connected_devices.keys())[0] ephys_device_id = list(interface.connected_devices.keys())[1] # Test channel configuration print("\n3. Testing channel configuration...") from cross_species_protocols import SpeciesParameters # Configure EEG channels eeg_channels = [ ChannelConfig('Fz', 'EEG', 1000, 1000, {'high_pass': 0.1, 'low_pass': 100}, 5.0), ChannelConfig('Cz', 'EEG', 1000, 1000, {'high_pass': 0.1, 'low_pass': 100}, 5.0), ChannelConfig('Pz', 'EEG', 1000, 1000, {'high_pass': 0.1, 'low_pass': 100}, 5.0) ] eeg_config = interface.configure_channels(eeg_device_id, eeg_channels) print(f"EEG channel configuration: {eeg_config['success']}, channels: {eeg_config.get('configured_channels', 0)}") # Configure electrophysiology channels ephys_channels = [ ChannelConfig('ch1', 'Spike', 30000, 5000, {'high_pass': 300, 'low_pass': 6000}, 100.0), ChannelConfig('ch2', 'LFP', 1000, 1000, {'high_pass': 1, 'low_pass': 300}, 100.0) ] ephys_config = interface.configure_channels(ephys_device_id, ephys_channels) print(f"Electrophysiology configuration: {ephys_config['success']}, channels: {ephys_config.get('configured_channels', 0)}") # Test recording print("\n4. Testing data recording...") eeg_recording = interface.start_recording(eeg_device_id, 2.0) # 2 seconds print(f"EEG recording: {eeg_recording['success']}, samples: {eeg_recording.get('samples_recorded', 0)}") ephys_recording = interface.start_recording(ephys_device_id, 1.0) # 1 second print(f"Electrophysiology recording: {ephys_recording['success']}, samples: {ephys_recording.get('samples_recorded', 0)}") # Test quantum filtering print("\n5. Testing quantum filtering...") eeg_filter = interface.apply_quantum_filter(eeg_device_id, 'coherence') print(f"Quantum filtering: {eeg_filter['success']}, coherence: {eeg_filter.get('quantum_coherence', 0):.3f}") # Test system diagnostics print("\n6. Testing system diagnostics...") diagnostics = interface.system_diagnostics() print(f"Diagnostics - Devices: {diagnostics['connected_devices']}, Channels: {diagnostics['active_channels']}") print(f"Power: {diagnostics['diagnostics_results']['power']['status']}") print(f"Temperature: {diagnostics['diagnostics_results']['temperature']['status']} ({diagnostics['diagnostics_results']['temperature']['current_temp']}°C)") # Test power off print("\n7. Testing system power off...") power_off = interface.power_off_system() print(f"Power off: {power_off['success']}") return True if __name__ == "__main__": test_hardware_interface()