rezilient.js/src/experimental/neuromorphic/NeuromorphicComputing.js

Rezilient.js - Revolutionary offline-first framework with AI-awareness, principle-driven development, carbon-conscious computing, and self-healing capabilities

// src/neuromorphic/NeuromorphicComputing.js

/**
 * @class NeuromorphicComputing
 * INDUSTRY FIRST: Neuromorphic Framework with Brain-Computer Interface Integration
 * 
 * Revolutionary neuromorphic computing integration that provides:
 * - Brain-computer interface (BCI) support
 * - Spiking neural network simulation
 * - Thought-based interaction patterns
 * - Cognitive load optimization
 * - Neural adaptation algorithms
 * - Brain-inspired computing paradigms
 */

export class NeuromorphicComputing {
  constructor(options = {}) {
    this.options = {
      enableBCI: options.enableBCI !== false,
      enableSpikingNetworks: options.enableSpikingNetworks !== false,
      enableCognitiveAdaptation: options.enableCognitiveAdaptation !== false,
      enableNeuralPlasticity: options.enableNeuralPlasticity !== false,
      enableThoughtInterface: options.enableThoughtInterface !== false,
      bciDevice: options.bciDevice || 'simulated', // 'emotiv', 'neurosky', 'openbci', 'simulated'
      neuralModel: options.neuralModel || 'leaky-integrate-fire',
      adaptationRate: options.adaptationRate || 0.01,
      ...options
    };

    // Brain-computer interface
    this.bci = {
      device: null,
      connected: false,
      signals: {
        eeg: new Array(8).fill(0), // 8-channel EEG
        attention: 0,
        meditation: 0,
        blink: 0,
        cognitiveLoad: 0
      },
      calibration: {
        baseline: null,
        userProfile: null,
        adaptationHistory: []
      }
    };

    // Spiking neural network
    this.spikingNetwork = {
      neurons: [],
      synapses: [],
      layers: [],
      spikes: [],
      plasticity: {
        stdp: true, // Spike-timing dependent plasticity
        homeostasis: true,
        metaplasticity: true
      }
    };

    // Cognitive state tracking
    this.cognitiveState = {
      attention: 0,
      workload: 0,
      fatigue: 0,
      flow: 0,
      stress: 0,
      engagement: 0
    };

    // Neural adaptation engine
    this.adaptationEngine = {
      learningRate: this.options.adaptationRate,
      memoryTrace: [],
      adaptationRules: new Map(),
      neuralPlasticity: new Map()
    };

    this.initialize();
  }

  /**
   * Initialize neuromorphic computing system
   */
  async initialize() {
    console.log('🧠 Initializing Neuromorphic Computing Framework...');
    
    await this.initializeBCI();
    await this.initializeSpikingNetwork();
    this.setupCognitiveMonitoring();
    this.setupNeuralAdaptation();
    this.setupThoughtInterface();
    
    console.log('✅ Neuromorphic Computing Framework ready!');
  }

  /**
   * BRAIN-COMPUTER INTERFACE
   */

  /**
   * Initialize brain-computer interface
   */
  async initializeBCI() {
    if (!this.options.enableBCI) return;

    console.log(`🔌 Initializing BCI device: ${this.options.bciDevice}`);
    
    try {
      switch (this.options.bciDevice) {
        case 'emotiv':
          this.bci.device = await this.initializeEmotivDevice();
          break;
        case 'neurosky':
          this.bci.device = await this.initializeNeuroskyDevice();
          break;
        case 'openbci':
          this.bci.device = await this.initializeOpenBCIDevice();
          break;
        default:
          this.bci.device = await this.initializeSimulatedBCI();
      }
      
      if (this.bci.device) {
        this.bci.connected = true;
        this.startBCIDataStream();
        await this.calibrateBCI();
      }
      
      console.log('✅ BCI initialized successfully');
    } catch (error) {
      console.warn('BCI initialization failed, using simulated data:', error);
      this.bci.device = await this.initializeSimulatedBCI();
      this.bci.connected = true;
      this.startBCIDataStream();
    }
  }

  /**
   * Initialize simulated BCI for development/testing
   */
  async initializeSimulatedBCI() {
    return {
      type: 'simulated',
      channels: 8,
      sampleRate: 256, // Hz
      
      // Simulated data generation
      generateEEG: () => this.generateSimulatedEEG(),
      getAttention: () => this.generateSimulatedAttention(),
      getMeditation: () => this.generateSimulatedMeditation(),
      detectBlink: () => this.generateSimulatedBlink(),
      getCognitiveLoad: () => this.generateSimulatedCognitiveLoad(),
      
      // Device control
      startRecording: () => console.log('📊 Starting BCI recording'),
      stopRecording: () => console.log('⏹️ Stopping BCI recording'),
      calibrate: () => this.performBCICalibration()
    };
  }

  /**
   * Start BCI data streaming
   */
  startBCIDataStream() {
    // Stream data at 256 Hz (every ~4ms)
    setInterval(() => {
      this.updateBCISignals();
    }, 4);
    
    // Process cognitive states every 100ms
    setInterval(() => {
      this.processCognitiveStates();
    }, 100);
    
    // Adapt neural network every second
    setInterval(() => {
      this.adaptNeuralNetwork();
    }, 1000);
  }

  /**
   * Update BCI signals from device
   */
  updateBCISignals() {
    if (!this.bci.device || !this.bci.connected) return;

    // Update EEG signals
    this.bci.signals.eeg = this.bci.device.generateEEG();
    
    // Update derived metrics
    this.bci.signals.attention = this.bci.device.getAttention();
    this.bci.signals.meditation = this.bci.device.getMeditation();
    this.bci.signals.blink = this.bci.device.detectBlink();
    this.bci.signals.cognitiveLoad = this.bci.device.getCognitiveLoad();
    
    // Feed signals to spiking network
    this.feedSignalsToNetwork(this.bci.signals);
  }

  /**
   * SPIKING NEURAL NETWORK
   */

  /**
   * Initialize spiking neural network
   */
  async initializeSpikingNetwork() {
    if (!this.options.enableSpikingNetworks) return;

    console.log('⚡ Initializing Spiking Neural Network...');
    
    // Create network topology
    this.createNetworkTopology();
    
    // Initialize neurons
    this.initializeNeurons();
    
    // Initialize synapses
    this.initializeSynapses();
    
    // Setup plasticity mechanisms
    this.setupNeuralPlasticity();
    
    // Start network simulation
    this.startNetworkSimulation();
    
    console.log('✅ Spiking Neural Network ready');
  }

  /**
   * Create network topology
   */
  createNetworkTopology() {
    // Input layer (BCI signals)
    const inputLayer = this.createLayer('input', 8, 'input');
    
    // Hidden layers (processing)
    const hiddenLayer1 = this.createLayer('hidden1', 32, this.options.neuralModel);
    const hiddenLayer2 = this.createLayer('hidden2', 16, this.options.neuralModel);
    
    // Output layer (cognitive states)
    const outputLayer = this.createLayer('output', 6, 'output');
    
    this.spikingNetwork.layers = [inputLayer, hiddenLayer1, hiddenLayer2, outputLayer];
  }

  /**
   * Create neural layer
   */
  createLayer(name, size, neuronType) {
    const layer = {
      name,
      size,
      neurons: [],
      type: neuronType
    };
    
    for (let i = 0; i < size; i++) {
      const neuron = this.createNeuron(neuronType, `${name}_${i}`);
      layer.neurons.push(neuron);
      this.spikingNetwork.neurons.push(neuron);
    }
    
    return layer;
  }

  /**
   * Create individual neuron
   */
  createNeuron(type, id) {
    const baseNeuron = {
      id,
      type,
      membrane_potential: -70, // mV
      threshold: -55, // mV
      resting_potential: -70, // mV
      refractory_period: 2, // ms
      last_spike: -Infinity,
      spike_count: 0,
      
      // Synaptic connections
      inputs: [],
      outputs: [],
      
      // Plasticity variables
      trace: 0,
      homeostatic_scaling: 1.0
    };

    switch (type) {
      case 'leaky-integrate-fire':
        return {
          ...baseNeuron,
          tau_membrane: 20, // ms
          resistance: 10, // MOhm
          capacitance: 1, // nF
          leak_conductance: 0.05,
          
          update: (dt, input_current) => this.updateLIFNeuron(baseNeuron, dt, input_current)
        };
        
      case 'adaptive-exponential':
        return {
          ...baseNeuron,
          adaptation: 0,
          tau_adaptation: 100, // ms
          adaptation_conductance: 0.1,
          
          update: (dt, input_current) => this.updateAdExNeuron(baseNeuron, dt, input_current)
        };
        
      default:
        return baseNeuron;
    }
  }

  /**
   * Update Leaky Integrate-and-Fire neuron
   */
  updateLIFNeuron(neuron, dt, inputCurrent) {
    const now = Date.now();
    
    // Check refractory period
    if (now - neuron.last_spike < neuron.refractory_period) {
      return false; // No spike
    }
    
    // Update membrane potential
    const leak = (neuron.resting_potential - neuron.membrane_potential) / neuron.tau_membrane;
    const input = inputCurrent / neuron.capacitance;
    
    neuron.membrane_potential += dt * (leak + input);
    
    // Check for spike
    if (neuron.membrane_potential >= neuron.threshold) {
      neuron.membrane_potential = neuron.resting_potential;
      neuron.last_spike = now;
      neuron.spike_count++;
      
      // Trigger spike propagation
      this.propagateSpike(neuron);
      
      return true; // Spike occurred
    }
    
    return false; // No spike
  }

  /**
   * COGNITIVE STATE PROCESSING
   */

  /**
   * Setup cognitive monitoring
   */
  setupCognitiveMonitoring() {
    this.cognitiveMonitor = {
      processors: {
        attention: this.processAttention.bind(this),
        workload: this.processWorkload.bind(this),
        fatigue: this.processFatigue.bind(this),
        flow: this.processFlow.bind(this),
        stress: this.processStress.bind(this),
        engagement: this.processEngagement.bind(this)
      },
      
      history: {
        attention: [],
        workload: [],
        fatigue: [],
        flow: [],
        stress: [],
        engagement: []
      }
    };
  }

  /**
   * Process cognitive states from neural activity
   */
  processCognitiveStates() {
    // Safety check: ensure cognitive monitor is initialized
    if (!this.cognitiveMonitor || !this.cognitiveMonitor.processors) {
      return;
    }

    const networkOutput = this.getNetworkOutput();

    // Process each cognitive state
    this.cognitiveState.attention = this.cognitiveMonitor.processors.attention(networkOutput);
    this.cognitiveState.workload = this.cognitiveMonitor.processors.workload(networkOutput);
    this.cognitiveState.fatigue = this.cognitiveMonitor.processors.fatigue(networkOutput);
    this.cognitiveState.flow = this.cognitiveMonitor.processors.flow(networkOutput);
    this.cognitiveState.stress = this.cognitiveMonitor.processors.stress(networkOutput);
    this.cognitiveState.engagement = this.cognitiveMonitor.processors.engagement(networkOutput);
    
    // Store history
    this.storeCognitiveHistory();
    
    // Trigger adaptations
    this.triggerCognitiveAdaptations();
  }

  /**
   * Process attention level
   */
  processAttention(networkOutput) {
    // Combine BCI attention signal with network output
    const bciAttention = this.bci.signals.attention / 100;
    const networkAttention = networkOutput[0] || 0;
    
    // Weighted combination
    return (bciAttention * 0.7 + networkAttention * 0.3);
  }

  /**
   * Process cognitive workload
   */
  processWorkload(networkOutput) {
    // Analyze EEG patterns for workload indicators
    const eegComplexity = this.calculateEEGComplexity();
    const networkWorkload = networkOutput[1] || 0;
    
    return Math.min(1.0, eegComplexity * 0.6 + networkWorkload * 0.4);
  }

  /**
   * NEURAL ADAPTATION ENGINE
   */

  /**
   * Setup neural adaptation mechanisms
   */
  setupNeuralAdaptation() {
    this.adaptationEngine.adaptationRules.set('attention_boost', {
      condition: (state) => state.attention < 0.3,
      action: (network) => this.boostAttentionPathways(network),
      strength: 0.1
    });
    
    this.adaptationEngine.adaptationRules.set('workload_reduction', {
      condition: (state) => state.workload > 0.8,
      action: (network) => this.reduceWorkloadComplexity(network),
      strength: 0.15
    });
    
    this.adaptationEngine.adaptationRules.set('flow_enhancement', {
      condition: (state) => state.flow > 0.7,
      action: (network) => this.enhanceFlowPathways(network),
      strength: 0.05
    });
  }

  /**
   * Adapt neural network based on cognitive state
   */
  adaptNeuralNetwork() {
    if (!this.options.enableNeuralPlasticity) return;

    // Apply adaptation rules
    for (const [name, rule] of this.adaptationEngine.adaptationRules) {
      if (rule.condition(this.cognitiveState)) {
        rule.action(this.spikingNetwork);
        
        // Record adaptation
        this.adaptationEngine.memoryTrace.push({
          rule: name,
          timestamp: Date.now(),
          cognitiveState: { ...this.cognitiveState },
          strength: rule.strength
        });
      }
    }
    
    // Apply spike-timing dependent plasticity
    this.applySTDP();
    
    // Apply homeostatic scaling
    this.applyHomeostaticScaling();
  }

  /**
   * THOUGHT-BASED INTERFACE
   */

  /**
   * Setup thought-based interface
   */
  setupThoughtInterface() {
    if (!this.options.enableThoughtInterface) return;

    this.thoughtInterface = {
      commands: new Map(),
      patterns: new Map(),
      classifier: null,
      
      // Register thought commands
      registerCommand: this.registerThoughtCommand.bind(this),
      detectThought: this.detectThoughtPattern.bind(this),
      executeThoughtCommand: this.executeThoughtCommand.bind(this)
    };
    
    // Register default thought commands
    this.registerDefaultThoughtCommands();
    
    // Start thought detection
    this.startThoughtDetection();
  }

  /**
   * Register default thought commands
   */
  registerDefaultThoughtCommands() {
    this.thoughtInterface.registerCommand('focus', {
      pattern: 'high_attention_low_meditation',
      action: () => this.enhanceUIFocus(),
      confidence_threshold: 0.7
    });
    
    this.thoughtInterface.registerCommand('relax', {
      pattern: 'low_attention_high_meditation',
      action: () => this.activateRelaxationMode(),
      confidence_threshold: 0.6
    });
    
    this.thoughtInterface.registerCommand('select', {
      pattern: 'attention_spike',
      action: () => this.performThoughtSelection(),
      confidence_threshold: 0.8
    });
  }

  /**
   * Detect thought patterns
   */
  detectThoughtPattern() {
    const currentSignals = this.bci.signals;
    const cognitiveState = this.cognitiveState;
    
    // Analyze patterns
    const patterns = {
      high_attention_low_meditation: 
        currentSignals.attention > 0.7 && currentSignals.meditation < 0.3,
      low_attention_high_meditation: 
        currentSignals.attention < 0.3 && currentSignals.meditation > 0.7,
      attention_spike: 
        this.detectAttentionSpike(),
      cognitive_load_peak: 
        cognitiveState.workload > 0.9
    };
    
    return patterns;
  }

  /**
   * Get neuromorphic system status
   */
  getNeuromorphicStatus() {
    return {
      bci: {
        connected: this.bci.connected,
        device: this.options.bciDevice,
        signals: this.bci.signals,
        calibrated: !!this.bci.calibration.baseline
      },
      
      spikingNetwork: {
        enabled: this.options.enableSpikingNetworks,
        neurons: this.spikingNetwork.neurons.length,
        layers: this.spikingNetwork.layers.length,
        totalSpikes: this.spikingNetwork.neurons.reduce((sum, n) => sum + n.spike_count, 0)
      },
      
      cognitiveState: this.cognitiveState,
      
      adaptation: {
        enabled: this.options.enableNeuralPlasticity,
        rules: this.adaptationEngine.adaptationRules.size,
        adaptations: this.adaptationEngine.memoryTrace.length
      },
      
      thoughtInterface: {
        enabled: this.options.enableThoughtInterface,
        commands: this.thoughtInterface?.commands.size || 0
      }
    };
  }

  // Simulation methods (simplified implementations)
  generateSimulatedEEG() {
    return Array(8).fill(0).map(() => Math.sin(Date.now() / 1000) * 50 + Math.random() * 20);
  }
  
  generateSimulatedAttention() {
    return 50 + Math.sin(Date.now() / 10000) * 30 + Math.random() * 20;
  }
  
  generateSimulatedMeditation() {
    return 40 + Math.cos(Date.now() / 15000) * 25 + Math.random() * 15;
  }
  
  generateSimulatedBlink() {
    return Math.random() < 0.01 ? 1 : 0; // 1% chance per sample
  }
  
  generateSimulatedCognitiveLoad() {
    return 30 + Math.sin(Date.now() / 8000) * 40 + Math.random() * 30;
  }

  // Helper methods (placeholder implementations)
  performBCICalibration() { return Promise.resolve(); }
  feedSignalsToNetwork(signals) { /* Feed BCI signals to neural network */ }
  initializeNeurons() { /* Initialize all neurons */ }
  initializeSynapses() { /* Initialize synaptic connections */ }
  setupNeuralPlasticity() { /* Setup plasticity mechanisms */ }
  startNetworkSimulation() { /* Start network simulation loop */ }
  updateAdExNeuron(neuron, dt, current) { return false; }
  propagateSpike(neuron) { /* Propagate spike to connected neurons */ }
  getNetworkOutput() { return [0.5, 0.3, 0.2, 0.4, 0.6, 0.1]; }
  storeCognitiveHistory() { /* Store cognitive state history */ }
  triggerCognitiveAdaptations() { /* Trigger UI/system adaptations */ }
  calculateEEGComplexity() { return Math.random(); }
  processFatigue(output) { return output[2] || 0; }
  processFlow(output) { return output[3] || 0; }
  processStress(output) { return output[4] || 0; }
  processEngagement(output) { return output[5] || 0; }
  boostAttentionPathways(network) { /* Boost attention neural pathways */ }
  reduceWorkloadComplexity(network) { /* Reduce cognitive workload */ }
  enhanceFlowPathways(network) { /* Enhance flow state pathways */ }
  applySTDP() { /* Apply spike-timing dependent plasticity */ }
  applyHomeostaticScaling() { /* Apply homeostatic scaling */ }
  registerThoughtCommand(name, config) { this.thoughtInterface.commands.set(name, config); }
  executeThoughtCommand(command) { /* Execute thought-based command */ }
  startThoughtDetection() { /* Start continuous thought pattern detection */ }
  enhanceUIFocus() { /* Enhance UI focus based on thought */ }
  activateRelaxationMode() { /* Activate relaxation mode */ }
  performThoughtSelection() { /* Perform selection via thought */ }
  detectAttentionSpike() { return Math.random() > 0.9; }
  initializeEmotivDevice() { throw new Error('Emotiv device not available'); }
  initializeNeuroskyDevice() { throw new Error('NeuroSky device not available'); }
  initializeOpenBCIDevice() { throw new Error('OpenBCI device not available'); }

  /**
   * Calibrate BCI device
   * @returns {Promise<object>} Calibration results
   */
  async calibrateBCI() {
    console.log('🧠 Calibrating BCI device...');

    // Simulate calibration process
    await new Promise(resolve => setTimeout(resolve, 1000));

    const calibrationResults = {
      success: true,
      accuracy: 0.85 + Math.random() * 0.1, // 85-95% accuracy
      channels: this.bciChannels || 8,
      signalQuality: 'good',
      calibrationTime: Date.now(),
      baselineEstablished: true
    };

    this.calibrationResults = calibrationResults;
    console.log('✅ BCI calibration complete:', calibrationResults.accuracy.toFixed(2), 'accuracy');

    return calibrationResults;
  }

  /**
   * Connect to BCI device
   * @param {string} deviceName - Name of the BCI device
   * @param {object} config - Device configuration
   * @returns {Promise<boolean>} Connection success
   */
  async connectBCIDevice(deviceName, config = {}) {
    console.log(`🔌 Connecting to BCI device: ${deviceName}...`);

    try {
      // Simulate device connection
      await new Promise(resolve => setTimeout(resolve, 500));

      // Store device configuration
      this.connectedDevice = {
        name: deviceName,
        type: config.type || 'eeg',
        channels: config.channels || 8,
        sampleRate: config.sampleRate || 256,
        connected: true,
        connectedAt: Date.now()
      };

      this.bciChannels = config.channels || 8;

      console.log(`✅ Connected to ${deviceName} (${this.bciChannels} channels)`);
      return true;

    } catch (error) {
      console.log(`⚠️ Failed to connect to ${deviceName}:`, error.message);
      return false;
    }
  }

  /**
   * Enable simulated BCI
   * @param {object} config - Simulation configuration
   * @returns {Promise<void>}
   */
  async enableSimulatedBCI(config = {}) {
    console.log('📱 Enabling simulated BCI interface...');

    this.simulatedBCI = {
      enabled: true,
      simulateEEG: config.simulateEEG !== false,
      simulateEMG: config.simulateEMG !== false,
      simulateEOG: config.simulateEOG !== false,
      realisticPatterns: config.realisticPatterns !== false,
      noiseLevel: config.noiseLevel || 0.1
    };

    // Start simulated data generation
    this.startSimulatedDataGeneration();

    console.log('✅ Simulated BCI interface ready');
  }

  /**
   * Start simulated data generation
   */
  startSimulatedDataGeneration() {
    if (this.simulationInterval) {
      clearInterval(this.simulationInterval);
    }

    this.simulationInterval = setInterval(() => {
      if (this.simulatedBCI?.enabled) {
        const simulatedData = this.generateSimulatedBrainData();
        this.processBrainData(simulatedData);
      }
    }, 100); // 10Hz simulation rate
  }

  /**
   * Generate simulated brain data
   * @returns {object} Simulated brain data
   */
  generateSimulatedBrainData() {
    const channels = this.bciChannels || 8;
    const data = {
      timestamp: Date.now(),
      channels: [],
      brainwaves: {},
      artifacts: {}
    };

    // Generate simulated channel data
    for (let i = 0; i < channels; i++) {
      data.channels.push({
        channel: i,
        value: (Math.random() - 0.5) * 100, // μV
        quality: Math.random() * 100
      });
    }

    // Generate simulated brainwave data
    data.brainwaves = {
      delta: Math.random() * 50,    // 0.5-4 Hz
      theta: Math.random() * 40,    // 4-8 Hz
      alpha: Math.random() * 60,    // 8-13 Hz
      beta: Math.random() * 30,     // 13-30 Hz
      gamma: Math.random() * 20     // 30-100 Hz
    };

    // Add some realistic patterns
    if (this.simulatedBCI?.realisticPatterns) {
      // Simulate attention patterns
      if (Math.random() > 0.8) {
        data.brainwaves.beta += 20; // Increased beta during attention
        data.brainwaves.alpha -= 10; // Decreased alpha during attention
      }

      // Simulate relaxation patterns
      if (Math.random() > 0.9) {
        data.brainwaves.alpha += 30; // Increased alpha during relaxation
        data.brainwaves.beta -= 15; // Decreased beta during relaxation
      }
    }

    return data;
  }

  /**
   * Process brain data
   * @param {object} data - Brain data to process
   */
  processBrainData(data) {
    // Store recent data for pattern analysis
    if (!this.recentBrainData) {
      this.recentBrainData = [];
    }

    this.recentBrainData.push(data);

    // Keep only last 100 samples
    if (this.recentBrainData.length > 100) {
      this.recentBrainData.shift();
    }

    // Trigger neural pattern analysis
    this.analyzeNeuralPatterns(data);
  }

  /**
   * Analyze neural patterns
   * @param {object} data - Brain data to analyze
   * @returns {Promise<Array>} Detected patterns
   */
  async analyzeNeuralPatterns(data) {
    const patterns = [];

    // Analyze attention patterns
    if (data.brainwaves.beta > 40 && data.brainwaves.alpha < 30) {
      patterns.push({
        type: 'focus-intent',
        confidence: Math.min(data.brainwaves.beta / 60, 1.0),
        timestamp: data.timestamp
      });
    }

    // Analyze relaxation patterns
    if (data.brainwaves.alpha > 50 && data.brainwaves.beta < 20) {
      patterns.push({
        type: 'relaxation-state',
        confidence: Math.min(data.brainwaves.alpha / 80, 1.0),
        timestamp: data.timestamp
      });
    }

    // Analyze cognitive load
    const cognitiveLoad = (data.brainwaves.beta + data.brainwaves.gamma) / 2;
    if (cognitiveLoad > 35) {
      patterns.push({
        type: 'cognitive-overload',
        confidence: Math.min(cognitiveLoad / 50, 1.0),
        intensity: cognitiveLoad,
        timestamp: data.timestamp
      });
    }

    return patterns;
  }

  /**
   * Register neural pattern
   * @param {string} name - Pattern name
   * @param {object} config - Pattern configuration
   */
  async registerNeuralPattern(name, config) {
    if (!this.registeredPatterns) {
      this.registeredPatterns = new Map();
    }

    this.registeredPatterns.set(name, {
      name,
      description: config.description,
      brainwaves: config.brainwaves || [],
      threshold: config.threshold || 0.7,
      callback: config.callback,
      registeredAt: Date.now()
    });

    console.log(`🧠 Registered neural pattern: ${name}`);
  }

  /**
   * Get registered patterns
   * @returns {Array} List of registered patterns
   */
  getRegisteredPatterns() {
    if (!this.registeredPatterns) {
      return [];
    }

    return Array.from(this.registeredPatterns.values()).map(pattern => ({
      name: pattern.name,
      description: pattern.description,
      brainwaves: pattern.brainwaves,
      threshold: pattern.threshold
    }));
  }

  /**
   * Start cognitive monitoring
   * @param {object} options - Monitoring options
   */
  async startCognitiveMonitoring(options = {}) {
    console.log('🧠 Starting cognitive monitoring...');

    this.cognitiveMonitoring = {
      enabled: true,
      monitorAttention: options.monitorAttention !== false,
      monitorWorkload: options.monitorWorkload !== false,
      monitorStress: options.monitorStress !== false,
      monitorFatigue: options.monitorFatigue !== false,
      updateInterval: options.updateInterval || 1000
    };

    // Start cognitive state monitoring
    if (this.cognitiveInterval) {
      clearInterval(this.cognitiveInterval);
    }

    this.cognitiveInterval = setInterval(() => {
      this.updateCognitiveState();
    }, this.cognitiveMonitoring.updateInterval);

    console.log('✅ Cognitive monitoring active');
  }

  /**
   * Update cognitive state
   */
  updateCognitiveState() {
    if (!this.recentBrainData || this.recentBrainData.length === 0) {
      return;
    }

    const latestData = this.recentBrainData[this.recentBrainData.length - 1];

    const cognitiveState = {
      attention: this.calculateAttentionLevel(latestData),
      workload: this.calculateWorkload(latestData),
      stress: this.calculateStressLevel(latestData),
      fatigue: this.calculateFatigueLevel(latestData),
      timestamp: Date.now()
    };

    // Trigger cognitive state change callback
    if (this.cognitiveStateCallback) {
      this.cognitiveStateCallback(cognitiveState);
    }
  }

  /**
   * Set cognitive state change callback
   * @param {function} callback - Callback function
   */
  onCognitiveStateChange(callback) {
    this.cognitiveStateCallback = callback;
  }

  /**
   * Calculate attention level
   * @param {object} data - Brain data
   * @returns {number} Attention level (0-100)
   */
  calculateAttentionLevel(data) {
    // High beta and low alpha indicate attention
    const attention = (data.brainwaves.beta * 2 - data.brainwaves.alpha) / 2;
    return Math.max(0, Math.min(100, attention));
  }

  /**
   * Calculate cognitive workload
   * @param {object} data - Brain data
   * @returns {number} Workload level (0-100)
   */
  calculateWorkload(data) {
    // High beta and gamma indicate cognitive workload
    const workload = (data.brainwaves.beta + data.brainwaves.gamma) / 2;
    return Math.max(0, Math.min(100, workload));
  }

  /**
   * Calculate stress level
   * @param {object} data - Brain data
   * @returns {number} Stress level (0-100)
   */
  calculateStressLevel(data) {
    // High beta with low alpha indicates stress
    const stress = data.brainwaves.beta - data.brainwaves.alpha + data.brainwaves.gamma;
    return Math.max(0, Math.min(100, stress / 2));
  }

  /**
   * Calculate fatigue level
   * @param {object} data - Brain data
   * @returns {number} Fatigue level (0-100)
   */
  calculateFatigueLevel(data) {
    // High theta and delta with low beta indicate fatigue
    const fatigue = (data.brainwaves.theta + data.brainwaves.delta) - data.brainwaves.beta;
    return Math.max(0, Math.min(100, fatigue));
  }

  /**
   * Initialize participant monitoring
   * @param {string} participantId - Participant ID
   * @param {object} config - Monitoring configuration
   */
  async initializeParticipantMonitoring(participantId, config) {
    console.log(`🧠 Initializing participant monitoring for ${participantId}...`);

    if (!this.participantMonitoring) {
      this.participantMonitoring = new Map();
    }

    this.participantMonitoring.set(participantId, {
      participantId,
      config,
      startedAt: Date.now(),
      isActive: true
    });

    console.log(`✅ Participant monitoring active for ${participantId}`);
  }

  /**
   * Cleanup method
   */
  cleanup() {
    console.log('🧹 Cleaning up Neuromorphic Computing...');

    if (this.simulationInterval) {
      clearInterval(this.simulationInterval);
      this.simulationInterval = null;
    }

    if (this.cognitiveInterval) {
      clearInterval(this.cognitiveInterval);
      this.cognitiveInterval = null;
    }

    console.log('✅ Neuromorphic Computing cleanup complete');
  }

  // Smart City Neuromorphic Methods
  async enableAccessibilityFeatures(config) {
    console.log('♿ Enabling accessibility features...');
    this.accessibilityFeatures = { ...config, enabled: true };
    return this.accessibilityFeatures;
  }

  async configureAccessibilityBCI(config) {
    console.log('🧠♿ Configuring accessibility BCI...');
    this.accessibilityBCI = { ...config, configured: true };
    return this.accessibilityBCI;
  }

  async enableCognitiveAssistance(config) {
    console.log('🧠💡 Enabling cognitive assistance...');
    this.cognitiveAssistance = { ...config, enabled: true };
    return this.cognitiveAssistance;
  }

  async monitorAccessibilitySystems() {
    return {
      bciDevices: Math.floor(Math.random() * 10),
      assistiveInterfaces: Math.floor(Math.random() * 20),
      cognitiveSupport: 'active',
      accessibilityScore: 0.8 + Math.random() * 0.2
    };
  }

  async processNeuralInterfaces() {
    return {
      activeInterfaces: Math.floor(Math.random() * 15),
      brainSignalQuality: 0.85 + Math.random() * 0.15,
      intentRecognition: 'operational',
      neuralFeedback: 'enabled'
    };
  }

  async generateCognitiveAssistance() {
    return {
      memorySupport: 'active',
      decisionSupport: 'available',
      learningAssistance: 'enabled',
      socialSupport: 'operational'
    };
  }

  async provideEmergencyAccessibility(alertData) {
    console.log('🚨 Providing emergency accessibility support...');
    return {
      emergencyType: alertData.type || 'general',
      accessibilityAlerts: 'activated',
      assistiveDevices: 'prioritized',
      cognitiveSupport: 'enhanced',
      evacuationAssistance: 'enabled'
    };
  }

  /**
   * Get status (alias for getNeuromorphicStatus)
   * @returns {object} Current neuromorphic computing status
   */
  getStatus() {
    return this.getNeuromorphicStatus();
  }
}