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| import asyncio | |
| import random | |
| from typing import Dict, List, Any | |
| import logging | |
| class BCIConnector: | |
| """ | |
| Connecteur pour interfaces cerveau-machine | |
| Simulation de lecture et d'interprétation de signaux cérébraux | |
| """ | |
| def __init__(self): | |
| self.logger = logging.getLogger("bci_connector") | |
| self.signal_quality = 0.0 | |
| self.mental_states = {} | |
| self.brain_patterns = {} | |
| async def initialize(self): | |
| """Initialise l'interface BCI""" | |
| self.logger.info("🧠 Initialisation de l'interface cerveau-machine...") | |
| try: | |
| await self._calibrate_sensors() | |
| await self._setup_pattern_recognition() | |
| self.signal_quality = 0.85 | |
| self.logger.info("✅ Interface BCI initialisée") | |
| return True | |
| except Exception as e: | |
| self.logger.error(f"❌ Erreur d'initialisation BCI: {e}") | |
| return False | |
| async def read_brain_signals(self) -> Dict[str, Any]: | |
| """Lit les signaux cérébraux en temps réel""" | |
| try: | |
| # Simulation de signaux cérébraux | |
| signals = { | |
| "eeg": { | |
| "delta": random.uniform(0.1, 0.5), | |
| "theta": random.uniform(0.1, 0.4), | |
| "alpha": random.uniform(0.1, 0.6), | |
| "beta": random.uniform(0.1, 0.8), | |
| "gamma": random.uniform(0.1, 0.3) | |
| }, | |
| "focus_level": random.uniform(0.3, 0.9), | |
| "mental_workload": random.uniform(0.2, 0.8), | |
| "emotional_state": random.choice(["calme", "concentré", "créatif", "attentif"]), | |
| "signal_quality": self.signal_quality | |
| } | |
| return signals | |
| except Exception as e: | |
| self.logger.error(f"Erreur de lecture BCI: {e}") | |
| return {"error": str(e)} | |
| async def detect_mental_commands(self) -> List[Dict[str, Any]]: | |
| """Détecte les commandes mentales""" | |
| commands = [ | |
| {"type": "focus", "intensity": random.uniform(0.5, 0.95), "confidence": 0.8}, | |
| {"type": "imagine", "content": "mouvement", "confidence": 0.7}, | |
| {"type": "emotion", "value": random.choice(["positive", "negative", "neutral"]), "confidence": 0.6} | |
| ] | |
| return random.sample(commands, random.randint(1, 3)) | |
| async def send_neuro_feedback(self, feedback: Dict[str, Any]) -> bool: | |
| """Envoie un feedback neuro-cognitif""" | |
| self.logger.info(f"📤 Envoi de feedback neuro: {feedback.get('type', 'inconnu')}") | |
| return True | |
| async def _calibrate_sensors(self): | |
| """Calibre les capteurs BCI""" | |
| self.logger.info("🎯 Calibration des capteurs cérébraux...") | |
| await asyncio.sleep(0.8) | |
| self.signal_quality = random.uniform(0.7, 0.95) | |
| self.logger.info(f"📊 Qualité du signal: {self.signal_quality:.3f}") | |
| async def _setup_pattern_recognition(self): | |
| """Configure la reconnaissance de patterns cérébraux""" | |
| self.brain_patterns = { | |
| "focus_patterns": ["concentration_intense", "attention_soutenue"], | |
| "creative_patterns": ["pensee_laterale", "association_libre"], | |
| "relaxation_patterns": ["meditation", "detente_profonde"] | |
| } |