diff --git a/BrainWave_Pred.py b/BrainWave_Pred.py new file mode 100644 index 0000000..23ce885 --- /dev/null +++ b/BrainWave_Pred.py @@ -0,0 +1,139 @@ +import numpy as np +from joblib import load +import os +# from __future__ import unicode_literals, print_function +from socket import socket, AF_INET, SOCK_DGRAM +from pythonosc import osc_message +import time + +HOST = '' +PORT = 8001 +class EEGPredictor: + def __init__(self, model_dir='trained_model'): + self.model, self.scaler = self._load_model(model_dir) + self.class_names = {0: 'neutral', 1: 'right', 2: 'left'} + self.feature_names = [ + 'alpha', 'beta', 'theta', 'delta', 'gamma', + 'alpha_beta_ratio', 'theta_beta_ratio', 'alpha_theta_ratio', + 'alpha_rel', 'beta_rel', 'theta_rel', 'delta_rel', 'gamma_rel', + 'alpha_log', 'beta_log', 'theta_log', 'delta_log', 'gamma_log' + ] + + def _load_model(self, model_dir): + model_path = os.path.join(model_dir, 'rf_model.joblib') + scaler_path = os.path.join(model_dir, 'scaler.joblib') + return load(model_path), load(scaler_path) + + def _create_features(self, basic_features): + """基本的な脳波データから追加の特徴量を生成""" + features = {} + + # 基本特徴量 + alpha, beta, theta, delta, gamma = basic_features + features.update({ + 'alpha': alpha, 'beta': beta, 'theta': theta, + 'delta': delta, 'gamma': gamma + }) + + # 比率の計算 + features['alpha_beta_ratio'] = alpha / beta if beta != 0 else 0 + features['theta_beta_ratio'] = theta / beta if beta != 0 else 0 + features['alpha_theta_ratio'] = alpha / theta if theta != 0 else 0 + + # 相対パワーの計算 + total_power = sum(basic_features) + if total_power != 0: + features.update({ + 'alpha_rel': alpha / total_power, + 'beta_rel': beta / total_power, + 'theta_rel': theta / total_power, + 'delta_rel': delta / total_power, + 'gamma_rel': gamma / total_power + }) + else: + features.update({ + 'alpha_rel': 0, 'beta_rel': 0, 'theta_rel': 0, + 'delta_rel': 0, 'gamma_rel': 0 + }) + + # 対数変換 + features.update({ + 'alpha_log': np.log1p(alpha) if alpha > 0 else 0, + 'beta_log': np.log1p(beta) if beta > 0 else 0, + 'theta_log': np.log1p(theta) if theta > 0 else 0, + 'delta_log': np.log1p(delta) if delta > 0 else 0, + 'gamma_log': np.log1p(gamma) if gamma > 0 else 0 + }) + + # 特徴量を正しい順序で並べ替え + return [features[name] for name in self.feature_names] + + def predict(self, eeg_data): + """脳波データから予測を行う""" + # 特徴量の生成 + features = self._create_features(eeg_data) + features = np.array(features).reshape(1, -1) + + # スケーリングと予測 + scaled_data = self.scaler.transform(features) + prediction = self.model.predict(scaled_data)[0] + probabilities = self.model.predict_proba(scaled_data)[0] + + # 結果の整形 + result = { + 'predicted_class': self.class_names[prediction], + 'confidence': float(probabilities[prediction]), + 'probabilities': { + self.class_names[i]: float(prob) + for i, prob in enumerate(probabilities) + } + } + + return result +def process_eeg_data(raw_eeg_data): + """ + 脳波データを処理して予測を行う + + Parameters: + raw_eeg_data: [alpha, beta, theta, delta, gamma]の形式の脳波データ + """ + predictor = EEGPredictor() + result = predictor.predict(raw_eeg_data) + + print(f"Predicted class: {result['predicted_class']}") + print(f"Confidence: {result['confidence']:.2f}") + print("Class probabilities:") + for class_name, prob in result['probabilities'].items(): + print(f" {class_name}: {prob:.2f}") + + return result + +def Convert_BrainWave(data): + msg = osc_message.OscMessage(data) + #print(msg.params) + types = msg.address + arguments = [] + if types == "/Attention": + arguments.append("Attention") + arguments.append(float(msg.params[0])) + + elif types == "/Meditation": + arguments.append("Meditation") + arguments.append(float(msg.params[0])) + + elif types == "/BandPower": + arguments.append("BandPower") + arguments += list(map(float, msg.params[0].split(";"))) + #print(map(float, msg.params[0].split(";"))) + return arguments +s = socket(AF_INET, SOCK_DGRAM) +s.bind((HOST, PORT)) +while True: + # 実際の脳波データをここに入れます + print("受信待ち") + data, address = s.recvfrom(1024) + received_data = Convert_BrainWave(data=data) + received_data = received_data[1:] + print(received_data) + result = process_eeg_data(received_data) + time.sleep(0.5) diff --git a/__pycache__/TaskManager.cpython-39.pyc b/__pycache__/TaskManager.cpython-39.pyc index 516a348..4a627af 100644 --- a/__pycache__/TaskManager.cpython-39.pyc +++ b/__pycache__/TaskManager.cpython-39.pyc Binary files differ diff --git a/csvfiles/nouhadata_Attention2024-12-19,11-24-40-242172.csv b/csvfiles/nouhadata_Attention2024-12-19,11-24-40-242172.csv new file mode 100644 index 0000000..4b683f7 --- /dev/null +++ b/csvfiles/nouhadata_Attention2024-12-19,11-24-40-242172.csv @@ -0,0 +1,27 @@ +attention,key +2.0987415313720703,1 +2.107482433319092,1 +1.9515609741210938,1 +1.9395179748535156,1 +2.604310989379883,1 +4.554898262023926,1 +6.518619537353516,1 +9.199201583862305,1 +12.295114517211914,1 +12.660309791564941,1 +12.981464385986328,1 +13.152748107910156,1 +13.247297286987305,1 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+1,27 @@ +meditation,key +14.223045349121094,1 +14.298620223999023,1 +14.455609321594238,1 +14.584455490112305,1 +14.739079475402832,1 +14.76807689666748,1 +14.737985610961914,1 +14.604192733764648,1 +14.697515487670898,1 +14.588272094726562,1 +14.252388000488281,1 +11.461552619934082,1 +11.322392463684082,1 +11.059428215026855,1 +10.909423828125,1 +10.820115089416504,1 +10.788674354553223,1 +10.733466148376465,1 +10.780632972717285,1 +10.899450302124023,1 +11.05318832397461,1 +11.167481422424316,1 +11.075584411621094,1 +10.983272552490234,1 +10.867120742797852,1 +10.874994277954102,1 diff --git a/run.py b/run.py index 966ebd5..38c2fc6 100644 --- a/run.py +++ b/run.py @@ -79,9 +79,10 @@ def send_nouhadata(s, choice, mind, process): print_text = text_type[choice] print("タスク経過時間：", str(get_tick_time[1] - get_tick_time[0]), print_text) - if offLineMode == False and get_tick_time[1] - get_tick_time[0] >= PREP_TIME: + if offLineMode == False: data, address = s.recvfrom(1024) process.Receive_BrainWave(nouha=data, key=mind, address=address) + get_tick_time[1] = pygame.time.get_ticks() #アプリの終了時の処理 @@ -153,7 +154,8 @@ else: screen.fill(black) pygame.display.update() - send_nouhadata(s, choice, mind, process) + if get_tick_time[1] - get_tick_time[0] >= PREP_TIME: + send_nouhadata(s, choice, mind, process) if check_exit(s, choice, process): break screen.fill(black) @@ -168,7 +170,8 @@ if not load_and_play_sound(filename): continue while pygame.mixer.music.get_busy(): - send_nouhadata(s, choice, mind, process) + if get_tick_time[1] - get_tick_time[0] >= PREP_TIME: + send_nouhadata(s, choice, mind, process) if check_exit(s, choice, process): break screen.fill(black) @@ -180,7 +183,8 @@ load_and_play_sound(filename) print(f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] {instruction}") while get_tick_time[1] - get_tick_time[0] <= TIME: - send_nouhadata(s, choice, mind, process) + if get_tick_time[1] - get_tick_time[0] >= PREP_TIME: + send_nouhadata(s, choice, mind, process) if check_exit(s, choice, process): break load_and_play_sound(filename = os.path.join(SOUND_DIR, "タスクを終了してください.wav"))