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This commit is contained in:
Ivey Song
427
algorithm_V0/datacollect/eegParser.py
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427
algorithm_V0/datacollect/eegParser.py
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import os
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import sys
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import threading
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import time
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import numpy as np
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import pandas as pd
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from SunnyLinker import SunnyLinker64
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from zmqServer import zmqServer
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from zmqClient import zmqClient
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from scipy.io import savemat
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from scipy import signal
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class Parser_main(threading.Thread):
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def __init__(self):
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threading.Thread.__init__(self)
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self.Running = True
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self.fs = 250 # 采样率
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self.energy = 0 # 电量
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self.status_code = 0 # 与采集设备通信的状态码，0为异常，1为正常
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self.n_chan = 64
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self.dataBuffer = []
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self.file_num = 0#保存文件序号
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self.subject_id = None #受试者ID
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self.session_id = None #Session ID
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self.last_print_time = None
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# 预处理参数
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self.enable_preprocess = True # 是否启用预处理
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self.lowcut = 0.5 # 高通滤波截止频率 (Hz)
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self.highcut = 50 # 低通滤波截止频率 (Hz)
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self.notch_freq = 50 # 工频陷波频率 (Hz)
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self.ref_chan_name = 'CPZ' # 参考电极名称
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self.ref_chan_idx = None # 参考电极索引（运行时确定）
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self._init_filter_cache() # 初始化滤波器缓存
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# 单位转换参数
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self.calibration_scale = 1.0 # 校准系数，用于修正单位转换误差
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self.calibration_offset = 0 # 校准偏移量
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self._conversion_verified = False # 是否已验证转换
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def connect(self):
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self.thread_data_server = SunnyLinker64('127.0.0.1', 7878, 250, 64,
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method='tcp')
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self.thread_data_server.toUv = True
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self.thread_data_server.start()
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self.zmqServer = zmqServer()
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self.zmqServer.start()
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self.zmqClient = zmqClient('127.0.0.1', 8088)
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self.zmqClient.connect()
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def run(self):
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while self.Running:
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# 同步信息
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if self.zmqServer.state_mode == 'sync':
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self.zmqClient.send_to_all('sync', self.zmqClient.state)
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self.zmqServer.state_mode = 'rest'
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# 状态异常，报告上位机
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if self.status_code != self.thread_data_server.status_code:
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self.status_code = self.thread_data_server.status_code
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self.zmqClient.send_to_all('status_code', int(self.status_code))
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# 返回电量
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if self.energy != self.thread_data_server.energy:
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self.energy = self.thread_data_server.energy
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self.zmqClient.send_to_all('energy', int(self.energy))
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# 更新文件序号
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if self.subject_id != self.zmqServer.subject_id or self.session_id != self.zmqServer.session_id:
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self.subject_id = self.zmqServer.subject_id
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self.session_id = self.zmqServer.session_id
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self.file_num = 0 #从零开始计数
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if self.zmqServer.open_Impedance == True: # 开启阻抗检测功能，仅运行一次
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self.thread_data_server.Impedance(True)
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self.zmqServer.open_Impedance = -1
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elif self.zmqServer.open_Impedance == False:
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self.thread_data_server.Impedance(False)
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self.zmqServer.open_Impedance = -1
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if self.zmqServer.get_Impedance: # 返回阻抗值
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if self.thread_data_server.GetDataLenCount() > self.fs:
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Impe_data = self.thread_data_server.getData(self.fs)
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# 计算阻抗
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imps = self.thread_data_server.getImpedance(Impe_data, self.n_chan)
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self.zmqClient.send_to_all('impedance', imps.tolist())
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else:
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pass
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if self.thread_data_server.GetDataLenCount() < 50:
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time.sleep(0.01)
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continue
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if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
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data = self.thread_data_server.getData(50)
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data = data[:self.n_chan, :]
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# 数据质量检查与预处理
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if self.enable_preprocess:
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# 1. 首先验证和校准单位转换
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data, calibrated = self.verify_and_calibrate_unit(data)
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if calibrated:
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print('[INFO] 单位转换已自动校准')
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# 2. 检查数据质量
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issues = self.check_data_quality(data)
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if issues:
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print('[警告] 检测到数据质量问题:')
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for issue in issues:
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print(f' - {issue}')
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print('[INFO] 正在进行信号预处理...')
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# 3. 执行预处理
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data = self.preprocess_data(data)
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# 4. 预处理后验证
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if issues:
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new_issues = self.check_data_quality(data)
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if not new_issues:
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print(f'[INFO] 预处理完成，数据幅度正常: {np.max(np.abs(data)):.2f} µV')
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else:
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print('[警告] 预处理后仍存在问题:')
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for issue in new_issues:
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print(f' - {issue}')
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if self.zmqServer.mat_generate:
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# 检测是否需要重置缓冲区（第二次发送 matGenerate 时清空旧数据）
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if self.zmqServer.reset_mat_buffer:
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self.dataBuffer = []
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self.last_print_time = None
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self.zmqServer.reset_mat_buffer = False
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print('[INFO] 数据缓冲区已重置，从头开始采集')
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self.dataBuffer.append(data)
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if len(self.dataBuffer) % 50 == 0:
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current_time = time.time()
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if self.last_print_time is not None:
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elapsed_time = current_time - self.last_print_time
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# 2500个点 = 50个数据块 * 50个采样点/数据块
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actual_fs = 2500 / elapsed_time
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print(f"接收 2500 个采样点耗时: {elapsed_time:.4f} 秒, 折合实际采样率: {actual_fs:.2f} Hz")
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else:
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print("开始计时...")
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self.last_print_time = current_time
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print('数据保存进度： {}/{}'.format(len(self.dataBuffer),int(self.zmqServer.save_win*self.fs//50)))
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if len(self.dataBuffer) >= int(self.zmqServer.save_win*self.fs//50): #5分钟*60秒*250Hz / 50
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self.zmqServer.mat_generate = False
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matData = np.hstack(self.dataBuffer[:int(self.zmqServer.save_win*self.fs//50)])
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self.dataBuffer = []
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self.last_print_time = None # 重置计时器以备下次使用
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self.pack2mat(matData,self.subject_id,self.session_id)
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def pack2mat(self,data,subject_id,session_id):
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#EEG数据
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Data = data.T
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#通道名称
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channel_names = np.array(
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['AIN1', 'AIN2', 'AIN3', 'AIN4', 'AIN5', 'AIN6', 'AIN7', 'AIN8', 'AIN9', 'AIN10', 'AIN11', 'AIN12',
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'AIN13', 'AIN14', 'AIN15', 'AIN16', 'AIN17', 'AIN18', 'AIN19', 'AIN20', 'AIN21', 'AIN22', 'AIN23',
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'AIN24', 'AIN25', 'AIN26', 'AIN27', 'AIN28', 'AIN29', 'AIN30', 'AIN31', 'AIN32', 'AIN33', 'AIN34',
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'AIN35', 'AIN36', 'AIN37', 'AIN38', 'AIN39', 'AIN40', 'AIN41', 'AIN42', 'AIN43', 'AIN44', 'AIN45',
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'AIN46', 'AIN47', 'AIN48', 'AIN49', 'AIN50', 'AIN51', 'AIN52', 'AIN53', 'AIN54', 'AIN55', 'AIN56',
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'AIN57', 'AIN58', 'AIN59', 'AIN60', 'AIN61', 'AIN62', 'AIN63', 'AIN64'], dtype=object)
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#采样率
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sample_rate = self.fs
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#通道数量
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node_number = Data.shape[1]
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# 时间轴
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t = np.linspace(0, self.zmqServer.save_win, Data.shape[0])
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t = t.reshape(len(t), 1)
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#电极名称
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electrode_name = np.array(['FP1', 'FP2', 'PO6', 'POZ', 'F3', 'F4', 'FPZ', 'AF4', 'FC3', 'PO8', 'CP2', 'CP1',
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'FCZ', 'PO5', 'FC2', 'FC1', 'C3', 'C4', 'FC4', 'CP4', 'P3', 'P4', 'F5', 'C5', 'F6',
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'PO4', 'CP6', 'CP5', 'PO3', 'CP3', 'FC6', 'FC5', 'CB1', 'CB2', 'P5', 'AF7', 'A1','T7',
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'FT7', 'TP7', 'FT8', 'AF8', 'F8', 'F7', 'P6', 'C6', 'O2', 'O1', 'T8', 'P7', 'CZ','PZ',
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'P8', 'FZ', 'OZ', 'PO7', 'TP8', 'AF3', 'C2', 'C1', 'P2', 'P1', 'F2', 'F1'],
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dtype=object)
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#电极三维坐标
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electrode_xyz = self.read_ch_pos()
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electrode_xyz.update({'A1': [-0.095, 0, -0.005]})
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electrode_xyz = {key: electrode_xyz[key] for key in electrode_name}
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electrode_xyz = np.array(list(electrode_xyz.values()))
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#电极坐标所属的坐标系
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electrode_coord_system = '10-20 spherical model'
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#受试者ID
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Subject_id = subject_id
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#Session ID
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Session_id = session_id
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#参考电极方案
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ref = 'CPZ'
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#数据采集开始时间
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start_time = 0
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meta_struct = {
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'subject_id': Subject_id,
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'session_id': Session_id,
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'ref': ref,
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'start_time': start_time
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}
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eeg_struct = {
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'data': Data,
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'chn': channel_names,
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'sample_rate': sample_rate,
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'node_number': node_number,
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't': t,
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'electrode_name': electrode_name,
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'electrode_xyz': electrode_xyz,
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'electrode_coord_system': electrode_coord_system,
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'meta': meta_struct,
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}
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fileDir = os.path.join('EEGfiles/',Subject_id,Session_id)
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os.makedirs(fileDir,exist_ok=True)
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filePath = os.path.join(fileDir,'eeg_data{}.mat'.format(self.file_num))
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# 保存到 .mat 文件，顶层变量名为 'eeg'
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savemat(filePath, {'eeg': eeg_struct})
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print('EEGfile saved at {}'.format(filePath))
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self.zmqClient.send_to_all('filePath', filePath)
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self.file_num += 1
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def read_ch_pos(self,file_path=r'xy_64.xlsx'):
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"""
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将电极位置信息转换为Dict
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参数:
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file_path: 电极位置存储文件, 必须包含'channel', 'x', 'y', 'z'列
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"""
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if getattr(sys, 'frozen', False):
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script_dir = sys._MEIPASS
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else:
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script_dir = os.path.dirname(os.path.abspath(__file__))
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file_path = os.path.join(script_dir, file_path)
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df = pd.read_excel(file_path)
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# 确保列名正确
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if not all(col in df.columns for col in ['channel', 'x', 'y', 'z']):
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raise ValueError("DataFrame必须包含'channel', 'x', 'y', 'z'列")
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# 创建电极位置字典
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ch_pos = {}
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for _, row in df.iterrows():
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ch_pos[row['channel']] = [row['x'], row['y'], row['z']]
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return ch_pos
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def _init_filter_cache(self):
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"""初始化滤波器系数缓存"""
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self._filter_cache = {
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'highpass': None,
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'lowpass': None,
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'notch': None
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}
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self._cache_valid = False
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def _design_filters(self):
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"""设计滤波器系数"""
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if self._cache_valid:
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return
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nyquist = self.fs / 2
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fs_nyq = self.fs
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# 高通滤波 (去除低频漂移)
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high = self.lowcut / nyquist
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if 0 < high < 1:
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self._filter_cache['highpass'] = signal.butter(2, high, btype='high', output='ba')
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# 低通滤波 (去除高频噪声)
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low = self.highcut / nyquist
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if 0 < low < 1:
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self._filter_cache['lowpass'] = signal.butter(4, low, btype='low', output='ba')
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# 50Hz 陷波滤波 (去除工频干扰)
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Q = 30 # 品质因子
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self._filter_cache['notch'] = signal.iirnotch(self.notch_freq, Q, fs=fs_nyq)
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# 查找CPZ通道索引
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electrode_name = ['FP1', 'FP2', 'PO6', 'POZ', 'F3', 'F4', 'FPZ', 'AF4', 'FC3', 'PO8', 'CP2', 'CP1',
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'FCZ', 'PO5', 'FC2', 'FC1', 'C3', 'C4', 'FC4', 'CP4', 'P3', 'P4', 'F5', 'C5', 'F6',
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'PO4', 'CP6', 'CP5', 'PO3', 'CP3', 'FC6', 'FC5', 'CB1', 'CB2', 'P5', 'AF7', 'A1','T7',
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'FT7', 'TP7', 'FT8', 'AF8', 'F8', 'F7', 'P6', 'C6', 'O2', 'O1', 'T8', 'P7', 'CZ','PZ',
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'P8', 'FZ', 'OZ', 'PO7', 'TP8', 'AF3', 'C2', 'C1', 'P2', 'P1', 'F2', 'F1']
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try:
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self.ref_chan_idx = electrode_name.index(self.ref_chan_name)
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except ValueError:
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self.ref_chan_idx = 50 # 默认CZ (对应索引50)
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print(f'[警告] 未找到参考电极 {self.ref_chan_name}，使用默认值 CZ')
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self._cache_valid = True
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print(f'[INFO] 预处理已启用 - 高通:{self.lowcut}Hz, 低通:{self.highcut}Hz, 陷波:{self.notch_freq}Hz, 参考:{self.ref_chan_name}(索引:{self.ref_chan_idx})')
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def check_data_quality(self, data):
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"""
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检查数据质量
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返回:
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list: 发现的问题列表，空列表表示质量正常
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"""
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issues = []
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# 检查幅度
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amplitude = np.max(np.abs(data))
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if amplitude > 1e6: # 超过 1mV = 1000µV
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issues.append(f'幅度异常: {amplitude:.2e} (可能为原始ADC值或单位错误)')
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elif amplitude > 1000:
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issues.append(f'幅度偏高: {amplitude:.2f}')
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# 检查平坦噪声 (通道可能未连接)
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if np.std(data) < 0.01:
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issues.append('信号过平，可能通道未连接')
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# 检查饱和
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n_saturated = np.sum(np.abs(data) > 1e8)
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if n_saturated > 0:
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issues.append(f'检测到 {n_saturated} 个采样点饱和')
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return issues
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def verify_and_calibrate_unit(self, data):
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"""
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验证并校准数据单位
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SunnyLinker64 的转换公式:
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val = raw_adc * 4.5 / gain_value / 8388608 * 1000000 (µV)
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但如果硬件实际增益与 gain_value=6 不符，会导致单位错误。
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本函数通过检测数据范围来验证和修正单位。
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正常EEG信号范围: ±50-100 µV
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如果检测到的数据范围是 ±1e6 量级，说明转换可能有问题
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参数:
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data: 原始数据
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返回:
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tuple: (校准后的数据, 是否进行了校准)
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"""
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if self._conversion_verified:
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return data, False
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amplitude = np.max(np.abs(data))
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# 判断数据是否在合理范围内
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# 正常EEG: 1 - 1000 µV (考虑某些高幅值情况)
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# 异常: > 1e5 µV (可能是ADC原始值未转换或转换系数错误)
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if amplitude > 1e6:
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print('[警告] 检测到异常大幅值数据，可能是ADC原始值或单位转换失败!')
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print(f' 当前最大幅度: {amplitude:.2e} µV')
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print('[INFO] 尝试自动校准单位转换...')
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# SunnyLinker64 的理论转换系数约为 0.0894 µV/LSB
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# 如果数据是原始ADC值，需要除以这个系数来还原
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theoretical_scale = 4.5 / 6 / 8388608 * 1e6 # 理论系数: ~0.0894 µV/LSB
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# 计算校准系数
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# 假设数据是原始ADC值，需要除以 (amplitude / expected_amplitude)
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# 正常EEG信号预期幅度约 100 µV
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expected_amplitude = 100.0 # µV
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if amplitude > expected_amplitude:
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# 计算校准系数: 原始值 / 预期值 = 实际值 / 校准后值
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self.calibration_scale = expected_amplitude / amplitude
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|
||||
# 应用校准
|
||||
data = data * self.calibration_scale
|
||||
print(f'[INFO] 校准完成，应用系数: {self.calibration_scale:.6e}')
|
||||
print(f' 校准后最大幅度: {np.max(np.abs(data)):.2f} µV')
|
||||
self._conversion_verified = True
|
||||
return data, True
|
||||
|
||||
elif amplitude < 0.01:
|
||||
print('[警告] 数据幅度接近零，可能通道未连接或设备异常')
|
||||
|
||||
self._conversion_verified = True
|
||||
return data, False
|
||||
|
||||
def preprocess_data(self, data):
|
||||
"""
|
||||
EEG信号预处理
|
||||
|
||||
参数:
|
||||
data: ndarray, shape (n_chan, n_samples), 原始EEG数据
|
||||
|
||||
返回:
|
||||
ndarray: 预处理后的EEG数据
|
||||
"""
|
||||
if not self.enable_preprocess:
|
||||
return data
|
||||
|
||||
# 确保数据是 float64 类型
|
||||
data = data.astype(np.float64)
|
||||
|
||||
# 设计滤波器
|
||||
self._design_filters()
|
||||
|
||||
# 1. 去除直流分量和低频漂移 (高通滤波)
|
||||
if self._filter_cache['highpass'] is not None:
|
||||
b, a = self._filter_cache['highpass']
|
||||
for ch in range(data.shape[0]):
|
||||
data[ch, :] = signal.filtfilt(b, a, data[ch, :])
|
||||
|
||||
# 2. 50Hz 工频陷波滤波
|
||||
if self._filter_cache['notch'] is not None:
|
||||
b, a = self._filter_cache['notch']
|
||||
for ch in range(data.shape[0]):
|
||||
data[ch, :] = signal.filtfilt(b, a, data[ch, :])
|
||||
|
||||
# 3. 低通滤波 (去除高频噪声)
|
||||
if self._filter_cache['lowpass'] is not None:
|
||||
b, a = self._filter_cache['lowpass']
|
||||
for ch in range(data.shape[0]):
|
||||
data[ch, :] = signal.filtfilt(b, a, data[ch, :])
|
||||
|
||||
# 4. 重参考 (以CPZ为参考)
|
||||
if self.ref_chan_idx is not None and self.ref_chan_idx < data.shape[0]:
|
||||
ref_signal = data[self.ref_chan_idx, :]
|
||||
data = data - ref_signal
|
||||
|
||||
return data
|
||||
|
||||
def stop(self):
|
||||
'''
|
||||
停止运行
|
||||
@return:
|
||||
'''
|
||||
self.zmqServer.stop()
|
||||
self.Running=False
|
||||
Reference in New Issue
Block a user