新闻资讯

产品展示

EMOTIV Epoc X 无线便携式脑电仪在国际上多次获得殊荣的EMOTIV Epoc X是一个14通道无线脑电图（EEG）,设计更符合实际的研究，先进的脑机接口(BCI)应用程序。通过Pure•EEG软件EPOC+能提供密集的记录数组,高品质、原始脑电图数据。VIEW MORE

新闻资讯

清华大学主办，全球学者共话人因与智能交互免费！高效 EEG/ERP 数据分析 MATLAB 工具分享脑电(EEG)公开数据集/平台汇总 EDA皮电信号采集与分析技术综述优化EEG ICA分解：静态与移动实验中数据清理的方法研究

学习与分享

您的当前位置：首页 > 新闻资讯 > 学习与分享

肌肉疲劳度分析指导

来源：未知 │ 发表时间：2021-07-15　| 浏览数：载入中...

肌肉疲劳度分析指导

肌肉疲劳研究和监测是一个很有前途的研究领域，已有的研究已证明从EMG信号中提取的某些参数会随着疲劳的获取而以特定方式演变。在这些参数之中，并可能是较为一致的参数，便是中值功率频率
1. 输入所需数据包

[1]

# OpenSignals Tools own package for loading and plotting the acquired data

import biosignalsnotebooks as bsnb

import biosignalsnotebooks.signal_samples as bsnb_ss

# Scientific packages

from numpy import linspace, where

from scipy.signal import periodogram

from scipy.integrate import cumtrapz

2. 加载EMG数据，收集肌肉疲劳诱发期间的数据

[2]

# Data loading

data, header = bsnb_ss.load_signal("emg_fatigue", get_header=True)

3.识别采集期间使用的通道

[3]

channel = "CH" + str(header["channels"][0])

4.采样率和获取数据样本

[4]

# Sampling rate and acquired data

sr = header["sampling rate"]

# Signal Samples

signal = data[channel]time = linspace(0, len(signal) / sr, len(signal))

5. 肌肉激活侦测

[5]

activation_begin, activation_end = bsnb.detect_emg_activations(signal, sr)[:2]

detect_emg_activations 默认调用的函数是：detect_emg_activations（emg_signal，sample_rate，smooth_level=20，threshold_level=10，time_units=False，volts=False，resolution=None，device="biosignalssplux"，plot_result=False）

bsnb.plot_compare_act_config(signal, sr)

7. 提取表征每个肌肉激活的中值功率频率（处理窗口）
中值功率频率定义为允许功率谱划分为具有相等功率的两个区域的频率值

8. [7]

 Iteration along muscular activations

median_freq_data = []

median_freq_time = []

for activation in range(0, len(activation_begin)):

    processing_window = signal[activation_begin[activation]:activation_end[activation]]

    central_point = (activation_begin[activation] + activation_end[activation]) / 2

    median_freq_time += [central_point / sr]

    # Processing window power spectrum (PSD) generation

    freqs, power = periodogram(processing_window, fs=sr)

    # Median power frequency determination

    area_freq = cumtrapz(power, freqs, initial=0)

    total_power = area_freq[-1]

    median_freq_data += [freqs[where(area_freq >= total_power / 2)[0][0]]]

    # The previous indexation [0][0] was specified in order to only the first sample that

    # verifies the condition area_freq >= total_power / 2 be returned (all the subsequent

    # samples will verify this condition, but, we only want the frequency that is nearest

    # to the ideal frequency value that divides power spectrum into to regions with the

# same power - which is not achievable in a digital processing perspective)

9. 随着时间序列的变化中值频率的变化

10.

bsnb.fatigue_eval_med_freq(signal, sr)

OUT[10]

上一个ISBS 2018 生物力学学术报告

下一个北京京显参加第七届全国脑电与脑成像研究与应用研讨会