数据分析实战＜一＞脑电(EEG)分析

这两天需要对预实验的脑电进行一个分类，在这里记录一下流程

脑电分析系列文章
mne官网
mne教程
随机森林分类
Python 多因素方差分析

文章目录

1. 脑电数据的处理

1.1 基本概念

由于是刚刚学习的一些概念，这里就不做过多的解释贻笑大方了。就简单说一下自己的理解。

raw ：读取脑电的原始数据，里面重要的数据结构如下： - info：记录一些备注信息，比如哪些是坏通道- ch_name：采集数据用到的channel名字
epoch：把原始的数据划分成段，方便后续的分析
annotation/event: 每一段epoch的标签,两者区别具体看官方文档。不过用起来基本是一样的，并且有mne.events_from_annotations和events_from_annotations可以相互转换。

了解完以上的概念之后，就可以进行实际的操作了。

1.2 实际处理

实际需求

由于采集数据的时候，没有进行annotation和event的标记，所以直接使用时间作为划分epoch的依据，这里采用30s作为一个epoch。
采集的是全64个通道，但是真正采集的就9个通道，需要对通道进行过滤
每20Min受试者报告一次或者两次本阶段的困倦程度，所以10 or 20min内的所有epoch都是一样的annotation
由于采集的时候电解液的风干，以及电极帽的接触不良，所以有的通道的数据可能不能用，应该进行过滤。

处理过程

1. 数据读取

import numpy as np
import mne
from mne.time_frequency import psd_welch
import os

################# 文件路径 ######################
filename ="D:/data/eeg/physionet-sleep-data/SC4001E0-PSG.edf"
filename2 ="D:/data/eeg/qyp.edf"
savepath ="result"## 使用hxx作为训练数据, qyp作为测试数据
resultName ="features_test.npy"# ############# 1. 读取文件 #################
raw = mne.io.read_raw(filename2, preload=True)

2. 数据预处理

数据预处理分成两步：

过滤不必要的频域
过滤不需要的通道

############# 2. 预处理 ###################### 过滤高低频域，过滤防止0的干扰，同时减少数据量
raw.filter(l_freq=0.1, h_freq=40)# 选择通道
alllist =['Fpz','Fp1','Fp2','AF3','AF4','AF7','AF8','Fz','F1','F2','F3','F4','F5','F6','F7','F8','FCz','FC1','FC2','FC3','FC4','FC5','FC6','FT7','FT8','Cz','C1','C2','C3','C4','C5','C6','T7','T8','CP1','CP2','CP3','CP4','CP5','CP6','TP7','TP8','Pz','P3','P4','P5','P6','P7','P8','POz','PO3','PO4','PO5','PO6','PO7','PO8','Oz','O1','O2','ECG','HEOR','HEOL','VEOU','VEOL']
goodlist =['FCz','Pz','Fpz','Cz','C1','C2','O1','O2','Oz',]
goodlist =set(goodlist)
badlist =[]for x in alllist:if x notin goodlist:
        badlist.append(x)
picks = mne.pick_channels(alllist, goodlist, badlist)
raw.plot(order=picks, n_channels=len(picks))for x in badlist:
    raw.info['bads'].append(x)

3. 按时间划分epoch

# 30s一个epoch
epochs = mne.make_fixed_length_epochs(raw, duration=30, preload=False)

4. 特征提取

defeeg_power_band(epochs):"""脑电相对功率带特征提取
    该函数接受一个""mne.Epochs"对象，
    并基于与scikit-learn兼容的特定频带中的相对功率创建EEG特征。
    Parameters
    ----------
    epochs : Epochs
        The data.
    Returns
    -------
    X : numpy array of shape [n_samples, 5]
        Transformed data.
    """# 特定频带
    FREQ_BANDS ={"delta":[0.5,4.5],"theta":[4.5,8.5],"alpha":[8.5,11.5],"sigma":[11.5,15.5],"beta":[15.5,30]}

    psds, freqs = psd_welch(epochs, picks='eeg', fmin=0.5, fmax=30.)# 归一化 PSDs,这个数组中含有0元素，所以会出现问题，正确的解决方式，从epoch中去除或者从数组中去除# psds = np.where(psds < 0.1, 0.1, psds)# sm = np.sum(psds, axis=-1, keepdims=True)# psds = numpy.divide(psds, sm)
    psds /= np.sum(psds, axis=-1, keepdims=True)

    X =[]for fmin, fmax in FREQ_BANDS.values():
        psds_band = psds[:,:,(freqs >= fmin)&(freqs < fmax)].mean(axis=-1)
        X.append(psds_band.reshape(len(psds),-1))return np.concatenate(X, axis=1)

5. 保存文件

由于实验2个小时，所以会划分成240个epoch，但是有的可能会长，有的可能会短一个，长的应该直接舍去，因为这个是实验之后关闭设备采到的。

ans = eeg_power_band(epochs)# 截取前240个数据if ans.shape[0]>240:
    ans = ans[:240]print(ans.shape)## hxx(240, 45), qyp (239, 45)ifnot os.path.exists(savepath):
    os.mkdir(savepath)
np.save(savepath +"/"+ resultName, ans)

1.3 全部代码

"""
@author:fuzekun
@file:myFile.py
@time:2022/10/10
@description:
"""import numpy as np
import mne
from mne.time_frequency import psd_welch
import os

################# 文件路径 ######################
filename ="D:/data/eeg/physionet-sleep-data/SC4001E0-PSG.edf"
filename2 ="D:/data/eeg/qyp.edf"
savepath ="result"## 使用hxx作为训练数据, qyp作为测试数据
resultName ="features_test.npy"# ############# 1. 读取文件 #################
raw = mne.io.read_raw(filename2, preload=True)# ############ 2. 预处理 ###################### # 过滤防止0的干扰
raw.filter(l_freq=0.1, h_freq=40)# # 选择通道
alllist =['Fpz','Fp1','Fp2','AF3','AF4','AF7','AF8','Fz','F1','F2','F3','F4','F5','F6','F7','F8','FCz','FC1','FC2','FC3','FC4','FC5','FC6','FT7','FT8','Cz','C1','C2','C3','C4','C5','C6','T7','T8','CP1','CP2','CP3','CP4','CP5','CP6','TP7','TP8','Pz','P3','P4','P5','P6','P7','P8','POz','PO3','PO4','PO5','PO6','PO7','PO8','Oz','O1','O2','ECG','HEOR','HEOL','VEOU','VEOL']
goodlist =['FCz','Pz','Fpz','Cz','C1','C2','O1','O2','Oz',]
goodlist =set(goodlist)
badlist =[]for x in alllist:if x notin goodlist:
        badlist.append(x)
picks = mne.pick_channels(alllist, goodlist, badlist)
raw.plot(order=picks, n_channels=len(picks))for x in badlist:
    raw.info['bads'].append(x)# ############## 2. 切分成epochs ################
epochs = mne.make_fixed_length_epochs(raw, duration=30, preload=False)# ############# 3 特征提取 ##################defeeg_power_band(epochs):"""脑电相对功率带特征提取
    该函数接受一个""mne.Epochs"对象，
    并基于与scikit-learn兼容的特定频带中的相对功率创建EEG特征。
    Parameters
    ----------
    epochs : Epochs
        The data.
    Returns
    -------
    X : numpy array of shape [n_samples, 5]
        Transformed data.
    """# 特定频带
    FREQ_BANDS ={"delta":[0.5,4.5],"theta":[4.5,8.5],"alpha":[8.5,11.5],"sigma":[11.5,15.5],"beta":[15.5,30]}

    psds, freqs = psd_welch(epochs, picks='eeg', fmin=0.5, fmax=30.)# 归一化 PSDs,这个数组中含有0元素，所以会出现问题，正确的解决方式，从epoch中去除或者从数组中去除# psds = np.where(psds < 0.1, 0.1, psds)# sm = np.sum(psds, axis=-1, keepdims=True)# psds = numpy.divide(psds, sm)
    psds /= np.sum(psds, axis=-1, keepdims=True)

    X =[]for fmin, fmax in FREQ_BANDS.values():
        psds_band = psds[:,:,(freqs >= fmin)&(freqs < fmax)].mean(axis=-1)
        X.append(psds_band.reshape(len(psds),-1))return np.concatenate(X, axis=1)

ans = eeg_power_band(epochs)# 截取前240个数据if ans.shape[0]>240:
    ans = ans[:240]print(ans.shape)## hxx(240, 45), qyp (239, 45)ifnot os.path.exists(savepath):
    os.mkdir(savepath)
np.save(savepath +"/"+ resultName, ans)

2. 随机森林分类

1. label的制作

label制作分成两部分：

将原始的label从excel或者txt文件中提取出来
对原始label进行扩展，形成维度和特征相同的label列表

步骤1：

# 没有操作excel，直接复制粘贴的，应该采用excel的操作更加通用一些"""
@author:fuzekun
@file:generateLabelFile.py
@time:2022/10/12
@description: 由原始标签生成每一个用户的id:label对应的set
"""from collections import defaultdict
from json import dump, load

rawlabelFile ="result/rawLabel.json"# 文件夹下面的名字
fileName =["hxx.edf","qyp.edf"]# 对应的label
raws =[]
raw1 =[(4,5),6,7,7,6,6]# hxx
raw2 =[4,6,7,(7,6),(7,5),5]#qup
raws.append(raw1)
raws.append(raw2)

ans =dict(zip(fileName, raws))print(ans)withopen(rawlabelFile,'w')as fp:
    dump(ans, fp)withopen(rawlabelFile,'r')as fp:
    ans = load(fp)print(ans)

将用户的文件名和label做一个映射：类似下面这种

在这里插入图片描述
步骤2：

defextractLabel(raw, n, spliteY, splitT, totalT):"""
    raw:原始标记
    n: 特征的数量
    spliteY: 划分的阈值
    splitT: 划分epoch的时间 s
    totalT: 总时常 h
    """# 总共有多少段，然后每一个标签应该对应多少段
    splitTime = totalT *3600// splitT //len(raw)
    label =[]for i, x inenumerate(raw):
        med =min(n,(i +1)* splitTime)
        mbg = i * splitTime
        if(str.isdigit(str(x))):
            x =0if x <= spliteY else1# [1,4]打成0，否则打成1for _ inrange(mbg, med):
                label.append(x)else:## 一次回答两个数值的情况
            x, y = x
            x =0if x <= spliteY else1
            y =0if y <= spliteY else1for _ inrange(mbg, mbg + splitTime //2):
                label.append(x)for _ inrange(mbg + splitTime //2, med):
                label.append(y)return label

2. 使用随机森林进行分类

"""
@author:fuzekun
@file:myFile.py
@time:2022/10/10
@description:
"""import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split

X_file ="result/X.npy"
y_file ="result/y.npy"

X = np.load(X_file)
y = np.load(y_file)

Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3)
clf = RandomForestClassifier(max_depth=2,random_state=0)
clf.fit(Xtrain, Ytrain)# print(clf.feature_importances_)

y_predict = clf.predict(Xtest)
acc = accuracy_score(Ytest, y_predict)print("Accuracy score: {}".format(acc))print(classification_report(Ytest, y_predict))

在这里插入图片描述
可以看到效果还是比较显著的。

3. 全部代码

整合数据

"""
@author:fuzekun
@file:extractFetures.py
@time:2022/10/12
@description: 特征提取
"""import os
from json import load

import numpy as np
import mne
from mne.time_frequency import psd_welch

dataFilePath ="D:/data/eeg"
rawLabelFile ="result/rawLabel.json"
save_X ="D:/data/X.npy"
save_y ="D:/data/y.npy"defextractFeture(dataFile:str, ch_name:list, spliteT:int, totalT:int):"""
    根据文件和chnnel提取特征
    dataFile: 文件名
    ch_name: 通道名称
    spliteT:划分的时间s
    totalT: 总时长h
    return: 返回特征numpys数组
    """# ############# 1. 读取文件 #################
    raw = mne.io.read_raw(dataFile, preload=True)# ############ 2. 预处理 ###################### # 过滤防止0的干扰
    raw.filter(l_freq=0.1, h_freq=40)# # 选择通道
    alllist =['Fpz','Fp1','Fp2','AF3','AF4','AF7','AF8','Fz','F1','F2','F3','F4','F5','F6','F7','F8','FCz','FC1','FC2','FC3','FC4','FC5','FC6','FT7','FT8','Cz','C1','C2','C3','C4','C5','C6','T7','T8','CP1','CP2','CP3','CP4','CP5','CP6','TP7','TP8','Pz','P3','P4','P5','P6','P7','P8','POz','PO3','PO4','PO5','PO6','PO7','PO8','Oz','O1','O2','ECG','HEOR','HEOL','VEOU','VEOL']
    goodlist = ch_name
    goodlist =set(goodlist)
    badlist =[]for x in alllist:if x notin goodlist:
            badlist.append(x)# picks = mne.pick_channels(alllist, goodlist, badlist)# raw.plot(order=picks, n_channels=len(picks))for x in badlist:
        raw.info['bads'].append(x)# ############## 2. 切分成epochs ################
    epochs = mne.make_fixed_length_epochs(raw, duration=spliteT, preload=False)# ############# 3 特征提取 ##################defeeg_power_band(epochs):"""脑电相对功率带特征提取
        该函数接受一个""mne.Epochs"对象，
        并基于与scikit-learn兼容的特定频带中的相对功率创建EEG特征。
        Parameters
        ----------
        epochs : Epochs
            The data.
        Returns
        -------
        X : numpy array of shape [n_samples, 5]
            Transformed data.
        """# 特定频带
        FREQ_BANDS ={"delta":[0.5,4.5],"theta":[4.5,8.5],"alpha":[8.5,11.5],"sigma":[11.5,15.5],"beta":[15.5,30]}

        psds, freqs = psd_welch(epochs, picks='eeg', fmin=0.5, fmax=30.)# 归一化 PSDs,这个数组中含有0元素，所以会出现问题，正确的解决方式，从epoch中去除或者从数组中去除# psds = np.where(psds < 0.1, 0.1, psds)# sm = np.sum(psds, axis=-1, keepdims=True)# psds = numpy.divide(psds, sm)
        psds /= np.sum(psds, axis=-1, keepdims=True)

        X =[]for fmin, fmax in FREQ_BANDS.values():
            psds_band = psds[:,:,(freqs >= fmin)&(freqs < fmax)].mean(axis=-1)
            X.append(psds_band.reshape(len(psds),-1))return np.concatenate(X, axis=1)

    ans = eeg_power_band(epochs)
    n = totalT *3600// spliteT
    # 截取前n个数据if ans.shape[0]> n:
        ans = ans[:n]print(ans.shape)## hxx(240, 45), qyp (239, 45)return ans

defextractLabel(raw, n, spliteY, splitT, totalT):"""
    raw:原始标记
    n: 特征的数量
    spliteY: 划分的阈值
    splitT: 划分epoch的时间 s
    totalT: 总时常 h
    """# 总共有多少段，然后每一个标签应该对应多少段
    splitTime = totalT *3600// splitT //len(raw)
    label =[]for i, x inenumerate(raw):
        med =min(n,(i +1)* splitTime)
        mbg = i * splitTime
        if(str.isdigit(str(x))):
            x =0if x <= spliteY else1# [1,4]打成0，否则打成1for _ inrange(mbg, med):
                label.append(x)else:## 一次回答两个数值的情况
            x, y = x
            x =0if x <= spliteY else1
            y =0if y <= spliteY else1for _ inrange(mbg, mbg + splitTime //2):
                label.append(x)for _ inrange(mbg + splitTime //2, med):
                label.append(y)return label

defextract(filePath, splitT, totalT, spliteY):# 提取文件夹下的全部efg作为训练集，同时进行标签的填充
    X =[]
    y =[]withopen(rawLabelFile,'r')as fp:
        AllRawLabels = load(fp)for fileName in os.listdir(filePath):file= os.path.join(filePath, fileName)ifnot os.path.isfile(file):continue
        ch_name =['FCz','Pz','Fpz','Cz','C1','C2','O1','O2','Oz',]
        features = extractFeture(file, ch_name, splitT, totalT)
        rawLabel = AllRawLabels[fileName]
        labels = extractLabel(rawLabel,len(features), spliteY, splitT, totalT)
        X.extend(list(features))
        y.extend(labels)return X, y

X, y = extract(dataFilePath,30,2)
X, y = np.array(X), np.array(y)print(X.shape, y.shape)
np.save(save_X, X)
np.save(save_y, y)

随机森林

"""
@author:fuzekun
@file:myFile.py
@time:2022/10/10
@description:
"""import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split

X_file ="result/X.npy"
y_file ="result/y.npy"

X = np.load(X_file)
y = np.load(y_file)

Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3)
clf = RandomForestClassifier(max_depth=2,random_state=0)
clf.fit(Xtrain, Ytrain)# print(clf.feature_importances_)

y_predict = clf.predict(Xtest)
acc = accuracy_score(Ytest, y_predict)print("Accuracy score: {}".format(acc))print(classification_report(Ytest, y_predict))

3. 显著性检验

显著性检验分成四步

读取numpy数组
将numpy数组转化成pandas数组
使用函数进行anova分析，首先进行主观效应，其次进行多重比较检验
根据结果判断是否影响是否显著

"""
@author:fuzekun
@file:anova.py
@time:2022/10/12
@description: 方差分析
"""import pandas as pd
import numpy as np
from statsmodels.formula.api import ols
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt

feature_name_list =[]for i inrange(45):
    feature_name_list.append('f'+str(i))# print(feature_name_list)

data_array = np.load("result/X.npy")
label_array = np.load("result/y.npy")

data_df = pd.DataFrame(data_array, columns=feature_name_list)
data_df['target']= label_array

# print(data_df)# print(label_df)
formula ='target ~ 'for i, x inenumerate(feature_name_list):if i !=len(feature_name_list)-1:
        formula += x +'+'else:
        formula += x

# print(data_df['f0'])
tired_anova = sm.stats.anova_lm(ols(formula,data = data_df).fit(),typ =3)print(tired_anova)

在这里插入图片描述

可以看到，上面的f0, f2, f4的远远小于了0.05, 也就是说拒绝了原假设，选择备择假设，可以说这些都是和target的相关性较强的点。

4. 多文件测试

1. 文件选择

使用了7个文件，选择了每一个文件的公共通道进行训练。部分脑电的图片如下所示：

在这里插入图片描述

2. 精确度分析

使用7个文件分类效果如下所示：精确度78%

在这里插入图片描述

3. anova分析

可以看到显著性较为明显

$[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-freWk28Q-1665668130424)(D:\blgs\source\imgs\image-20221013213403549.png)]$

4. 可扩展性

1. 抽取代码

Json文件中存放了原始的标签。可以通过下面的代码生成，也可以直接进行编写。

"""
@author:fuzekun
@file:generateLabelFile.py
@time:2022/10/12
@description: 由原始标签生成每一个用户的id:label对应的set
"""from collections import defaultdict
from json import dump, load

rawlabelFile ="result/rawLabel.json"# 文件夹下面的名字
fileName =["hxx.edf","qyp.edf","1_1.edf","4_1.edf","7_1.edf","11_1.edf","csk.edf"]# 对应的label
raws =[]
raw1 =[(4,5),6,7,7,6,6]# hxx
raw2 =[4,6,7,(7,6),(7,5),5]#qup
raw3 =[7,(9,8),(5,7),(5,4),4,3]# 1_1
raw4 =[2,5,4,5,7,8]# 4_1
raw5 =[7,7,8,7,6,5]# 7_1
raw6 =[3,5,8,6,9,5]# 11_1
raw7 =[3,4,5,5,6,5]# csk

raws.append(raw1)
raws.append(raw2)
raws.append(raw3)
raws.append(raw4)
raws.append(raw5)
raws.append(raw6)
raws.append(raw7)

ans =dict(zip(fileName, raws))print(ans)withopen(rawlabelFile,'w')as fp:
    dump(ans, fp)withopen(rawlabelFile,'r')as fp:
    ans = load(fp)print(ans)

将label和特征的生成抽取成文件。

划分epoch的时间进行抽取
实验的时长，以及每一次询问的间隔。需要满足整数次
选择的通道进行抽取

"""
@author:fuzekun
@file:extractFetures.py
@time:2022/10/12
@description: 特征提取
"""import os
from json import load

import numpy as np
import mne
from mne.time_frequency import psd_welch

### 原始的标签放在了rawLable.json文件中，可以进行编写，也可以直接修改generateLableFile生成。

dataFilePath ="D:/data/eeg"
rawLabelFile ="result/rawLabel.json"
save_X ="result/X.npy"
save_y ="result/y.npy"
ch_name =['FCz','Pz','Fpz','Cz','C1','C2',]defextractFeture(dataFile:str, ch_name:list, spliteT:int, totalT:int):"""
    根据文件和chnnel提取特征
    dataFile: 文件名
    ch_name: 通道名称
    spliteT:划分的时间s
    totalT: 总时长h
    return: 返回特征numpys数组

    totalT * 3600 / spliteT一定要能整除。
    """# ############# 1. 读取文件 #################
    raw = mne.io.read_raw(dataFile, preload=True)# ############ 2. 预处理 ###################### # 过滤防止0的干扰
    raw.filter(l_freq=0.1, h_freq=40)# # 选择通道
    alllist =['Fpz','Fp1','Fp2','AF3','AF4','AF7','AF8','Fz','F1','F2','F3','F4','F5','F6','F7','F8','FCz','FC1','FC2','FC3','FC4','FC5','FC6','FT7','FT8','Cz','C1','C2','C3','C4','C5','C6','T7','T8','CP1','CP2','CP3','CP4','CP5','CP6','TP7','TP8','Pz','P3','P4','P5','P6','P7','P8','POz','PO3','PO4','PO5','PO6','PO7','PO8','Oz','O1','O2','ECG','HEOR','HEOL','VEOU','VEOL']
    goodlist = ch_name
    goodlist =set(goodlist)
    badlist =[]for x in alllist:if x notin goodlist:
            badlist.append(x)# picks = mne.pick_channels(alllist, goodlist, badlist)# raw.plot(order=picks, n_channels=len(picks))for x in badlist:
        raw.info['bads'].append(x)# ############## 2. 切分成epochs ################
    epochs = mne.make_fixed_length_epochs(raw, duration=spliteT, preload=False)# ############# 3 特征提取 ##################defeeg_power_band(epochs):"""脑电相对功率带特征提取
        该函数接受一个""mne.Epochs"对象，
        并基于与scikit-learn兼容的特定频带中的相对功率创建EEG特征。
        Parameters
        ----------
        epochs : Epochs
            The data.
        Returns
        -------
        X : numpy array of shape [n_samples, 5]
            Transformed data.
        """# 特定频带
        FREQ_BANDS ={"delta":[0.5,4.5],"theta":[4.5,8.5],"alpha":[8.5,11.5],"sigma":[11.5,15.5],"beta":[15.5,30]}

        psds, freqs = psd_welch(epochs, picks='eeg', fmin=0.5, fmax=30.)# 归一化 PSDs,这个数组中含有0元素，所以会出现问题，正确的解决方式，从epoch中去除或者从数组中去除# psds = np.where(psds < 0.1, 0.1, psds)# sm = np.sum(psds, axis=-1, keepdims=True)# psds = numpy.divide(psds, sm)
        psds /= np.sum(psds, axis=-1, keepdims=True)

        X =[]for fmin, fmax in FREQ_BANDS.values():
            psds_band = psds[:,:,(freqs >= fmin)&(freqs < fmax)].mean(axis=-1)
            X.append(psds_band.reshape(len(psds),-1))return np.concatenate(X, axis=1)

    ans = eeg_power_band(epochs)
    n = totalT *3600// spliteT
    # 截取前n个数据if ans.shape[0]> n:
        ans = ans[:n]print(ans.shape)return ans

defextractLabel(raw, n, spliteY, splitT, totalT):"""
    raw:原始标记
    n: 特征的数量
    spliteY: 划分的阈值
    splitT: 划分epoch的时间 s
    totalT: 总时常 h
    """# 总共有多少段，然后每一个标签应该对应多少段# 注意这个一定要能整除，否则出现标签数量过少的情况
    splitTime = totalT *3600// splitT //len(raw)
    label =[]for i, x inenumerate(raw):
        med =min(n,(i +1)* splitTime)
        mbg = i * splitTime
        if(str.isdigit(str(x))):
            x =0if x <= spliteY else1# [1,4]打成0，否则打成1for _ inrange(mbg, med):
                label.append(x)else:## 一次回答两个数值的情况
            x, y = x
            x =0if x <= spliteY else1
            y =0if y <= spliteY else1for _ inrange(mbg, mbg + splitTime //2):
                label.append(x)for _ inrange(mbg + splitTime //2, med):
                label.append(y)return label

defextract(filePath, splitT, totalT, spliteY, ch_name):# 提取文件夹下的全部efg作为训练集，同时进行标签的填充
    X =[]
    y =[]withopen(rawLabelFile,'r')as fp:
        AllRawLabels = load(fp)for fileName in os.listdir(filePath):print('##################'+ fileName +'###################')file= os.path.join(filePath, fileName)ifnot os.path.isfile(file):continue
        features = extractFeture(file, ch_name, splitT, totalT)
        rawLabel = AllRawLabels[fileName]
        labels = extractLabel(rawLabel,len(features), spliteY, splitT, totalT)print(len(features),len(labels))
        X.extend(list(features))
        y.extend(labels)return X, y

X, y = extract(dataFilePath,30,2,4, ch_name)
X, y = np.array(X), np.array(y)print(X.shape, y.shape)
np.save(save_X, X)
np.save(save_y, y)

2. 有待扩展

应该从excel文件中直接读取，但是没有这样做，还需要手动写。
实验如果有中断，每一个文件的时常会变化，单是extract()函数默认每一个文件的时长和划分的间隔都是一样的。可以修改extract函数，将每一个文件的时长形成列表。然后分别extractLable和extractFeatur

标签：数据分析 python 人工智能

本文转载自: https://blog.csdn.net/fuzekun/article/details/127273769
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数据分析实战＜一＞脑电(EEG)分析

文章目录

1. 脑电数据的处理

1.1 基本概念

1.2 实际处理

1.3 全部代码

2. 随机森林分类

1. label的制作

2. 使用随机森林进行分类

3. 全部代码

3. 显著性检验

4. 多文件测试

1. 文件选择

2. 精确度分析

3. anova分析

4. 可扩展性

1. 抽取代码

2. 有待扩展

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