数据分析实战＜一＞脑电(EEG)分析(数据分析实战)

数据分析实战＜一＞脑电(EEG)分析

推荐整理分享数据分析实战＜一＞脑电(EEG)分析(数据分析实战)，希望有所帮助，仅作参考，欢迎阅读内容。

文章相关热门搜索词:数据分析实战训练营,数据分析实战案例宝典,数据分析实战案例宝典,数据分析实战,数据分析实战案例,数据分析实战项目整理,数据分析实战45讲,r语言医学数据分析实战,内容如对您有帮助，希望把文章链接给更多的朋友！

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 npimport mnefrom mne.time_frequency import psd_welchimport 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 not in 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epochepochs = mne.make_fixed_length_epochs(raw, duration=30, preload=False)

4. 特征提取

def eeg_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)if not 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 npimport mnefrom mne.time_frequency import psd_welchimport 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 not in 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 ##################def eeg_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)if not 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 defaultdictfrom json import dump, loadrawlabelFile = "result/rawLabel.json"# 文件夹下面的名字fileName = ["hxx.edf", "qyp.edf"]# 对应的labelraws = []raw1 = [(4, 5), 6, 7, 7, 6, 6] # hxxraw2 = [4, 6, 7, (7, 6), (7, 5),5] #qupraws.append(raw1)raws.append(raw2)ans = dict(zip(fileName, raws))print(ans)with open(rawlabelFile, 'w') as fp: dump(ans, fp)with open(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

步骤2：

def extractLabel(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 in enumerate(raw): med = min(n, (i + 1) * splitTime) mbg = i * splitTime if (str.isdigit(str(x))): x = 0 if x <= spliteY else 1 # [1,4]打成0，否则打成1 for _ in range(mbg, med): label.append(x) else: ## 一次回答两个数值的情况 x, y = x x = 0 if x <= spliteY else 1 y = 0 if y <= spliteY else 1 for _ in range(mbg, mbg + splitTime // 2): label.append(x) for _ in range(mbg + splitTime // 2, med): label.append(y) return label2. 使用随机森林进行分类"""@author:fuzekun@file:myFile.py@time:2022/10/10@description:"""import numpy as npfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, classification_reportfrom sklearn.model_selection import train_test_splitX_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))

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 osfrom json import loadimport numpy as npimport mnefrom mne.time_frequency import psd_welchdataFilePath = "D:/data/eeg"rawLabelFile = "result/rawLabel.json"save_X = "D:/data/X.npy"save_y = "D:/data/y.npy"def extractFeture(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 not in 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 ################## def eeg_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 ansdef extractLabel(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 in enumerate(raw): med = min(n, (i + 1) * splitTime) mbg = i * splitTime if (str.isdigit(str(x))): x = 0 if x <= spliteY else 1 # [1,4]打成0，否则打成1 for _ in range(mbg, med): label.append(x) else: ## 一次回答两个数值的情况 x, y = x x = 0 if x <= spliteY else 1 y = 0 if y <= spliteY else 1 for _ in range(mbg, mbg + splitTime // 2): label.append(x) for _ in range(mbg + splitTime // 2, med): label.append(y) return labeldef extract(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 = [] with open(rawLabelFile, 'r') as fp: AllRawLabels = load(fp) for fileName in os.listdir(filePath): file = os.path.join(filePath, fileName) if not 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, yX, 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 npfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, classification_reportfrom sklearn.model_selection import train_test_splitX_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 pdimport numpy as npfrom statsmodels.formula.api import olsfrom scipy import statsimport statsmodels.api as smimport matplotlib.pyplot as pltfeature_name_list = []for i in range(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 in enumerate(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)

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：

2. 精确度分析

使用7个文件分类效果如下所示：精确度78%

3. anova分析

可以看到显著性较为明显

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 defaultdictfrom json import dump, loadrawlabelFile = "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labelraws = []raw1 = [(4, 5), 6, 7, 7, 6, 6] # hxxraw2 = [4, 6, 7, (7, 6), (7, 5),5] #qupraw3 = [7,(9, 8),(5, 7),(5, 4),4,3] # 1_1raw4 = [2,5,4,5,7,8] # 4_1raw5 = [7,7,8,7,6,5] # 7_1raw6 = [3,5,8,6,9,5] # 11_1raw7 = [3,4,5,5,6,5] # cskraws.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)with open(rawlabelFile, 'w') as fp: dump(ans, fp)with open(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 osfrom json import loadimport numpy as npimport mnefrom 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', ]def extractFeture(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 not in 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 ################## def eeg_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 ansdef extractLabel(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 in enumerate(raw): med = min(n, (i + 1) * splitTime) mbg = i * splitTime if (str.isdigit(str(x))): x = 0 if x <= spliteY else 1 # [1,4]打成0，否则打成1 for _ in range(mbg, med): label.append(x) else: ## 一次回答两个数值的情况 x, y = x x = 0 if x <= spliteY else 1 y = 0 if y <= spliteY else 1 for _ in range(mbg, mbg + splitTime // 2): label.append(x) for _ in range(mbg + splitTime // 2, med): label.append(y) return labeldef extract(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 = [] with open(rawLabelFile, 'r') as fp: AllRawLabels = load(fp) for fileName in os.listdir(filePath): print('##################' + fileName + '###################') file = os.path.join(filePath, fileName) if not 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, yX, 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