使用python接受tgam的脑波数据实例

废话不多说，来看看实例吧！

# -*- coding: utf-8 -*-
import serial 

filename='yjy.txt'
t = serial.Serial('COM5',57600)
b=t.read(3)
vaul=[]
i=0
y=0
p=0
while b[0]!=170 or b[1]!=170 or b[2]!=4:
 b=t.read(3)
 print(b)
if b[0]==b[1]==170 and b[2]==4:
 a=b+t.read(5)
 print(a)
 if a[0] == 170 and a[1]==170 and a[2]==4 and a[3]==128 and a[4]==2:
 while 1:
  i=i+1
#  print(i)
  a=t.read(8)
#  print(a)
  sum=((0x80+0x02+a[5]+a[6])^0xffffffff)&0xff
  if a[0]==a[1]==170 and a[2]==32:
  y=1
  else:
  y=0
  if a[0] == 170 and a[1]==170 and a[2]==4 and a[3]==128 and a[4]==2:
  p=1
  else:
  p=0
  if sum!=a[7] and y!=1 and p!=1:
   print("wrroy1")
   b=t.read(3)
   c=b[0]
   d=b[1]
   e=b[2]
   print(b)
   while c!=170 or d!=170 or e!=4:
   c=d
   d=e
   e=t.read()
   print("c:")
   print(c)
   print("d:")
   print(d)
   print("e:")
   print(e)
   if c==(b'\xaa'or 170) and d==(b'\xaa'or 170) and e==b'\x04':
    g=t.read(5)
    print(g)
    if c == b'\xaa' and d==b'\xaa' and e==b'\x04' and g[0]==128 and g[1]==2:
    a=t.read(8)
    print(a)
    break

#  if a[0]==a[1]==170 and a[2]==4:
  # print(type(a))

  if a[0] == 170 and a[1]==170 and a[2]==4 and a[3]==128 and a[4]==2:
  high=a[5]
  low=a[6]
#  print(a)
  rawdata=(high<<8)|low
  if rawdata>32768:
   rawdata=rawdata-65536
#  vaul.append(rawdata)
  sum=((0x80+0x02+high+low)^0xffffffff)&0xff
  if sum==a[7]:
   vaul.append(rawdata)
  if sum!=a[7]:
   print("wrroy2")
   b=t.read(3)
   c=b[0]
   d=b[1]
   e=b[2]
#   print(b)
   while c!=170 or d!=170 or e!=4:
   c=d
   d=e
   e=t.read()
   if c==b'\xaa' and d==b'\xaa' and e==b'\x04':
    g=t.read(5)
    print(g)
    if c == b'\xaa' and d==b'\xaa' and e==b'\x04' and g[0]==128 and g[1]==2:
    a=t.read(8)
    print(a)
    break
  if a[0]==a[1]==170 and a[2]==32:
  c=a+t.read(28)
  print(vaul)
  print(len(vaul))
  for v in vaul:
   w=0
   if v<=102:
   w+=v
   q=w/len(vaul)
   q=str(q)
   with open(filename,'a') as file_object:
    file_object.write(q)
    file_object.write("\n")
   if 102<v<=204:
   w+=v
   q=w/len(vaul)
   q=str(q)
   with open(filename,'a') as file_object:
    file_object.write(q)
    file_object.write("\n")
   if 204<v<=306:
   w+=v
   q=w/len(vaul)
   q=str(q)
   with open(filename,'a') as file_object:
    file_object.write(q)
    file_object.write("\n")
   if 306<v<=408:
   w+=v
   q=w/len(vaul)
   q=str(q)
   with open(filename,'a') as file_object:
    file_object.write(q)
    file_object.write("\n")
   if 408<v<=510:
   w+=v
   q=w/len(vaul)
   q=str(q)
   with open(filename,'a') as file_object:
    file_object.write(q)
    file_object.write("\n")
#  print(c)
  vaul=[]
#  if i==250:
#  break
#  with open(filename,'a') as file_object:
#   file_object.write(q)
#   file_object.write("\n")

补充知识：Python处理脑电数据：PCA数据降维

pca.py

#!-coding:UTF-8-
from numpy import *
import numpy as np

def loadDataSet(fileName, delim='\t'):
 fr = open(fileName)
 stringArr = [line.strip().split(delim) for line in fr.readlines()]
 datArr = [map(float,line) for line in stringArr]
 return mat(datArr)

def percentage2n(eigVals,percentage):
 sortArray=np.sort(eigVals) #升序
 sortArray=sortArray[-1::-1] #逆转，即降序
 arraySum=sum(sortArray)
 tmpSum=0
 num=0
 for i in sortArray:
 tmpSum+=i
 num+=1
 if tmpSum>=arraySum*percentage:
  return num

def pca(dataMat, topNfeat=9999999):
 meanVals = mean(dataMat, axis=0)
 meanRemoved = dataMat - meanVals #remove mean
 covMat = cov(meanRemoved, rowvar=0)
 eigVals,eigVects = linalg.eig(mat(covMat))
 eigValInd = argsort(eigVals)  #sort, sort goes smallest to largest
 eigValInd = eigValInd[:-(topNfeat+1):-1] #cut off unwanted dimensions
 redEigVects = eigVects[:,eigValInd] #reorganize eig vects largest to smallest
 lowData_N = meanRemoved * redEigVects#transform data into new dimensions
 reconMat_N = (lowData_N * redEigVects.T) + meanVals
 return lowData_N,reconMat_N

def pcaPerc(dataMat, percentage=1):
 meanVals = mean(dataMat, axis=0)
 meanRemoved = dataMat - meanVals #remove mean
 covMat = cov(meanRemoved, rowvar=0)
 eigVals,eigVects = linalg.eig(mat(covMat))
 eigValInd = argsort(eigVals)  #sort, sort goes smallest to largest
 n=percentage2n(eigVals,percentage)
 n_eigValIndice=eigValInd[-1:-(n+1):-1]
 n_eigVect=eigVects[:,n_eigValIndice]
 lowData_P=meanRemoved*n_eigVect
 reconMat_P = (lowData_P * n_eigVect.T) + meanVals
 return lowData_P,reconMat_P

readData.py

import matplotlib.pyplot as plt
from pylab import *
import numpy as np
import scipy.io as sio
def loadData(filename,mName):
 load_fn = filename
 load_data = sio.loadmat(load_fn)
 load_matrix = load_data[mName]
 #load_matrix_row = load_matrix[0]

 #figure(mName)
 #plot(load_matrix,'r-')
 #show()

 #print type(load_data)
 #print type(load_matrix)
 #print load_matrix_row
 return load_matrix

main.py

#!-coding:UTF-8
import matplotlib.pyplot as plt
from pylab import *
import numpy as np
import scipy.io as sio
import pca
from numpy import mat,matrix
import scipy as sp
import readData
import pca

if __name__ == '__main__':
 A1=readData.loadData('6electrodes.mat','A1')
 lowData_N, reconMat_N= pca.pca(A1,30)
 lowData_P, reconMat_P = pca.pcaPerc(A1,0.95)
 #print lowDMat
 #print reconMat
 print shape(lowData_N)
 print shape(reconMat_N)
 print shape(lowData_P)
 print shape(reconMat_P)

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