def get_data():
datax = []
datay = []
for i in tqdm(range(1, 60)):
try:
data_score = get_score(i)[0]
data_state = get_state(i, 5)
data_eeg = get_epoch_eeg(i).drop(["condition"], axis=1)
if not (len(data_score) == len(data_eeg["epoch"].value_counts())
and len(data_score) == len(
data_state[data_state["markerText"] == "ShotOps"])):
continue
print("yes")
for shoot in range(len(data_score)):
datax.append(
get_aeraeeg_psd(data_eeg[data_eeg["epoch"] == i][-1000:]))
datay.extend(data_score)
"""
data=get_raw_eeg(i)
events,event_id=mne.events_from_annotations(data)
epochs=mne.Epochs(data,events,event_id,tmin=-5,tmax=0,event_repeated='drop',preload=True)
first,second=get_score(i+1)
rest_eeg=epochs["s1001"][0]
total_shot_num=len(first)+len(second)
first_shoot_eeg=epochs["s1002"][-total_shot_num:-len(second)]
first_shoot_eeg["s1002"][0].plot_psd(fmin=2., fmax=40., average=True, spatial_colors=False,picks=["Fz"])
plt.show()
"""
except Exception as e:
traceback.print_exc()
pass
return datax, datay
def get_data():
datax = []
datay = []
high, low = 0, 0
for i in tqdm(range(1, 60)):
if i == 32: continue
try:
data_score = get_score(i)[0]
data_state = get_state(i, 3)
data_eeg = get_epoch_eeg(i).drop(["condition"], axis=1)
data_aimtrack = get_aimtrack(i)
data_aimtrack.drop(
data_aimtrack[data_aimtrack["exp_num"] == 2].index,
inplace=True)
data_aimtrack.drop(["exp_num"], axis=1, inplace=True)
"""
extract aimtrack data
"""
if not (len(data_score) == len(data_eeg["epoch"].value_counts())
and len(data_score) == len(
data_state[data_state["markerText"] == "ShotOps"])
and len(data_score) == len(
data_aimtrack["shoot_num"].value_counts())):
continue
print("yes")
for j in range(len(data_score)):
if data_score[j] > 9.5:
curaimtrackx = data_aimtrack[data_aimtrack["shoot_num"] ==
j + 1]["x"][-40:].tolist()
curaimtracky = data_aimtrack[data_aimtrack["shoot_num"] ==
j + 1]["y"][-40:].tolist()
if len(curaimtrackx) != 40 or len(curaimtracky) != 40:
continue
datax.append([curaimtrackx, curaimtracky])
datay.append(0)
print(data_score[j])
high += 1
if data_score[j] < 7.5:
curaimtrackx = data_aimtrack[data_aimtrack["shoot_num"] ==
j + 1]["x"][-40:].tolist()
curaimtracky = data_aimtrack[data_aimtrack["shoot_num"] ==
j + 1]["y"][-40:].tolist()
if len(curaimtrackx) != 40 or len(curaimtracky) != 40:
continue
datax.append([curaimtrackx, curaimtracky])
datay.append(1)
low += 1
print(len(datay), len(datax))
#if len(datay)!=len(datax):
# raise Exception("length not matched")
except Exception as e:
traceback.print_exc()
pass
print(low, high)
return datax, datay
def get_data():
datax = []
datay = []
high, low = 0, 0
for i in tqdm(range(1, 60)):
if i == 32: continue
try:
data_score = get_score(i)[0]
data_state = get_state(i, 3)
data_eeg = get_epoch_eeg(i).drop(["condition"], axis=1)
if not (len(data_score) == len(data_eeg["epoch"].value_counts())
and len(data_score) == len(
data_state[data_state["markerText"] == "ShotOps"])):
continue
print("yes")
for j in range(len(data_score)):
if data_score[j] > 9.5:
datay.append(0)
c6 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-3000:-2500])
c5 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-2500:-2000])
c4 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-2000:-1500])
c3 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-1500:-1000])
c2 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-1000:-500])
c1 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-500:])
datax.append([c6, c5, c4, c3, c2, c1])
high += 1
if data_score[j] < 7.5:
datay.append(1)
c6 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-3000:-2500])
c5 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-2500:-2000])
c4 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-2000:-1500])
c3 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-1500:-1000])
c2 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-1000:-500])
c1 = get_aeraeeg_corr(
data_eeg[data_eeg["epoch"] == j][-500:])
datax.append([c6, c5, c4, c3, c2, c1])
low += 1
print(len(datay), len(datax))
#if len(datay)!=len(datax):
# raise Exception("length not matched")
except Exception as e:
traceback.print_exc()
pass
print(low, high)
return datax, datay
def get_data():
datax=[]
datay=[]
high,low=0,0
for i in tqdm(range(1,60)):
if i==32: continue
try:
data_score=get_score(i)[0]
data_state=get_state(i,3)
data_eeg=get_epoch_eeg(i).drop(["condition"],axis=1)
if not (len(data_score)==len(data_eeg["epoch"].value_counts()) and len(data_score)==len(data_state[data_state["markerText"]=="ShotOps"])): continue
print("yes")
for j in range(len(data_score)):
if data_score[j]>9.5:
datay.append(0)
high+=1
d6=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-3000:-2500])
d5=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-2500:-2000])
d4=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-2000:-1500])
d3=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-1500:-1000])
d2=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-1000:-500])
d1=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-500:])
datax.append([d6,d5,d4,d3,d2,d1])
if data_score[j]<7.5:
datay.append(1)
low+=1
d6=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-3000:-2500])
d5=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-2500:-2000])
d4=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-2000:-1500])
d3=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-1500:-1000])
d2=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-1000:-500])
d1=get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==j][-500:])
datax.append([d6,d5,d4,d3,d2,d1])
print(len(datay),len(datax))
#if len(datay)!=len(datax):
# raise Exception("length not matched")
"""
data=get_raw_eeg(i)
events,event_id=mne.events_from_annotations(data)
epochs=mne.Epochs(data,events,event_id,tmin=-5,tmax=0,event_repeated='drop',preload=True)
first,second=get_score(i+1)
rest_eeg=epochs["s1001"][0]
total_shot_num=len(first)+len(second)
first_shoot_eeg=epochs["s1002"][-total_shot_num:-len(second)]
first_shoot_eeg["s1002"][0].plot_psd(fmin=2., fmax=40., average=True, spatial_colors=False,picks=["Fz"])
plt.show()
"""
except Exception as e:
traceback.print_exc()
pass
print(low,high)
return datax,datay
def discover_channel_psd_difference():
highscore_epoch, lowscore_epoch = None, None
highscorenum, lowscorenum = 0, 0
for i in range(1, 60):
try:
data_score = get_score(i)[0]
data_state = get_state(i, 3)
data_eeg = get_epoch_eeg(i).drop(["condition"], axis=1)
data_raw_eeg = get_raw_eeg(i)
if not (len(data_score) == len(data_eeg["epoch"].value_counts())
and len(data_score) == len(
data_state[data_state["markerText"] == "ShotOps"])):
continue
print("yes")
events, event_id = mne.events_from_annotations(data_raw_eeg)
epochs = mne.Epochs(data_raw_eeg,
events,
event_id,
tmin=-3,
tmax=0,
event_repeated='drop',
preload=True)
first, second = get_score(i + 1)
rest_eeg = epochs["s1001"][0]
total_shot_num = len(first) + len(second)
first_shoot_eeg = epochs["s1002"][-total_shot_num:-len(second)]
for j in range(len(data_score)):
if data_score[j] > 9:
if highscore_epoch == None:
highscore_epoch = first_shoot_eeg["s1002"][j]
else:
highscore_epoch = mne.concatenate_epochs(
[highscore_epoch, first_shoot_eeg["s1002"][j]])
highscorenum += 1
if data_score[j] < 8:
if lowscore_epoch == None:
lowscore_epoch = first_shoot_eeg["s1002"][j]
else:
lowscore_epoch = mne.concatenate_epochs(
[lowscore_epoch, first_shoot_eeg["s1002"][j]])
lowscorenum += 1
print(highscorenum)
print(lowscorenum)
except Exception as e:
traceback.print_exc()
channelnames = ["Fz", "F3", "F4", "P3", "Pz", "P4", "O1", "O2", "POz"]
print("test")
for channelname in channelnames:
plot_channel_psd(highscore_epoch, channelname)
for channelname in channelnames:
plot_channel_psd(lowscore_epoch, channelname)
def discover_event_difference():
highscore_epoch, lowscore_epoch = None, None
highscorenum, lowscorenum = 0, 0
for i in range(1, 60):
try:
data_score = get_score(i)[0]
data_state = get_state(i, 3)
data_eeg = get_epoch_eeg(i).drop(["condition"], axis=1)
data_raw_eeg = get_raw_eeg(i)
if not (len(data_score) == len(data_eeg["epoch"].value_counts())
and len(data_score) == len(
data_state[data_state["markerText"] == "ShotOps"])):
continue
print("yes")
events, event_id = mne.events_from_annotations(data_raw_eeg)
#print(events)
epochs = mne.Epochs(data_raw_eeg,
events,
event_id,
tmin=-3,
tmax=0,
event_repeated='drop',
preload=True)
first, second = get_score(i + 1)
rest_eeg = epochs["s1001"][0]
total_shot_num = len(first) + len(second)
first_shoot_eeg = epochs["s1002"][-total_shot_num:-len(second)]
for j in range(len(data_score)):
if data_score[j] > 9:
if highscore_epoch == None:
highscore_epoch = first_shoot_eeg["s1002"][j]
else:
highscore_epoch = mne.concatenate_epochs(
[highscore_epoch, first_shoot_eeg["s1002"][j]])
highscorenum += 1
if data_score[j] < 8:
if lowscore_epoch == None:
lowscore_epoch = first_shoot_eeg["s1002"][j]
else:
lowscore_epoch = mne.concatenate_epochs(
[lowscore_epoch, first_shoot_eeg["s1002"][j]])
lowscorenum += 1
print(highscorenum)
print(lowscorenum)
#fig1=highscore_epoch.plot_psd_topomap(show=False,normalize=True)
#fig2=lowscore_epoch.plot_psd_topomap(show=False,normalize=True)
#fig1.savefig(r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\high_score_3s_test\high_score{}.jpg".format(i))
#fig2.savefig(r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\low_score_3s_test\low_score{}.jpg".format(i))
except Exception as e:
traceback.print_exc()
freq_low = [0, 4, 8, 12, 30]
freq_high = [4, 8, 12, 30, 45]
for j in range(len(freq_low)):
highscore_epoch.load_data().filter(l_freq=freq_low[j],
h_freq=freq_high[j])
highscoredata = highscore_epoch.get_data()
corr_matrix = mne.connectivity.envelope_correlation(highscoredata,
combine="mean")
plt.imshow(corr_matrix)
plt.colorbar()
plt.title("highscore_({}Hz-{}Hz)".format(freq_low[j], freq_high[j]))
plt.savefig(
r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\diff\corr\high_score\high_score({}Hz-{}Hz).jpg"
.format(freq_low[j], freq_high[j]))
plt.close()
lowscore_epoch.load_data().filter(l_freq=freq_low[j],
h_freq=freq_high[j])
lowscoredata = lowscore_epoch.get_data()
corr_matrix = mne.connectivity.envelope_correlation(lowscoredata,
combine="mean")
plt.imshow(corr_matrix)
plt.colorbar()
plt.title("lowscore_({}Hz-{}Hz)".format(freq_low[j], freq_high[j]))
plt.savefig(
r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\diff\corr\low_score\low_score({}Hz-{}Hz).jpg"
.format(freq_low[j], freq_high[j]))
plt.close()
#fig1.savefig(r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\diff\event\high_score.jpg")
#fig2.savefig(r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\diff\event\low_score.jpg")
print(highscorenum)
print(lowscorenum)
def discover_psd_difference():
highscore_epoch, lowscore_epoch = None, None
highscorenum, lowscorenum = 0, 0
for i in range(1, 60):
try:
data_score = get_score(i)[0]
data_state = get_state(i, 3)
data_eeg = get_epoch_eeg(i).drop(["condition"], axis=1)
data_raw_eeg = get_raw_eeg(i)
if not (len(data_score) == len(data_eeg["epoch"].value_counts())
and len(data_score) == len(
data_state[data_state["markerText"] == "ShotOps"])):
continue
print("yes")
events, event_id = mne.events_from_annotations(data_raw_eeg)
#print(events)
epochs = mne.Epochs(data_raw_eeg,
events,
event_id,
tmin=-3,
tmax=0,
event_repeated='drop',
preload=True)
first, second = get_score(i + 1)
rest_eeg = epochs["s1001"][0]
total_shot_num = len(first) + len(second)
first_shoot_eeg = epochs["s1002"][-total_shot_num:-len(second)]
for j in range(len(data_score)):
if data_score[j] > 9:
if highscore_epoch == None:
highscore_epoch = first_shoot_eeg["s1002"][j]
else:
highscore_epoch = mne.concatenate_epochs(
[highscore_epoch, first_shoot_eeg["s1002"][j]])
highscorenum += 1
if data_score[j] < 8:
if lowscore_epoch == None:
lowscore_epoch = first_shoot_eeg["s1002"][j]
else:
lowscore_epoch = mne.concatenate_epochs(
[lowscore_epoch, first_shoot_eeg["s1002"][j]])
lowscorenum += 1
print(highscorenum)
print(lowscorenum)
fig1 = highscore_epoch.plot_psd_topomap(show=False,
normalize=False,
cmap="RdBu_r")
fig2 = lowscore_epoch.plot_psd_topomap(show=False,
normalize=False,
cmap="RdBu_r")
fig1.savefig(
r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\high_low1\high_score{}.jpg"
.format(i))
fig2.savefig(
r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\high_low1\low_score{}.jpg"
.format(i))
break
except Exception as e:
traceback.print_exc()
break
#fig1=highscore_epoch.plot_psd_topomap(show=False,cmap="RdBu_r",normalize=True)
#fig2=lowscore_epoch.plot_psd_topomap(show=False,cmap="RdBu_r",normalize=True)
#fig1.savefig(r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\high_low\high_score.jpg")
#fig2.savefig(r"C:\Users\zhou\Desktop\毕业设计\mechanical\最终论文\pic\high_low\low_score.jpg")
print(highscorenum)
print(lowscorenum)
def draw_pic():
data_eeg=get_epoch_eeg(1).drop(["condition"],axis=1)
get_aeraeeg_psd(data_eeg[data_eeg["epoch"]==0][-2000:])