非接触式，心率检测，情感识别
自定义协议，非商业可用
Big-data节点
秦玉婷维护
数据类型：开放获取
数据来源：S. Koelstra et al.
创建时间：2011-06-09
数据集编号：No.12132
热度：
详情
DEAP为情感计算领域的开源数据集，可用于非接触式心率检测。数据集包括问卷评分、中枢和外周生理信号、面部表情等多源数据。采集过程中安排32位受试者在线观看并评价40个音乐视频，同步记录受试者生理信号和部面部表情视频（720×576@50fps），观看结束后受试者在唤醒度、效价、支配度、喜欢度和熟悉度五个维度上进行打分标注。生理信信号包括ECG信号、PPG信号、呼吸信号、皮肤电GSR、皮肤温度、肌电图、眼电图。
访问地址
配置方式
1.PPG标签处理
datapath = r'/home/som/lab/seed-yzj/DEAP/data/deap_ppg.csv'
savepath = r'/home/som/lab/seed-yzj/DEAP/data/new_deap_ppg.csv'
write = open(savepath, 'a', newline='', encoding='gb18030')
writer = csv.writer(write)
with open(datapath, 'r', encoding="utf-8") as csvfile:
reader = csv.reader(csvfile)
for row in reader:
if len(row[0].split('_')[-1])==2:
newnum=row[0].split('_')[0]+'_trial'+row[0].split('_')[-1]
else:
newnum = row[0].split('_')[0] + '_trial0' + row[0].split('_')[-1]
ppg=[float(i) for i in row[sr*5+1:sr*25+1]]
detrend_signals = detrend(ppg, 100)
detrend_signals = detrend_signals.flatten()
normalized_signals = normalize(detrend_signals)
resample_signal=signal.resample(normalized_signals,50*20)
recording =[]
recording.append(newnum)
for j in range(len(resample_signal)):
recording.append(resample_signal[j])
writer.writerow(recording)
def detrend(signals,param_lambda):
# https://blog.csdn.net/piaoxuezhong/article/details/79211586
signal_len = len(signals)
I = np.identity(signal_len)
B = np.array([1, -2, 1])
ones = np.ones((signal_len - 2, 1))
multi = B * ones
D2 = np.zeros((signal_len - 2, signal_len))
for i in range(D2.shape[0]):
D2[i, i:i + 3] = multi[i]
tr_D2 = np.transpose(D2)
multi_D2 = np.dot(tr_D2, D2)
inverse = I - (np.linalg.inv(I + (multi_D2 * pow(param_lambda, 2))))
detrend_signals = np.dot(inverse, signals)
return detrend_signals
def normalize(signals):
normalized_signals = (signals - np.mean(signals)) / np.std(signals, ddof=1)
return normalized_signals