在EEG数据python中查找峰值振幅和潜伏期，最好使用MNE

import mne
import numpy as np

# 假设你已经加载了EEG数据和事件信息
raw = mne.io.read_raw_eeglab('your_eeg_file.set', preload=True)
events = mne.read_events('your_events_file.eve')

# 创建Epochs对象
event_id = {'stimulus/left': 1, 'stimulus/right': 2}
epochs = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5, baseline=(None, 0))

# 查找峰值振幅和潜伏期
for event_label in event_id:
    evoked = epochs[event_label].average()
    peaks = mne.preprocessing.find_peaks(evoked.data, height=None, distance=100)
    
    for peak in peaks:
        latency = peak[1] * epochs.info['sfreq']  # 潜伏期（秒）
        amplitude = evoked.data[peak[0], peak[1]]  # 峰值振幅（微伏）
        print(f"Event: {event_label}, Peak Amplitude: {amplitude:.2f} uV, Latency: {latency:.2f} ms")

# 可视化结果
evoked.plot()