如果使用data_path = mne.datasets.ssvep.data_path()这个数据集来进行以上处理呢，请生成代码

以下是使用MNE库加载SSVEP数据集并进行预处理的示例代码：

import mne

# Load SSVEP dataset
data_path = mne.datasets.ssvep.data_path()
raw = mne.io.read_raw_edf(data_path + '/sub-01/ses-test/eeg/sub-01_ses-test_task-ssvep_eeg.edf', preload=True)

# Set channel types and montage
raw.set_channel_types({'HEOG': 'eog', 'VEOG': 'eog'})
montage = mne.channels.make_standard_montage('standard_1005')
raw.set_montage(montage)

# Apply bandpass filter
raw.filter(1, 40)

# Apply notch filter to remove line noise at 50 Hz
raw.notch_filter(50)

# Apply ICA to remove eye blinks and other artifacts
ica = mne.preprocessing.ICA(n_components=20, random_state=0)
ica.fit(raw)
raw = ica.apply(raw)

# Epoch data and apply baseline correction
events, event_id = mne.events_from_annotations(raw)
epochs = mne.Epochs(raw, events, event_id, tmin=0, tmax=4, baseline=(None, 0), preload=True)

# Average across trials
evoked = epochs.average()

# Plot evoked response
evoked.plot()