ERP CORE.¶
This example demonstrate how to process 5 participants from the ERP CORE dataset. It shows how to obtain 7 ERP components from a total of 6 experimental tasks:
- N170 (face perception)
- MMN (passive auditory oddball)
- N2pc (visual search)
- N400 (word pair judgment)
- P3b (active visual oddball)
- LRP and ERN (flankers task)
Dataset information¶
- Authors: Emily S. Kappenman, Jaclyn L. Farrens, Wendy Zhang, Andrew X. Stewart, and Steven J. Luck
- License: CC-BY-4.0
- URL: https://erpinfo.org/erp-core
- Citation: Kappenman, E., Farrens, J., Zhang, W., Stewart, A. X., & Luck, S. J. (2021). ERP CORE: An open resource for human event-related potential research. NeuroImage 225: 117465. https://doi.org/10.1016/j.neuroimage.2020.117465
Demonstrated features¶
| Feature | This example |
|---|---|
| MEG processing | ❌ |
| EEG processing | ✅ |
| Maxwell filter | ❌ |
| Frequency filter | ✅ |
| Artifact regression | ❌ |
| SSP | ❌ |
| ICA | ✅ |
| Evoked contrasts | ✅ |
| Time-by-time decoding | ✅ |
| Time-generalization decoding | ✅ |
| CSP decoding | ✅ |
| Time-frequency analysis | ❌ |
| BEM surface creation | ❌ |
| Template MRI | ❌ |
Dataset source¶
This dataset was acquired from https://osf.io/3zk6n/download?version=2
Configuration¶
Click to expand
import argparse
import sys
import mne
bids_root = "~/mne_data/ERP_CORE"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ERP_CORE"
# Find the --task option
args = [arg for arg in sys.argv if arg.startswith("--task") or not arg.startswith("-")]
parser = argparse.ArgumentParser()
parser.add_argument("ignored", nargs="*")
parser.add_argument(
"--task", choices=("N400", "ERN", "LRP", "MMN", "N2pc", "N170", "P3"), required=True
)
task = parser.parse_args(args).task
sessions = [task]
subjects = ["015", "016", "017", "018", "019"]
ch_types = ["eeg"]
interactive = False
raw_resample_sfreq = 128
# Suppress "Data file name in EEG.data (sub-019_task-ERN_eeg.fdt) is incorrect..."
read_raw_bids_verbose = "error"
eeg_template_montage = mne.channels.make_standard_montage("standard_1005")
eeg_bipolar_channels = {
"HEOG": ("HEOG_left", "HEOG_right"),
"VEOG": ("VEOG_lower", "FP2"),
}
drop_channels = ["HEOG_left", "HEOG_right", "VEOG_lower"]
eog_channels = ["HEOG", "VEOG"]
l_freq = 0.1
h_freq = None
notch_freq = 60
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 2
find_breaks = True
min_break_duration = 10
t_break_annot_start_after_previous_event = 3.0
t_break_annot_stop_before_next_event = 1.5
if task == "N400": # test autoreject local without ICA
spatial_filter = None
reject = "autoreject_local"
autoreject_n_interpolate = [2, 4]
elif task == "N170": # test autoreject local before ICA
spatial_filter = "ica"
ica_reject = "autoreject_local"
reject = "autoreject_global"
autoreject_n_interpolate = [2, 4]
else:
spatial_filter = "ica"
ica_reject = dict(eeg=350e-6, eog=500e-6)
reject = "autoreject_global"
# These settings are only used for the cases where spatial_filter="ica"
ica_max_iterations = 1000
ica_eog_threshold = 2
ica_decim = 2 # speed up ICA fitting
run_source_estimation = False
on_rename_missing_events = "ignore"
parallel_backend = "dask"
dask_worker_memory_limit = "2.5G"
n_jobs = 4
if task == "N400":
dask_open_dashboard = True
rename_events = {
"response/201": "response/correct",
"response/202": "response/error",
"stimulus/111": "stimulus/prime/related",
"stimulus/112": "stimulus/prime/related",
"stimulus/121": "stimulus/prime/unrelated",
"stimulus/122": "stimulus/prime/unrelated",
"stimulus/211": "stimulus/target/related",
"stimulus/212": "stimulus/target/related",
"stimulus/221": "stimulus/target/unrelated",
"stimulus/222": "stimulus/target/unrelated",
}
eeg_reference = ["P9", "P10"]
epochs_tmin = -0.2
epochs_tmax = 0.8
epochs_metadata_tmin = 0
epochs_metadata_tmax = 1.5
epochs_metadata_keep_first = ["stimulus/target", "response"]
baseline = (None, 0)
conditions = {
"related": '`first_stimulus/target` == "related" and '
'first_response == "correct"',
"unrelated": '`first_stimulus/target` == "unrelated" and '
'first_response == "correct"',
}
contrasts = [("unrelated", "related")]
cluster_forming_t_threshold = 1.5 # Only for testing!
cluster_permutation_p_threshold = 0.2 # Only for testing!
elif task == "ERN":
rename_events = {
"stimulus/11": "compatible/left",
"stimulus/12": "compatible/right",
"stimulus/21": "incompatible/left",
"stimulus/22": "incompatible/right",
"response/111": "response/correct",
"response/112": "response/incorrect",
"response/121": "response/correct",
"response/122": "response/incorrect",
"response/211": "response/incorrect",
"response/212": "response/correct",
"response/221": "response/incorrect",
"response/222": "response/correct",
}
eeg_reference = ["P9", "P10"]
epochs_tmin = -0.6
epochs_tmax = 0.4
baseline = (-0.4, -0.2)
conditions = ["response/correct", "response/incorrect"]
contrasts = [("response/incorrect", "response/correct")]
cluster_forming_t_threshold = 5 # Only for testing!
cluster_permutation_p_threshold = 0.2 # Only for testing!
decoding_csp = True
decoding_csp_freqs = {
"theta": [4, 7],
"alpha": [8, 12],
"beta": [13, 20, 30],
"gamma": [50, 63],
}
decoding_csp_times = [-0.2, 0.0, 0.2, 0.4]
elif task == "LRP":
rename_events = {
"stimulus/11": "compatible/left",
"stimulus/12": "compatible/right",
"stimulus/21": "incompatible/left",
"stimulus/22": "incompatible/right",
"response/111": "response/left/correct",
"response/112": "response/left/incorrect",
"response/121": "response/left/correct",
"response/122": "response/left/incorrect",
"response/211": "response/right/incorrect",
"response/212": "response/right/correct",
"response/221": "response/right/incorrect",
"response/222": "response/right/correct",
}
eeg_reference = ["P9", "P10"]
epochs_tmin = -0.8
epochs_tmax = 0.2
baseline = (None, -0.6)
conditions = ["response/left", "response/right"]
contrasts = [("response/right", "response/left")] # contralateral vs ipsi
elif task == "MMN":
rename_events = {
"stimulus/70": "stimulus/deviant",
"stimulus/80": "stimulus/standard",
}
eeg_reference = ["P9", "P10"]
epochs_tmin = -0.2
epochs_tmax = 0.8
baseline = (None, 0)
conditions = ["stimulus/standard", "stimulus/deviant"]
contrasts = [("stimulus/deviant", "stimulus/standard")]
elif task == "N2pc":
rename_events = {
"response/201": "response/correct",
"response/202": "response/error",
"stimulus/111": "stimulus/blue/left",
"stimulus/112": "stimulus/blue/left",
"stimulus/121": "stimulus/blue/right",
"stimulus/122": "stimulus/blue/right",
"stimulus/211": "stimulus/pink/left",
"stimulus/212": "stimulus/pink/left",
"stimulus/221": "stimulus/pink/right",
"stimulus/222": "stimulus/pink/right",
}
eeg_reference = ["P9", "P10"]
# Analyze all EEG channels -- we only specify the channels here for the purpose of
# demonstration
analyze_channels = [
"FP1",
"F3",
"F7",
"FC3",
"C3",
"C5",
"P3",
"P7",
"P9",
"PO7",
"PO3",
"O1",
"Oz",
"Pz",
"CPz",
"FP2",
"Fz",
"F4",
"F8",
"FC4",
"FCz",
"Cz",
"C4",
"C6",
"P4",
"P8",
"P10",
"PO8",
"PO4",
"O2",
]
epochs_tmin = -0.2
epochs_tmax = 0.8
baseline = (None, 0)
conditions = ["stimulus/right", "stimulus/left"]
contrasts = [("stimulus/right", "stimulus/left")] # Contralteral vs ipsi
elif task == "N170":
rename_events = {
"response/201": "response/correct",
"response/202": "response/error",
}
eeg_reference = "average"
# Analyze all EEG channels -- we only specify the channels here for the purpose of
# demonstration
analyze_channels = [
"FP1",
"F3",
"F7",
"FC3",
"C3",
"C5",
"P3",
"P7",
"P9",
"PO7",
"PO3",
"O1",
"Oz",
"Pz",
"CPz",
"FP2",
"Fz",
"F4",
"F8",
"FC4",
"FCz",
"Cz",
"C4",
"C6",
"P4",
"P8",
"P10",
"PO8",
"PO4",
"O2",
]
ica_n_components = 30 - 1
for i in range(1, 180 + 1):
orig_name = f"stimulus/{i}"
if 1 <= i <= 40:
new_name = "stimulus/face/normal"
elif 41 <= i <= 80:
new_name = "stimulus/car/normal"
elif 101 <= i <= 140:
new_name = "stimulus/face/scrambled"
elif 141 <= i <= 180:
new_name = "stimulus/car/scrambled"
else:
continue
rename_events[orig_name] = new_name
epochs_tmin = -0.2
epochs_tmax = 0.8
baseline = (None, 0)
conditions = ["stimulus/face/normal", "stimulus/car/normal"]
contrasts = [("stimulus/face/normal", "stimulus/car/normal")]
elif task == "P3":
rename_events = {
"response/201": "response/correct",
"response/202": "response/incorrect",
"stimulus/11": "stimulus/target/11",
"stimulus/22": "stimulus/target/22",
"stimulus/33": "stimulus/target/33",
"stimulus/44": "stimulus/target/44",
"stimulus/55": "stimulus/target/55",
"stimulus/21": "stimulus/non-target/21",
"stimulus/31": "stimulus/non-target/31",
"stimulus/41": "stimulus/non-target/41",
"stimulus/51": "stimulus/non-target/51",
"stimulus/12": "stimulus/non-target/12",
"stimulus/32": "stimulus/non-target/32",
"stimulus/42": "stimulus/non-target/42",
"stimulus/52": "stimulus/non-target/52",
"stimulus/13": "stimulus/non-target/13",
"stimulus/23": "stimulus/non-target/23",
"stimulus/43": "stimulus/non-target/43",
"stimulus/53": "stimulus/non-target/53",
"stimulus/14": "stimulus/non-target/14",
"stimulus/24": "stimulus/non-target/24",
"stimulus/34": "stimulus/non-target/34",
"stimulus/54": "stimulus/non-target/54",
"stimulus/15": "stimulus/non-target/15",
"stimulus/25": "stimulus/non-target/25",
"stimulus/35": "stimulus/non-target/35",
"stimulus/45": "stimulus/non-target/45",
}
eeg_reference = ["P9", "P10"]
epochs_tmin = -0.2
epochs_tmax = 0.8
baseline = (None, 0)
conditions = ["stimulus/target", "stimulus/non-target"]
contrasts = [("stimulus/target", "stimulus/non-target")]
cluster_forming_t_threshold = 0.8 # Only for testing!
cluster_permutation_p_threshold = 0.2 # Only for testing!
else:
raise RuntimeError(f"Task {task} not currently supported")
Generated output¶
Summary reports
sub-015_ses-ERN_task-ERN_report.html
sub-015_ses-LRP_task-LRP_report.html
sub-015_ses-MMN_task-MMN_report.html
sub-015_ses-N170_task-N170_report.html
sub-015_ses-N2pc_task-N2pc_report.html
sub-015_ses-N400_task-N400_report.html
sub-015_ses-P3_task-P3_report.html
sub-016_ses-ERN_task-ERN_report.html
sub-016_ses-LRP_task-LRP_report.html
sub-016_ses-MMN_task-MMN_report.html
sub-016_ses-N170_task-N170_report.html
sub-016_ses-N2pc_task-N2pc_report.html
sub-016_ses-N400_task-N400_report.html
sub-016_ses-P3_task-P3_report.html
sub-017_ses-ERN_task-ERN_report.html
sub-017_ses-LRP_task-LRP_report.html
sub-017_ses-MMN_task-MMN_report.html
sub-017_ses-N170_task-N170_report.html
sub-017_ses-N2pc_task-N2pc_report.html
sub-017_ses-N400_task-N400_report.html
sub-017_ses-P3_task-P3_report.html
sub-018_ses-ERN_task-ERN_report.html
sub-018_ses-LRP_task-LRP_report.html
sub-018_ses-MMN_task-MMN_report.html
sub-018_ses-N170_task-N170_report.html
sub-018_ses-N2pc_task-N2pc_report.html
sub-018_ses-N400_task-N400_report.html
sub-018_ses-P3_task-P3_report.html
sub-019_ses-ERN_task-ERN_report.html
sub-019_ses-LRP_task-LRP_report.html
sub-019_ses-MMN_task-MMN_report.html
sub-019_ses-N170_task-N170_report.html
sub-019_ses-N2pc_task-N2pc_report.html
sub-019_ses-N400_task-N400_report.html
sub-019_ses-P3_task-P3_report.html
sub-average_ses-ERN_task-ERN_report.html
sub-average_ses-LRP_task-LRP_report.html
sub-average_ses-MMN_task-MMN_report.html
sub-average_ses-N170_task-N170_report.html
sub-average_ses-N2pc_task-N2pc_report.html
Data cleaning
sub-015_ses-ERN_task-ERN_proc-ica+components_report.html
sub-015_ses-ERN_task-ERN_proc-ica_report.html
sub-015_ses-ERN_task-ERN_proc-icafit_report.html
sub-015_ses-LRP_task-LRP_proc-ica+components_report.html
sub-015_ses-LRP_task-LRP_proc-ica_report.html
sub-015_ses-LRP_task-LRP_proc-icafit_report.html
sub-015_ses-MMN_task-MMN_proc-ica+components_report.html
sub-015_ses-MMN_task-MMN_proc-ica_report.html
sub-015_ses-MMN_task-MMN_proc-icafit_report.html
sub-015_ses-N170_task-N170_proc-ica+components_report.html
sub-015_ses-N170_task-N170_proc-ica_report.html
sub-015_ses-N170_task-N170_proc-icafit_report.html
sub-015_ses-N2pc_task-N2pc_proc-ica+components_report.html
sub-015_ses-N2pc_task-N2pc_proc-ica_report.html
sub-015_ses-N2pc_task-N2pc_proc-icafit_report.html
sub-015_ses-P3_task-P3_proc-ica+components_report.html
sub-015_ses-P3_task-P3_proc-ica_report.html
sub-015_ses-P3_task-P3_proc-icafit_report.html
sub-016_ses-ERN_task-ERN_proc-ica+components_report.html
sub-016_ses-ERN_task-ERN_proc-ica_report.html
sub-016_ses-ERN_task-ERN_proc-icafit_report.html
sub-016_ses-LRP_task-LRP_proc-ica+components_report.html
sub-016_ses-LRP_task-LRP_proc-ica_report.html
sub-016_ses-LRP_task-LRP_proc-icafit_report.html
sub-016_ses-MMN_task-MMN_proc-ica+components_report.html
sub-016_ses-MMN_task-MMN_proc-ica_report.html
sub-016_ses-MMN_task-MMN_proc-icafit_report.html
sub-016_ses-N170_task-N170_proc-ica+components_report.html
sub-016_ses-N170_task-N170_proc-ica_report.html
sub-016_ses-N170_task-N170_proc-icafit_report.html
sub-016_ses-N2pc_task-N2pc_proc-ica+components_report.html
sub-016_ses-N2pc_task-N2pc_proc-ica_report.html
sub-016_ses-N2pc_task-N2pc_proc-icafit_report.html
sub-016_ses-P3_task-P3_proc-ica+components_report.html
sub-016_ses-P3_task-P3_proc-ica_report.html
sub-016_ses-P3_task-P3_proc-icafit_report.html
sub-017_ses-ERN_task-ERN_proc-ica+components_report.html
sub-017_ses-ERN_task-ERN_proc-ica_report.html
sub-017_ses-ERN_task-ERN_proc-icafit_report.html
sub-017_ses-LRP_task-LRP_proc-ica+components_report.html
sub-017_ses-LRP_task-LRP_proc-ica_report.html
sub-017_ses-LRP_task-LRP_proc-icafit_report.html
sub-017_ses-MMN_task-MMN_proc-ica+components_report.html
sub-017_ses-MMN_task-MMN_proc-ica_report.html
sub-017_ses-MMN_task-MMN_proc-icafit_report.html
sub-017_ses-N170_task-N170_proc-ica+components_report.html
sub-017_ses-N170_task-N170_proc-ica_report.html
sub-017_ses-N170_task-N170_proc-icafit_report.html
sub-017_ses-N2pc_task-N2pc_proc-ica+components_report.html
sub-017_ses-N2pc_task-N2pc_proc-ica_report.html
sub-017_ses-N2pc_task-N2pc_proc-icafit_report.html
sub-017_ses-P3_task-P3_proc-ica+components_report.html
sub-017_ses-P3_task-P3_proc-ica_report.html
sub-017_ses-P3_task-P3_proc-icafit_report.html
sub-018_ses-ERN_task-ERN_proc-ica+components_report.html
sub-018_ses-ERN_task-ERN_proc-ica_report.html
sub-018_ses-ERN_task-ERN_proc-icafit_report.html
sub-018_ses-LRP_task-LRP_proc-ica+components_report.html
sub-018_ses-LRP_task-LRP_proc-ica_report.html
sub-018_ses-LRP_task-LRP_proc-icafit_report.html
sub-018_ses-MMN_task-MMN_proc-ica+components_report.html
sub-018_ses-MMN_task-MMN_proc-ica_report.html
sub-018_ses-MMN_task-MMN_proc-icafit_report.html
sub-018_ses-N170_task-N170_proc-ica+components_report.html
sub-018_ses-N170_task-N170_proc-ica_report.html
sub-018_ses-N170_task-N170_proc-icafit_report.html
sub-018_ses-N2pc_task-N2pc_proc-ica+components_report.html
sub-018_ses-N2pc_task-N2pc_proc-ica_report.html
sub-018_ses-N2pc_task-N2pc_proc-icafit_report.html
sub-018_ses-P3_task-P3_proc-ica+components_report.html
sub-018_ses-P3_task-P3_proc-ica_report.html
sub-018_ses-P3_task-P3_proc-icafit_report.html
sub-019_ses-ERN_task-ERN_proc-ica+components_report.html
sub-019_ses-ERN_task-ERN_proc-ica_report.html
sub-019_ses-ERN_task-ERN_proc-icafit_report.html
sub-019_ses-LRP_task-LRP_proc-ica+components_report.html
sub-019_ses-LRP_task-LRP_proc-ica_report.html
sub-019_ses-LRP_task-LRP_proc-icafit_report.html
sub-019_ses-MMN_task-MMN_proc-ica+components_report.html
sub-019_ses-MMN_task-MMN_proc-ica_report.html
sub-019_ses-MMN_task-MMN_proc-icafit_report.html
sub-019_ses-N170_task-N170_proc-ica+components_report.html
sub-019_ses-N170_task-N170_proc-ica_report.html
sub-019_ses-N170_task-N170_proc-icafit_report.html
sub-019_ses-N2pc_task-N2pc_proc-ica+components_report.html
sub-019_ses-N2pc_task-N2pc_proc-ica_report.html
sub-019_ses-N2pc_task-N2pc_proc-icafit_report.html
sub-019_ses-P3_task-P3_proc-ica+components_report.html