ICA – sub-018, ses-P3, task-P3

Epochs used for ICA fitting epochs

Info epochs

Number of events	199
Events	stimulus/non-target/12: 7 stimulus/non-target/13: 5 stimulus/non-target/14: 9 stimulus/non-target/15: 11 stimulus/non-target/21: 7 stimulus/non-target/23: 5 stimulus/non-target/24: 11 stimulus/non-target/25: 9 stimulus/non-target/31: 6 stimulus/non-target/32: 10 stimulus/non-target/34: 6 stimulus/non-target/35: 10 stimulus/non-target/41: 7 stimulus/non-target/42: 3 stimulus/non-target/43: 14 stimulus/non-target/45: 8 stimulus/non-target/51: 10 stimulus/non-target/52: 5 stimulus/non-target/53: 6 stimulus/non-target/54: 10 stimulus/target/11: 8 stimulus/target/22: 8 stimulus/target/33: 8 stimulus/target/44: 8 stimulus/target/55: 8
Time range	-0.203 – 0.797 s
Baseline	off

Metadata epochs

	Epoch #	event_name	response/correct	response/incorrect	stimulus/non-target/12	stimulus/non-target/13	stimulus/non-target/14	stimulus/non-target/15	stimulus/non-target/21	stimulus/non-target/23	stimulus/non-target/24	stimulus/non-target/25	stimulus/non-target/31	stimulus/non-target/32	stimulus/non-target/34	stimulus/non-target/35	stimulus/non-target/41	stimulus/non-target/42	stimulus/non-target/43	stimulus/non-target/45	stimulus/non-target/51	stimulus/non-target/52	stimulus/non-target/53	stimulus/non-target/54	stimulus/target/11	stimulus/target/22	stimulus/target/33	stimulus/target/44	stimulus/target/55

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Epoch #	event_name	response/correct	response/incorrect	stimulus/non-target/12	stimulus/non-target/13	stimulus/non-target/14	stimulus/non-target/15	stimulus/non-target/21	stimulus/non-target/23	stimulus/non-target/24	stimulus/non-target/25	stimulus/non-target/31	stimulus/non-target/32	stimulus/non-target/34	stimulus/non-target/35	stimulus/non-target/41	stimulus/non-target/42	stimulus/non-target/43	stimulus/non-target/45	stimulus/non-target/51	stimulus/non-target/52	stimulus/non-target/53	stimulus/non-target/54	stimulus/target/11	stimulus/target/22	stimulus/target/33	stimulus/target/44	stimulus/target/55
0	stimulus/non-target/31	0.398										0.000
1	stimulus/non-target/35	0.555													0.000
2	stimulus/non-target/31	0.453										0.000
3	stimulus/non-target/35	0.367													0.000
4	stimulus/target/33	0.328																								0.000
5	stimulus/non-target/35	0.406													0.000
6	stimulus/non-target/35	0.406													0.000
7	stimulus/non-target/35	0.344													0.000
8	stimulus/target/33	0.336																								0.000
9	stimulus/non-target/32	0.438											0.000
10	stimulus/non-target/32	0.320											0.000
11	stimulus/target/33	0.422																								0.000
12	stimulus/non-target/32	0.406											0.000
13	stimulus/target/33	0.367																								0.000
14	stimulus/non-target/34	0.438												0.000
15	stimulus/non-target/35	0.570													0.000
16	stimulus/target/33	0.336																								0.000
17	stimulus/non-target/34	0.414												0.000
18	stimulus/non-target/35	0.445													0.000
19	stimulus/non-target/32	0.539											0.000
20	stimulus/non-target/35	0.406													0.000
21	stimulus/non-target/31	0.438										0.000
22	stimulus/non-target/32		0.391										0.000
23	stimulus/non-target/32	0.492											0.000
24	stimulus/non-target/32	0.461											0.000
25	stimulus/non-target/34	0.523												0.000
26	stimulus/non-target/34	0.453												0.000
27	stimulus/non-target/31	0.438										0.000
28	stimulus/non-target/32	0.406											0.000
29	stimulus/non-target/32	0.320											0.000
30	stimulus/non-target/35	0.336													0.000
31	stimulus/target/33	0.359																								0.000
32	stimulus/target/33	0.367																								0.000
33	stimulus/non-target/34	0.336												0.000
34	stimulus/non-target/35	0.352													0.000
35	stimulus/non-target/32	0.383											0.000
36	stimulus/target/33	0.445																								0.000
37	stimulus/non-target/34	0.438												0.000
38	stimulus/non-target/31	0.359										0.000
39	stimulus/non-target/31	0.367										0.000
40	stimulus/non-target/12	0.508		0.000
41	stimulus/non-target/15	0.398					0.000
42	stimulus/non-target/13	0.344			0.000
43	stimulus/target/11	0.391																						0.000
44	stimulus/non-target/12	0.336		0.000
45	stimulus/non-target/14	0.562				0.000
46	stimulus/non-target/15	0.516					0.000
47	stimulus/non-target/14	0.336				0.000
48	stimulus/non-target/13	0.352			0.000
49	stimulus/non-target/15	0.656					0.000
50	stimulus/non-target/15	0.359					0.000
51	stimulus/non-target/12	0.383		0.000
52	stimulus/target/11	0.484																						0.000
53	stimulus/target/11	0.367																						0.000
54	stimulus/non-target/14	0.312				0.000
55	stimulus/non-target/14	0.344				0.000
56	stimulus/non-target/12	0.328		0.000
57	stimulus/target/11		0.367																					0.000
58	stimulus/non-target/14	0.344				0.000
59	stimulus/non-target/15	0.320					0.000
60	stimulus/non-target/15	0.312					0.000
61	stimulus/non-target/13	0.336			0.000
62	stimulus/non-target/15	0.367					0.000
63	stimulus/non-target/14	0.305				0.000
64	stimulus/target/11	0.344																						0.000
65	stimulus/non-target/12	0.406		0.000
66	stimulus/non-target/15	0.352					0.000
67	stimulus/target/11		0.375																					0.000
68	stimulus/non-target/14	0.344				0.000
69	stimulus/target/11	0.453																						0.000
70	stimulus/non-target/15	0.344					0.000
71	stimulus/non-target/15	0.305					0.000
72	stimulus/non-target/13	0.328			0.000
73	stimulus/target/11	0.312																						0.000
74	stimulus/non-target/13	0.336			0.000
75	stimulus/non-target/14	0.398				0.000
76	stimulus/non-target/12	0.359		0.000
77	stimulus/non-target/14	0.375				0.000
78	stimulus/non-target/12	0.414		0.000
79	stimulus/non-target/15	0.398					0.000
80	stimulus/non-target/23	0.383							0.000
81	stimulus/target/22	0.422																							0.000
82	stimulus/target/22	0.461																							0.000
83	stimulus/non-target/23	0.414							0.000
84	stimulus/non-target/25	0.461									0.000
85	stimulus/non-target/21	0.352						0.000
86	stimulus/target/22	0.398																							0.000
87	stimulus/non-target/24	0.391								0.000
88	stimulus/non-target/25	0.344									0.000
89	stimulus/non-target/21	0.375						0.000
90	stimulus/non-target/23	0.375							0.000
91	stimulus/non-target/25	0.328									0.000
92	stimulus/non-target/25	0.422									0.000
93	stimulus/non-target/24	0.562								0.000
94	stimulus/non-target/25	0.438									0.000
95	stimulus/non-target/24	0.406								0.000
96	stimulus/target/22	0.406																							0.000
97	stimulus/non-target/24	0.367								0.000
98	stimulus/non-target/21	0.398						0.000
99	stimulus/non-target/23	0.492							0.000
100	stimulus/target/22	0.461																							0.000
101	stimulus/non-target/24	0.344								0.000
102	stimulus/non-target/24	0.328								0.000
103	stimulus/non-target/25	0.375									0.000
104	stimulus/non-target/23	0.438							0.000
105	stimulus/non-target/25	0.383									0.000
106	stimulus/non-target/24	0.375								0.000
107	stimulus/non-target/21	0.336						0.000
108	stimulus/non-target/24	0.320								0.000
109	stimulus/non-target/21	0.289						0.000
110	stimulus/non-target/24	0.414								0.000
111	stimulus/non-target/25	0.352									0.000
112	stimulus/non-target/25	0.289									0.000
113	stimulus/target/22		0.266																						0.000
114	stimulus/target/22	0.406																							0.000
115	stimulus/non-target/24	0.344								0.000
116	stimulus/non-target/24	0.312								0.000
117	stimulus/non-target/21	0.352						0.000
118	stimulus/target/22		0.391																						0.000
119	stimulus/non-target/21	0.328						0.000
120	stimulus/non-target/41	0.430														0.000
121	stimulus/non-target/41	0.305														0.000
122	stimulus/non-target/43	0.320																0.000
123	stimulus/non-target/43	0.312																0.000
124	stimulus/target/44		0.305																								0.000
125	stimulus/target/44	0.406																									0.000
126	stimulus/non-target/41	0.375														0.000
127	stimulus/non-target/43	0.320																0.000
128	stimulus/non-target/41	0.312														0.000
129	stimulus/non-target/43	0.281																0.000
130	stimulus/non-target/43	0.305																0.000
131	stimulus/non-target/45	0.312																	0.000
132	stimulus/non-target/45	0.266																	0.000
133	stimulus/non-target/43	0.359																0.000
134	stimulus/target/44	0.344																									0.000
135	stimulus/non-target/41	0.328														0.000
136	stimulus/non-target/42	0.344															0.000
137	stimulus/non-target/45	0.312																	0.000
138	stimulus/non-target/43	0.297																0.000
139	stimulus/target/44	0.414																									0.000
140	stimulus/non-target/42	0.375															0.000
141	stimulus/non-target/45	0.352																	0.000
142	stimulus/non-target/42	0.383															0.000
143	stimulus/target/44	0.344																									0.000
144	stimulus/non-target/41	0.391														0.000
145	stimulus/non-target/43	0.320																0.000
146	stimulus/non-target/43	0.352																0.000
147	stimulus/non-target/43	0.320																0.000
148	stimulus/target/44	0.430																									0.000
149	stimulus/non-target/45	0.305																	0.000
150	stimulus/target/44	0.445																									0.000
151	stimulus/non-target/45	0.391																	0.000
152	stimulus/non-target/43	0.344																0.000
153	stimulus/non-target/45	0.336																	0.000
154	stimulus/non-target/41	0.312														0.000
155	stimulus/non-target/43	0.352																0.000
156	stimulus/non-target/43	0.414																0.000
157	stimulus/non-target/45	0.367																	0.000
158	stimulus/non-target/43	0.422																0.000
159	stimulus/target/44		0.352																								0.000
160	stimulus/non-target/54	0.383																					0.000
161	stimulus/non-target/51	0.320																		0.000
162	stimulus/non-target/52	0.305																			0.000
163	stimulus/non-target/54	0.281																					0.000
164	stimulus/non-target/54	0.344																					0.000
165	stimulus/non-target/51	0.328																		0.000
166	stimulus/non-target/52	0.328																			0.000
167	stimulus/non-target/51	0.312																		0.000
168	stimulus/non-target/54	0.344																					0.000
169	stimulus/non-target/51	0.367																		0.000
170	stimulus/non-target/53	0.383																				0.000
171	stimulus/target/55	0.359																										0.000
172	stimulus/non-target/51	0.383																		0.000
173	stimulus/non-target/51	0.289																		0.000
174	stimulus/non-target/54	0.344																					0.000
175	stimulus/non-target/53	0.383																				0.000
176	stimulus/non-target/53	0.328																				0.000
177	stimulus/non-target/52	0.328																			0.000
178	stimulus/non-target/54	0.312																					0.000
179	stimulus/target/55	0.484																										0.000
180	stimulus/target/55	0.430																										0.000
181	stimulus/non-target/54	0.336																					0.000
182	stimulus/non-target/51	0.266																		0.000
184	stimulus/target/55		0.453																									0.000
185	stimulus/non-target/51	0.680																		0.000
186	stimulus/non-target/53	0.438																				0.000
187	stimulus/non-target/54	0.312																					0.000
188	stimulus/target/55	0.352																										0.000
189	stimulus/target/55	0.414																										0.000
190	stimulus/target/55	0.383																										0.000
191	stimulus/non-target/51		0.445																	0.000
192	stimulus/non-target/52	0.367																			0.000
193	stimulus/target/55	0.398																										0.000
194	stimulus/non-target/54	0.273																					0.000
195	stimulus/non-target/54	0.422																					0.000
196	stimulus/non-target/51	0.344																		0.000
197	stimulus/non-target/53	0.445																				0.000
198	stimulus/non-target/53	0.352																				0.000
199	stimulus/non-target/52	0.383																			0.000

199 rows × 29 columns

ERP image (EEG) epochs

Drop log epochs

PSD epochs

PSD calculated from 199 epochs (199.0 s).

ICA cleaning ica

Info ica

Method	picard
Fit parameters	fastica_it=5 max_iter=1000
Fit	110 iterations on epochs (12935 samples)
ICA components	29
Available PCA components	30
Channel types	eeg
ICA components marked for exclusion	ICA000 ICA001 ICA002 ICA004 ICA013 ICA019 ICA020 ICA023

Original and cleaned signal ica

Scores for matching EOG patterns ica

Original and cleaned EOG epochs ica

ICA component topographies ica

Move slider to change view

ICA component properties ica

Move slider to change view

Configuration file configuration

  """ERP CORE.

This example demonstrate how to process 5 participants from the
[ERP CORE](https://erpinfo.org/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](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](https://doi.org/10.1016/j.neuroimage.2020.117465)
"""

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")

System information mne-sysinfo

  Platform             Linux-5.15.0-1053-aws-x86_64-with-glibc2.35
Python               3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
Executable           /home/circleci/python_env/bin/python3.10
CPU                  x86_64 (36 cores)
Memory               68.6 GB

Core
├☑ mne               1.7.0.dev156+g415e7f68e (devel, latest release is 1.6.1)
├☑ numpy             1.26.4 (OpenBLAS 0.3.23.dev with 2 threads)
├☑ scipy             1.12.0
└☑ matplotlib        3.8.3 (backend=agg)

Numerical (optional)
├☑ sklearn           1.4.1.post1
├☑ numba             0.59.1
├☑ nibabel           5.2.1
├☑ pandas            2.2.1
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.43.4 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.0
├☑ vtk               9.3.0
├☑ qtpy              2.4.1 (PyQt6=6.6.0)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.15.0.dev43+g17d20c132
├☑ mne-bids-pipeline 1.8.0
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo

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