Number of events 403
Events response/left/correct: 178
response/left/incorrect: 24
response/right/correct: 178
response/right/incorrect: 23
Time range -0.797 – 0.203 s
Baseline off
Epoch #
event_name
compatible/left
compatible/right
incompatible/left
incompatible/right
response/left/correct
response/left/incorrect
response/right/correct
response/right/incorrect
Loading, please wait
Epoch #
event_name
compatible/left
compatible/right
incompatible/left
incompatible/right
response/left/correct
response/left/incorrect
response/right/correct
response/right/incorrect
0response/right/correct0.000
1response/right/correct0.000
2response/left/correct-0.4380.000
3response/right/correct-0.3980.000
4response/left/correct-0.5160.000
5response/right/correct-0.4060.000
6response/right/correct-0.5470.000
7response/left/correct-0.3750.000
8response/right/correct-0.3590.000
9response/right/correct-0.3980.000
10response/right/correct-0.3750.000
11response/left/correct-0.4060.000
12response/right/incorrect-0.3830.000
13response/left/correct-0.4300.000
14response/right/correct-0.3750.000
15response/right/correct-0.4920.000
16response/left/incorrect-0.3750.000
17response/right/correct-0.4840.000
18response/right/correct-0.4770.000
19response/left/correct-0.3910.000
20response/left/correct-0.4140.000
21response/left/correct-0.3830.000
22response/right/correct-0.4060.000
23response/right/correct-0.4220.000
24response/right/correct-0.3520.000
25response/right/correct-0.3750.000
26response/left/correct-0.4450.000
27response/left/correct-0.4920.000
28response/right/correct-0.3590.000
29response/left/correct-0.4530.000
30response/left/correct-0.4300.000
31response/left/correct-0.5470.000
32response/right/correct-0.3910.000
33response/left/correct-0.3750.000
34response/left/correct-0.3520.000
35response/right/correct-0.3590.000
36response/left/correct-0.4450.000
37response/left/correct-0.3910.000
38response/right/correct-0.4140.000
39response/right/correct-0.5550.000
40response/left/correct-0.3830.000
41response/left/correct-0.4840.000
42response/right/correct-0.3520.000
43response/right/correct-0.3200.000
44response/left/correct-0.3750.000
45response/left/correct-0.3120.000
46response/right/correct-0.3520.000
47response/right/correct-0.3360.000
48response/left/incorrect-0.3360.000
49response/right/correct-0.3980.000
50response/right/correct-0.4300.000
51response/right/incorrect-0.3910.000
52response/left/correct-0.3980.000
53response/right/correct-0.4060.000
54response/right/correct-0.3440.000
55response/right/incorrect-0.4140.000
56response/left/incorrect-0.3120.000
57response/left/correct-0.4840.000
58response/left/correct-0.4690.000
59response/left/correct-0.4300.000
60response/right/correct-0.4220.000
61response/left/correct-0.4380.000
62response/left/correct-0.4530.000
63response/left/correct-0.3670.000
64response/left/correct-0.5470.000
65response/right/correct-0.4380.000
66response/left/correct-0.3910.000
67response/left/correct-0.5310.000
68response/right/correct-0.3910.000
69response/right/correct-0.3830.000
70response/left/correct-0.3360.000
71response/left/correct-0.3910.000
72response/right/correct-0.3830.000
73response/left/correct-0.3980.000
74response/left/correct-0.3440.000
75response/right/correct-0.4140.000
76response/left/incorrect-0.3280.000
77response/right/correct-0.4380.000
78response/left/correct-0.4450.000
79response/right/correct-0.3590.000
80response/left/correct-0.3980.000
81response/right/correct-0.3980.000
82response/left/correct-0.5160.000
83response/left/correct-0.3590.000
84response/left/incorrect-0.3280.000
85response/left/correct-0.4300.000
86response/left/correct-0.4220.000
87response/left/correct-0.4770.000
88response/right/correct-0.4300.000
89response/right/correct-0.4220.000
90response/right/correct-0.3120.000
91response/right/correct-0.3280.000
92response/left/correct-0.3520.000
93response/right/correct-0.3520.000
94response/right/correct-0.4140.000
95response/left/correct-0.3830.000
96response/right/correct-0.3440.000
97response/left/incorrect-0.3280.000
98response/right/correct-0.5080.000
99response/left/correct-0.3520.000
100response/left/correct-0.3830.000
101response/right/correct-0.3590.000
102response/right/correct-0.3590.000
103response/right/correct-0.3750.000
104response/left/correct-0.4380.000
105response/right/correct-0.3280.000
106response/right/correct-0.5160.000
107response/left/correct-0.3440.000
108response/right/correct-0.4530.000
109response/left/correct-0.4300.000
110response/left/correct-0.3670.000
111response/left/correct-0.3590.000
112response/right/incorrect-0.3590.000
113response/right/correct-0.4530.000
114response/left/correct-0.3830.000
115response/left/correct-0.5080.000
116response/left/correct-0.3590.000
117response/right/correct-0.4140.000
118response/left/correct-0.3830.000
119response/right/correct-0.3280.000
120response/left/correct-0.4450.000
121response/right/correct-0.4530.000
122response/left/correct-0.3590.000
123response/right/correct-0.3520.000
124response/left/correct-0.3280.000
125response/left/correct-0.3440.000
126response/right/correct-0.3360.000
127response/right/correct-0.3590.000
128response/left/correct-0.4060.000
129response/right/correct-0.3980.000
130response/right/correct-0.3750.000
131response/right/correct-0.3200.000
132response/left/correct-0.3910.000
133response/left/correct-0.4450.000
134response/right/correct-0.4220.000
135response/left/correct-0.3830.000
136response/right/incorrect-0.3120.000
137response/left/correct-0.3980.000
138response/right/correct-0.3980.000
139response/left/correct-0.3670.000
140response/left/correct-0.3830.000
141response/right/correct-0.4060.000
142response/left/correct-0.4060.000
143response/right/correct-0.3440.000
144response/left/correct-0.2730.000
145response/left/correct-0.3440.000
146response/left/correct-0.3520.000
147response/left/correct-0.3200.000
148response/left/correct-0.5940.000
149response/right/correct-0.4300.000
150response/left/correct-0.3360.000
151response/right/correct-0.3520.000
152response/left/incorrect-0.3440.000
153response/right/correct-0.3830.000
154response/right/correct-0.3440.000
155response/left/incorrect-0.2810.000
156response/right/correct-0.4060.000
157response/left/correct-0.4920.000
158response/right/correct-0.3670.000
159response/left/correct-0.4840.000
160response/right/correct-0.4380.000
161response/right/correct-0.3590.000
162response/left/correct-0.3980.000
163response/left/correct-0.5940.000
164response/left/correct-0.3590.000
165response/right/correct-0.3200.000
166response/left/correct-0.3670.000
167response/left/correct-0.3590.000
168response/right/correct-0.3520.000
169response/left/correct-0.3050.000
170response/right/incorrect-0.3050.000
171response/left/correct-0.3590.000
172response/right/correct-0.3520.000
173response/left/correct-0.4450.000
174response/right/correct-0.3750.000
175response/left/correct-0.3590.000
176response/right/correct-0.4060.000
177response/left/correct-0.4300.000
178response/left/correct-0.4060.000
179response/right/correct-0.3360.000
180response/right/correct-0.5080.000
181response/right/incorrect-0.3440.000
182response/right/correct-0.5000.000
183response/right/correct-0.4220.000
184response/right/correct-0.3440.000
185response/right/incorrect-0.3520.000
186response/right/correct-0.3980.000
187response/right/correct-0.3520.000
188response/left/correct-0.3670.000
189response/right/correct-0.4530.000
190response/right/correct-0.3980.000
191response/left/incorrect-0.3360.000
192response/right/correct-0.4380.000
193response/right/correct-0.4770.000
194response/left/correct-0.4140.000
195response/left/correct-0.5620.000
196response/left/correct-0.4220.000
197response/right/correct-0.5940.000
198response/right/correct-0.3440.000
199response/right/correct-0.3440.000
200response/left/correct-0.3670.000
201response/left/correct-0.3440.000
202response/left/correct-0.4770.000
203response/left/correct-0.3980.000
204response/left/correct-0.3830.000
205response/right/correct-0.3830.000
206response/left/incorrect-0.3590.000
207response/left/incorrect-0.3440.000
208response/right/correct-0.4220.000
209response/right/correct-0.4220.000
210response/right/correct-0.3440.000
211response/left/correct-0.3520.000
212response/right/correct-0.5160.000
213response/right/correct-0.3520.000
214response/right/correct-0.3910.000
215response/left/correct-0.3980.000
216response/left/correct-0.3910.000
217response/right/correct-0.3670.000
218response/left/correct-0.3200.000
219response/left/correct-0.3910.000
220response/right/correct-0.3980.000
221response/left/correct-0.3520.000
222response/left/correct-0.4690.000
223response/right/correct-0.4140.000
224response/right/correct-0.3590.000
225response/left/correct-0.3520.000
226response/left/correct-0.4140.000
227response/right/correct-0.3750.000
228response/left/correct-0.4140.000
229response/right/correct-0.4380.000
230response/left/correct-0.3440.000
231response/left/correct-0.3910.000
232response/right/incorrect-0.3910.000
233response/left/correct-0.3750.000
234response/right/correct-0.3980.000
235response/left/correct-0.4380.000
236response/left/correct-0.3830.000
237response/right/correct-0.3440.000
238response/right/correct-0.4840.000
239response/right/correct-0.3440.000
240response/right/correct-0.3120.000
241response/left/correct-0.3830.000
242response/right/correct-0.4300.000
243response/left/correct-0.3280.000
244response/left/correct-0.3440.000
245response/left/correct-0.3440.000
246response/left/correct-0.4450.000
247response/right/incorrect-0.3360.000
248response/left/correct-0.4060.000
249response/right/correct-0.3280.000
250response/right/correct-0.3590.000
251response/left/incorrect-0.3440.000
252response/left/correct-0.3520.000
253response/left/correct-0.3440.000
254response/left/correct-0.3520.000
255response/right/correct-0.3670.000
256response/left/correct-0.4770.000
257response/right/correct-0.4380.000
258response/left/correct-0.3910.000
259response/left/correct-0.3830.000
260response/right/correct-0.4690.000
261response/right/correct-0.3830.000
262response/right/correct-0.4530.000
263response/right/correct-0.3440.000
264response/right/correct-0.4060.000
265response/left/correct-0.3830.000
266response/right/correct-0.3280.000
267response/right/correct-0.3670.000
268response/right/correct-0.3360.000
269response/left/correct-0.3520.000
270response/left/correct-0.4530.000
271response/right/correct-0.3360.000
272response/right/correct-0.3750.000
273response/right/incorrect-0.3280.000
274response/right/correct-0.3980.000
275response/left/correct-0.3120.000
276response/right/correct-0.4380.000
277response/right/correct-0.4140.000
278response/left/incorrect-0.3670.000
279response/left/correct-0.4060.000
280response/left/correct-0.3520.000
281response/left/correct-0.3200.000
282response/right/correct-0.3980.000
283response/right/correct-0.4300.000
284response/right/correct-0.4610.000
285response/right/incorrect-0.2970.000
286response/left/correct-0.4140.000
287response/right/correct-0.4220.000
288response/right/correct-0.3120.000
289response/right/correct-0.3360.000
290response/right/correct-0.4220.000
291response/right/incorrect-0.2810.000
292response/left/correct-0.3910.000
293response/left/correct-0.3120.000
294response/left/correct-0.3280.000
295response/right/correct-0.3120.000
296response/left/incorrect-0.3280.000
297response/right/correct-0.3520.000
298response/right/correct-0.3910.000
299response/left/correct-0.3440.000
300response/right/correct-0.3050.000
301response/right/correct-0.4530.000
302response/right/incorrect-0.3440.000
303response/left/correct-0.3910.000
304response/right/incorrect-0.3750.000
305response/left/correct-0.3980.000
306response/right/correct-0.3750.000
307response/left/correct-0.3200.000
308response/right/correct-0.3980.000
309response/left/incorrect-0.3280.000
310response/right/correct-0.3830.000
311response/right/incorrect-0.3590.000
312response/left/correct-0.3670.000
313response/right/correct-0.3520.000
314response/left/correct-0.4840.000
315response/left/correct-0.3590.000
316response/right/correct-0.3980.000
317response/right/correct-0.3830.000
318response/left/correct-0.4380.000
319response/left/correct-0.3590.000
320response/left/correct-0.3360.000
321response/left/correct-0.3670.000
322response/right/correct-0.3750.000
323response/right/incorrect-0.3980.000
324response/left/correct-0.3280.000
325response/right/correct-0.4300.000
326response/left/incorrect-0.2970.000
327response/left/correct-0.4450.000
328response/left/correct-0.3200.000
329response/left/correct-0.3440.000
330response/left/correct-0.3980.000
331response/left/correct-0.3910.000
332response/right/correct-0.3520.000
333response/right/correct-0.3980.000
334response/right/correct-0.3590.000
335response/right/incorrect-0.3280.000
336response/right/correct-0.3520.000
337response/right/correct-0.3520.000
338response/left/correct-0.2580.000
339response/right/correct-0.3440.000
340response/right/correct-0.3590.000
341response/left/correct-0.4140.000
342response/right/correct-0.4610.000
343response/right/correct-0.3910.000
344response/left/correct-0.3050.000
345response/left/incorrect-0.3120.000
346response/left/correct-0.3910.000
347response/right/incorrect-0.3590.000
348response/right/correct-0.4450.000
349response/left/correct-0.3360.000
350response/right/incorrect-0.3360.000
351response/left/correct-0.4060.000
352response/right/correct-0.3360.000
353response/left/incorrect-0.3440.000
354response/left/correct-0.4140.000
355response/right/correct-0.3440.000
356response/left/correct-0.3670.000
357response/right/correct-0.3830.000
358response/right/correct-0.4920.000
359response/left/correct-0.2890.000
360response/right/correct-0.2970.000
361response/left/correct-0.2580.000
362response/right/incorrect-0.3520.000
363response/left/incorrect-0.3830.000
364response/left/correct-0.3750.000
365response/right/correct-0.4380.000
366response/left/correct-0.3360.000
367response/right/correct-0.3280.000
368response/left/incorrect-0.2890.000
369response/left/correct-0.4920.000
370response/left/correct-0.5080.000
371response/right/correct-0.4300.000
372response/right/correct-0.3440.000
373response/right/correct-0.3590.000
374response/left/incorrect-0.3360.000
375response/left/correct-0.3670.000
376response/left/correct-0.4530.000
377response/right/correct-0.3980.000
378response/right/correct-0.3670.000
379response/left/correct-0.3280.000
380response/left/incorrect-0.4220.000
381response/right/correct-0.3590.000
382response/left/incorrect-0.3750.000
383response/left/incorrect-0.3520.000
384response/left/correct-0.4690.000
385response/left/correct-0.4300.000
386response/right/correct-0.4770.000
387response/left/correct-0.4450.000
388response/right/correct-0.3280.000
389response/right/correct-0.3910.000
390response/left/correct-0.3360.000
391response/right/incorrect-0.3360.000
392response/left/correct-0.3830.000
393response/left/correct-0.2970.000
394response/left/correct-0.3590.000
395response/right/correct-0.3910.000
396response/left/correct-0.3590.000
397response/left/correct-0.3670.000
398response/right/correct-0.5000.000
399response/right/correct-0.3590.000
400response/left/correct-0.3360.000
401response/left/correct-0.4610.000
402response/right/incorrect0.000

403 rows × 10 columns

No epochs exceeded the rejection thresholds. Nothing was dropped.
PSD
PSD calculated from 403 epochs (403.0 s).
  """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")

  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