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
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)
Drop log
PSD
PSD calculated from 199 epochs (199.0 s).
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		 
  """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