Number of events 78
Events coherent/down: 20
coherent/up: 19
incoherent/down: 21
incoherent/up: 18
Time range -0.200 – 1.000 s
Baseline off
Epoch # event_name coherent/down coherent/up incoherent/down incoherent/up
0 incoherent/up 0.000
1 incoherent/up 0.000
2 incoherent/down 0.000
3 coherent/down 0.000
4 coherent/down 0.000
5 coherent/up 0.000
6 coherent/down 0.000
7 coherent/up 0.000
8 coherent/up 0.000
9 incoherent/down 0.000
10 coherent/up 0.000
11 incoherent/up 0.000
12 coherent/up 0.000
13 coherent/up 0.000
14 incoherent/up 0.000
15 coherent/up 0.000
16 incoherent/down 0.000
17 incoherent/up 0.000
18 coherent/down 0.000
19 coherent/down 0.000
20 incoherent/up 0.000
21 incoherent/down 0.000
22 incoherent/down 0.000
23 incoherent/down 0.000
24 coherent/down 0.000
25 incoherent/down 0.000
26 coherent/down 0.000
27 incoherent/up 0.000
28 coherent/up 0.000
29 incoherent/up 0.000
30 coherent/down 0.000
31 incoherent/down 0.000
32 coherent/up 0.000
33 incoherent/down 0.000
34 coherent/down 0.000
35 incoherent/down 0.000
36 incoherent/up 0.000
37 incoherent/up 0.000
38 incoherent/down 0.000
39 coherent/up 0.000
40 incoherent/down 0.000
41 incoherent/up 0.000
42 incoherent/down 0.000
43 incoherent/down 0.000
44 incoherent/down 0.000
45 coherent/down 0.000
46 incoherent/up 0.000
47 coherent/up 0.000
48 incoherent/up 0.000
49 incoherent/down 0.000
50 coherent/up 0.000
51 coherent/down 0.000
52 coherent/down 0.000
53 incoherent/up 0.000
54 coherent/down 0.000
55 coherent/up 0.000
56 incoherent/down 0.000
57 incoherent/up 0.000
58 incoherent/up 0.000
59 incoherent/down 0.000
60 coherent/down 0.000
61 coherent/down 0.000
62 coherent/down 0.000
63 coherent/up 0.000
64 incoherent/up 0.000
65 incoherent/down 0.000
66 coherent/up 0.000
67 coherent/down 0.000
68 incoherent/down 0.000
69 coherent/up 0.000
70 coherent/up 0.000
71 coherent/down 0.000
72 coherent/up 0.000
73 coherent/down 0.000
74 coherent/down 0.000
75 incoherent/up 0.000
76 incoherent/down 0.000
77 coherent/up 0.000

78 rows × 6 columns

Drop log
PSD
PSD calculated from 78 epochs (93.6 s).
  """hMT+ Localizer."""

bids_root = "~/mne_data/ds003392"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds003392"

subjects = ["01"]

task = "localizer"
# usually a good idea to use True, but we know no bads are detected for this dataset
find_flat_channels_meg = False
find_noisy_channels_meg = False
use_maxwell_filter = True
ch_types = ["meg"]

l_freq = 1.0
h_freq = 40.0
raw_resample_sfreq = 250
crop_runs = (0, 180)

# Artifact correction.
spatial_filter = "ica"
ica_algorithm = "picard-extended_infomax"
ica_max_iterations = 1000
ica_l_freq = 1.0
ica_n_components = 0.99

# Epochs
epochs_tmin = -0.2
epochs_tmax = 1.0
baseline = (None, 0)

# Conditions / events to consider when epoching
conditions = ["coherent", "incoherent"]

# Decoding
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 4
contrasts = [("incoherent", "coherent")]
decoding_csp = True
decoding_csp_times = []
decoding_csp_freqs = {
    "alpha": (8, 12),
}

# Noise estimation
noise_cov = "emptyroom"

  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