diff --git a/doc/tutorials/0-1_meg_preprocessing.py b/doc/tutorials/0-1_meg_preprocessing.py
index cffbd077..b7379288 100644
--- a/doc/tutorials/0-1_meg_preprocessing.py
+++ b/doc/tutorials/0-1_meg_preprocessing.py
@@ -4,7 +4,7 @@
This tutorial walks through the full MEG processing pipeline step by step:
-1. Preprocessing — Downsample, filter, detect bad segments/channels.
+1. Preprocessing — Downsample, filter, detect bad segments/channels, ICA artefact rejection.
2. Surface Extraction — Extract skull/scalp surfaces from a structural MRI.
3. Coregistration — Align MEG sensor space to MRI space.
4. Forward Model — Compute the lead field matrix.
@@ -161,11 +161,36 @@
# raw = preproc.detect_bad_channels(raw, picks="grad")
#%%
-# Save preprocessing QC plots (PSD, sum-of-squares time series, channel standard deviations). Check that bad segments and channels are being correctly identified.
+# ICA artefact rejection
+# **********************
+#
+# Automatic ICA artefact rejection to remove ECG and EOG artefacts.
+# Two approaches are available:
+#
+# **Option A: ECG/EOG correlation** — Uses MNE-Python's built-in
+# correlation-based detection.
+#
+# .. code-block:: python
+#
+# raw, ica, ic_labels = preproc.ica_ecg_eog_correlation(raw, picks="meg")
+#
+# **Option B: MEGNet automatic labelling** — Uses ``mne-icalabel`` (included
+# in the osl-dynamics conda environments) to classify components with a
+# pre-trained deep learning model. Note, MEGNet was trained on ``'mag'``
+# sensor topographies.
+#
+# .. code-block:: python
+#
+# raw, ica, ic_labels = preproc.ica_label(raw, picks="mag", method="megnet")
+
+#%%
+# Save preprocessing QC plots (PSD, sum-of-squares time series, channel
+# standard deviations, ICA component topographies). Check that bad segments,
+# channels, and ICA components are being correctly identified.
#
# .. code-block:: python
#
-# preproc.save_qc_plots(raw, plots_dir / id, show=True)
+# preproc.save_qc_plots(raw, plots_dir / id, show=True, ica=ica, ic_labels=ic_labels)
#%%
# Save preprocessed data
diff --git a/envs/bmrc.yml b/envs/bmrc.yml
index d82ed818..25731dbd 100644
--- a/envs/bmrc.yml
+++ b/envs/bmrc.yml
@@ -8,15 +8,14 @@ dependencies:
- aiohttp=3.13.3=py312h5d8c7f2_0
- aiosignal=1.4.0=pyhd8ed1ab_0
- alsa-lib=1.2.15.3=hb03c661_0
- - anyio=4.12.1=pyhcf101f3_0
+ - anyio=4.13.0=pyhcf101f3_0
- aom=3.9.1=hac33072_0
- argon2-cffi=25.1.0=pyhd8ed1ab_0
- argon2-cffi-bindings=25.1.0=py312h4c3975b_2
- arrow=1.4.0=pyhcf101f3_0
- asttokens=3.0.1=pyhd8ed1ab_0
- - async-lru=2.2.0=pyhcf101f3_0
- - attr=2.5.2=h39aace5_0
- - attrs=25.4.0=pyhcf101f3_1
+ - async-lru=2.3.0=pyhcf101f3_0
+ - attrs=26.1.0=pyhcf101f3_0
- babel=2.18.0=pyhcf101f3_1
- backports.zstd=1.3.0=py312h90b7ffd_0
- beautifulsoup4=4.14.3=pyha770c72_0
@@ -38,12 +37,12 @@ dependencies:
- certifi=2026.2.25=pyhd8ed1ab_0
- cffi=2.0.0=py312h460c074_1
- charls=2.4.3=hecca717_0
- - charset-normalizer=3.4.5=pyhd8ed1ab_0
+ - charset-normalizer=3.4.6=pyhd8ed1ab_0
- comm=0.2.3=pyhe01879c_0
- contourpy=1.3.3=py312h0a2e395_4
- cpython=3.12.13=py312hd8ed1ab_0
- cycler=0.12.1=pyhcf101f3_2
- - cyclopts=4.7.0=pyhcf101f3_0
+ - cyclopts=4.10.1=pyhcf101f3_0
- cyrus-sasl=2.1.28=hd9c7081_0
- dav1d=1.2.1=hd590300_0
- dbus=1.16.2=h24cb091_1
@@ -67,19 +66,19 @@ dependencies:
- fonts-conda-forge=1=hc364b38_1
- fonttools=4.62.0=py312h8a5da7c_0
- fqdn=1.5.1=pyhd8ed1ab_1
- - freetype=2.14.2=ha770c72_0
+ - freetype=2.14.3=ha770c72_0
- fribidi=1.0.16=hb03c661_0
- frozenlist=1.7.0=py312h447239a_0
- fslpy=3.27.0=pyhd8ed1ab_0
- gdk-pixbuf=2.44.4=h2b0a6b4_0
- giflib=5.2.2=hd590300_0
- - gl2ps=1.4.2=hae5d5c5_1
+ - gl2ps=1.4.2=h36e74d4_2
- gmp=6.3.0=hac33072_2
- gnutls=3.8.11=h18acefa_1
- graphite2=1.3.14=hecca717_2
- h11=0.16.0=pyhcf101f3_1
- h2=4.3.0=pyhcf101f3_0
- - h5io=0.2.5=pyhecae5ae_0
+ - h5io=0.2.5=pyhc455866_0
- h5py=3.13.0=nompi_py312hedeef09_100
- harfbuzz=12.2.0=h15599e2_0
- hdf4=4.2.15=h2a13503_7
@@ -92,9 +91,9 @@ dependencies:
- idna=3.11=pyhd8ed1ab_0
- imagecodecs=2026.1.14=py312h40df4bb_1
- imageio=2.37.0=pyhfb79c49_0
- - importlib-metadata=8.7.0=pyhe01879c_1
+ - importlib-metadata=8.8.0=pyhcf101f3_0
- importlib_resources=6.5.2=pyhd8ed1ab_0
- - intel-gmmlib=22.9.0=hb700be7_0
+ - intel-gmmlib=22.10.0=hb700be7_0
- intel-media-driver=25.3.4=hecca717_0
- ipyevents=2.0.4=pyhbbac1ac_0
- ipykernel=7.2.0=pyha191276_1
@@ -107,7 +106,7 @@ dependencies:
- joblib=1.5.3=pyhd8ed1ab_0
- json5=0.13.0=pyhd8ed1ab_0
- jsoncpp=1.9.6=hf42df4d_1
- - jsonpointer=3.0.0=pyhcf101f3_3
+ - jsonpointer=3.1.1=pyhcf101f3_0
- jsonschema=4.26.0=pyhcf101f3_0
- jsonschema-specifications=2025.9.1=pyhcf101f3_0
- jsonschema-with-format-nongpl=4.26.0=hcf101f3_0
@@ -132,7 +131,7 @@ dependencies:
- lazy-loader=0.5=pyhd8ed1ab_0
- lazy_loader=0.5=pyhd8ed1ab_0
- lcms2=2.18=h0c24ade_0
- - ld_impl_linux-64=2.45.1=default_hbd61a6d_101
+ - ld_impl_linux-64=2.45.1=default_hbd61a6d_102
- lerc=4.1.0=hdb68285_0
- level-zero=1.28.2=hb700be7_0
- libabseil=20250512.1=cxx17_hba17884_0
@@ -144,7 +143,7 @@ dependencies:
- libbrotlicommon=1.2.0=hb03c661_1
- libbrotlidec=1.2.0=hb03c661_1
- libbrotlienc=1.2.0=hb03c661_1
- - libcap=2.77=h3ff7636_0
+ - libcap=2.77=hd0affe5_1
- libcblas=3.11.0=5_h0358290_openblas
- libclang-cpp21.1=21.1.0=default_h99862b1_1
- libclang13=21.1.0=default_h746c552_1
@@ -158,8 +157,8 @@ dependencies:
- libexpat=2.7.4=hecca717_0
- libffi=3.5.2=h3435931_0
- libflac=1.5.0=he200343_1
- - libfreetype=2.14.2=ha770c72_0
- - libfreetype6=2.14.2=h73754d4_0
+ - libfreetype=2.14.3=ha770c72_0
+ - libfreetype6=2.14.3=h73754d4_0
- libgcc=15.2.0=he0feb66_18
- libgcc-ng=15.2.0=h69a702a_18
- libgfortran=15.2.0=h69a702a_18
@@ -181,7 +180,7 @@ dependencies:
- liblzma=5.8.2=hb03c661_0
- libmicrohttpd=1.0.2=hc2fc477_0
- libnetcdf=4.9.2=nompi_h00e09a9_116
- - libnghttp2=1.67.0=had1ee68_0
+ - libnghttp2=1.68.1=h877daf1_0
- libnsl=2.0.1=hb9d3cd8_1
- libntlm=1.8=hb9d3cd8_0
- libogg=1.3.5=hd0c01bc_1
@@ -212,11 +211,11 @@ dependencies:
- libssh2=1.11.1=hcf80075_0
- libstdcxx=15.2.0=h934c35e_18
- libstdcxx-ng=15.2.0=hdf11a46_18
- - libsystemd0=257.10=hd0affe5_4
+ - libsystemd0=257.13=hd0affe5_0
- libtasn1=4.21.0=hb03c661_0
- libtheora=1.1.1=h4ab18f5_1006
- libtiff=4.7.1=h9d88235_1
- - libudev1=257.10=hd0affe5_4
+ - libudev1=257.13=hd0affe5_0
- libunistring=0.9.10=h7f98852_0
- libunwind=1.8.3=h65a8314_0
- liburing=2.12=hb700be7_0
@@ -234,7 +233,7 @@ dependencies:
- libxml2=2.13.9=h04c0eec_0
- libxslt=1.1.43=h7a3aeb2_0
- libzip=1.11.2=h6991a6a_0
- - libzlib=1.3.1=hb9d3cd8_2
+ - libzlib=1.3.2=h25fd6f3_2
- libzopfli=1.0.3=h9c3ff4c_0
- llvmlite=0.46.0=py312h7424e68_0
- loguru=0.7.3=pyh707e725_0
@@ -270,7 +269,7 @@ dependencies:
- notebook-shim=0.2.4=pyhd8ed1ab_1
- numba=0.64.0=py312hd1dde6f_0
- ocl-icd=2.3.3=hb9d3cd8_0
- - opencl-headers=2025.06.13=h5888daf_0
+ - opencl-headers=2025.06.13=hecca717_0
- openh264=2.6.0=hc22cd8d_0
- openjpeg=2.5.4=h55fea9a_0
- openjph=0.26.3=h8d634f6_0
@@ -326,7 +325,7 @@ dependencies:
- rav1e=0.7.1=h8fae777_3
- readline=8.3=h853b02a_0
- referencing=0.37.0=pyhcf101f3_0
- - requests=2.32.5=pyhcf101f3_1
+ - requests=2.33.0=pyhcf101f3_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_1
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- rfc3987-syntax=1.1.0=pyhe01879c_1
@@ -358,14 +357,14 @@ dependencies:
- tinycss2=1.4.0=pyhd8ed1ab_0
- tk=8.6.13=noxft_h366c992_103
- tomli=2.4.0=pyhcf101f3_0
- - tornado=6.5.3=py312h4c3975b_0
+ - tornado=6.5.5=py312h4c3975b_0
- tqdm=4.67.3=pyh8f84b5b_0
- traitlets=5.14.3=pyhd8ed1ab_1
- trame=3.12.0=pyhd8ed1ab_0
- - trame-client=3.11.3=pyhd8ed1ab_0
- - trame-common=1.1.2=pyhd8ed1ab_0
+ - trame-client=3.11.4=pyhd8ed1ab_0
+ - trame-common=1.1.3=pyhd8ed1ab_0
- trame-server=3.10.0=pyhd8ed1ab_0
- - trame-vtk=2.11.1=pyh932262d_0
+ - trame-vtk=2.11.5=pyh3504b2d_0
- trame-vuetify=3.2.1=pyhd8ed1ab_0
- typing-extensions=4.15.0=h396c80c_0
- typing_extensions=4.15.0=pyhcf101f3_0
@@ -378,7 +377,7 @@ dependencies:
- vtk=9.3.1=osmesa_py312hf4758c4_116
- vtk-base=9.3.1=osmesa_py312hc9bc066_116
- vtk-io-ffmpeg=9.3.1=osmesa_py312hf4758c4_116
- - wayland=1.24.0=hd6090a7_1
+ - wayland=1.25.0=hd6090a7_0
- wayland-protocols=1.47=hd8ed1ab_0
- wcwidth=0.6.0=pyhd8ed1ab_0
- webcolors=25.10.0=pyhd8ed1ab_0
@@ -419,7 +418,7 @@ dependencies:
- zeromq=4.3.5=h387f397_9
- zfp=1.0.1=h909a3a2_5
- zipp=3.23.0=pyhcf101f3_1
- - zlib=1.3.1=hb9d3cd8_2
+ - zlib=1.3.2=h25fd6f3_2
- zlib-ng=2.3.3=hceb46e0_1
- zstd=1.5.7=hb78ec9c_6
- pip:
@@ -435,6 +434,8 @@ dependencies:
- libclang==18.1.1
- markdown==3.10.2
- ml-dtypes==0.5.4
+ - mne-icalabel==0.8.1
+ - mpmath==1.3.0
- namex==0.1.0
- numpy==2.1.3
- nvidia-cublas-cu12==12.5.3.2
@@ -449,15 +450,17 @@ dependencies:
- nvidia-cusparse-cu12==12.5.1.3
- nvidia-nccl-cu12==2.23.4
- nvidia-nvjitlink-cu12==12.5.82
+ - onnxruntime==1.24.4
- opt-einsum==3.4.0
- optree==0.19.0
- protobuf==5.29.6
+ - sympy==1.14.0
- tensorboard==2.19.0
- tensorboard-data-server==0.7.2
- tensorflow==2.19.0
- tensorflow-probability==0.25.0
- termcolor==3.3.0
- tf-keras==2.19.0
- - werkzeug==3.1.6
+ - werkzeug==3.1.7
- wrapt==2.1.2
- osl-dynamics
diff --git a/envs/fsl.yml b/envs/fsl.yml
index 6a306830..1bd228b6 100644
--- a/envs/fsl.yml
+++ b/envs/fsl.yml
@@ -31,5 +31,6 @@ dependencies:
- trame-vtk
- trame-vuetify
- pip:
+ - mne-icalabel[onnx]
- tensorflow==2.19
- tensorflow-probability[tf]==0.25
diff --git a/envs/hbaws.yml b/envs/hbaws.yml
index 663933e8..2239a6cf 100644
--- a/envs/hbaws.yml
+++ b/envs/hbaws.yml
@@ -8,15 +8,14 @@ dependencies:
- aiohttp=3.13.3=py312h5d8c7f2_0
- aiosignal=1.4.0=pyhd8ed1ab_0
- alsa-lib=1.2.15.3=hb03c661_0
- - anyio=4.12.1=pyhcf101f3_0
+ - anyio=4.13.0=pyhcf101f3_0
- aom=3.9.1=hac33072_0
- argon2-cffi=25.1.0=pyhd8ed1ab_0
- argon2-cffi-bindings=25.1.0=py312h4c3975b_2
- arrow=1.4.0=pyhcf101f3_0
- asttokens=3.0.1=pyhd8ed1ab_0
- - async-lru=2.2.0=pyhcf101f3_0
- - attr=2.5.2=h39aace5_0
- - attrs=25.4.0=pyhcf101f3_1
+ - async-lru=2.3.0=pyhcf101f3_0
+ - attrs=26.1.0=pyhcf101f3_0
- babel=2.18.0=pyhcf101f3_1
- backports.zstd=1.3.0=py312h90b7ffd_0
- beautifulsoup4=4.14.3=pyha770c72_0
@@ -38,12 +37,12 @@ dependencies:
- certifi=2026.2.25=pyhd8ed1ab_0
- cffi=2.0.0=py312h460c074_1
- charls=2.4.3=hecca717_0
- - charset-normalizer=3.4.5=pyhd8ed1ab_0
+ - charset-normalizer=3.4.6=pyhd8ed1ab_0
- comm=0.2.3=pyhe01879c_0
- contourpy=1.3.3=py312h0a2e395_4
- cpython=3.12.13=py312hd8ed1ab_0
- cycler=0.12.1=pyhcf101f3_2
- - cyclopts=4.7.0=pyhcf101f3_0
+ - cyclopts=4.10.1=pyhcf101f3_0
- cyrus-sasl=2.1.28=hd9c7081_0
- dav1d=1.2.1=hd590300_0
- dbus=1.16.2=h24cb091_1
@@ -67,19 +66,19 @@ dependencies:
- fonts-conda-forge=1=hc364b38_1
- fonttools=4.62.0=py312h8a5da7c_0
- fqdn=1.5.1=pyhd8ed1ab_1
- - freetype=2.14.2=ha770c72_0
+ - freetype=2.14.3=ha770c72_0
- fribidi=1.0.16=hb03c661_0
- frozenlist=1.7.0=py312h447239a_0
- fslpy=3.27.0=pyhd8ed1ab_0
- gdk-pixbuf=2.44.4=h2b0a6b4_0
- giflib=5.2.2=hd590300_0
- - gl2ps=1.4.2=hae5d5c5_1
+ - gl2ps=1.4.2=h36e74d4_2
- gmp=6.3.0=hac33072_2
- gnutls=3.8.11=h18acefa_1
- graphite2=1.3.14=hecca717_2
- h11=0.16.0=pyhcf101f3_1
- h2=4.3.0=pyhcf101f3_0
- - h5io=0.2.5=pyhecae5ae_0
+ - h5io=0.2.5=pyhc455866_0
- h5py=3.13.0=nompi_py312hedeef09_100
- harfbuzz=12.2.0=h15599e2_0
- hdf4=4.2.15=h2a13503_7
@@ -92,9 +91,9 @@ dependencies:
- idna=3.11=pyhd8ed1ab_0
- imagecodecs=2026.1.14=py312h40df4bb_1
- imageio=2.37.0=pyhfb79c49_0
- - importlib-metadata=8.7.0=pyhe01879c_1
+ - importlib-metadata=8.8.0=pyhcf101f3_0
- importlib_resources=6.5.2=pyhd8ed1ab_0
- - intel-gmmlib=22.9.0=hb700be7_0
+ - intel-gmmlib=22.10.0=hb700be7_0
- intel-media-driver=25.3.4=hecca717_0
- ipyevents=2.0.4=pyhbbac1ac_0
- ipykernel=7.2.0=pyha191276_1
@@ -107,7 +106,7 @@ dependencies:
- joblib=1.5.3=pyhd8ed1ab_0
- json5=0.13.0=pyhd8ed1ab_0
- jsoncpp=1.9.6=hf42df4d_1
- - jsonpointer=3.0.0=pyhcf101f3_3
+ - jsonpointer=3.1.1=pyhcf101f3_0
- jsonschema=4.26.0=pyhcf101f3_0
- jsonschema-specifications=2025.9.1=pyhcf101f3_0
- jsonschema-with-format-nongpl=4.26.0=hcf101f3_0
@@ -132,7 +131,7 @@ dependencies:
- lazy-loader=0.5=pyhd8ed1ab_0
- lazy_loader=0.5=pyhd8ed1ab_0
- lcms2=2.18=h0c24ade_0
- - ld_impl_linux-64=2.45.1=default_hbd61a6d_101
+ - ld_impl_linux-64=2.45.1=default_hbd61a6d_102
- lerc=4.1.0=hdb68285_0
- level-zero=1.28.2=hb700be7_0
- libabseil=20250512.1=cxx17_hba17884_0
@@ -144,7 +143,7 @@ dependencies:
- libbrotlicommon=1.2.0=hb03c661_1
- libbrotlidec=1.2.0=hb03c661_1
- libbrotlienc=1.2.0=hb03c661_1
- - libcap=2.77=h3ff7636_0
+ - libcap=2.77=hd0affe5_1
- libcblas=3.11.0=5_h0358290_openblas
- libclang-cpp21.1=21.1.0=default_h99862b1_1
- libclang13=21.1.0=default_h746c552_1
@@ -158,8 +157,8 @@ dependencies:
- libexpat=2.7.4=hecca717_0
- libffi=3.5.2=h3435931_0
- libflac=1.5.0=he200343_1
- - libfreetype=2.14.2=ha770c72_0
- - libfreetype6=2.14.2=h73754d4_0
+ - libfreetype=2.14.3=ha770c72_0
+ - libfreetype6=2.14.3=h73754d4_0
- libgcc=15.2.0=he0feb66_18
- libgcc-ng=15.2.0=h69a702a_18
- libgfortran=15.2.0=h69a702a_18
@@ -181,7 +180,7 @@ dependencies:
- liblzma=5.8.2=hb03c661_0
- libmicrohttpd=1.0.2=hc2fc477_0
- libnetcdf=4.9.2=nompi_h00e09a9_116
- - libnghttp2=1.67.0=had1ee68_0
+ - libnghttp2=1.68.1=h877daf1_0
- libnsl=2.0.1=hb9d3cd8_1
- libntlm=1.8=hb9d3cd8_0
- libogg=1.3.5=hd0c01bc_1
@@ -212,11 +211,11 @@ dependencies:
- libssh2=1.11.1=hcf80075_0
- libstdcxx=15.2.0=h934c35e_18
- libstdcxx-ng=15.2.0=hdf11a46_18
- - libsystemd0=259.4=h6569c3e_0
+ - libsystemd0=260.1=h6569c3e_0
- libtasn1=4.21.0=hb03c661_0
- libtheora=1.1.1=h4ab18f5_1006
- libtiff=4.7.1=h9d88235_1
- - libudev1=259.4=h6569c3e_0
+ - libudev1=260.1=h6569c3e_0
- libunistring=0.9.10=h7f98852_0
- libunwind=1.8.3=h65a8314_0
- liburing=2.12=hb700be7_0
@@ -234,7 +233,7 @@ dependencies:
- libxml2=2.13.9=h04c0eec_0
- libxslt=1.1.43=h7a3aeb2_0
- libzip=1.11.2=h6991a6a_0
- - libzlib=1.3.1=hb9d3cd8_2
+ - libzlib=1.3.2=h25fd6f3_2
- libzopfli=1.0.3=h9c3ff4c_0
- llvmlite=0.46.0=py312h7424e68_0
- loguru=0.7.3=pyh707e725_0
@@ -270,7 +269,7 @@ dependencies:
- notebook-shim=0.2.4=pyhd8ed1ab_1
- numba=0.64.0=py312hd1dde6f_0
- ocl-icd=2.3.3=hb9d3cd8_0
- - opencl-headers=2025.06.13=h5888daf_0
+ - opencl-headers=2025.06.13=hecca717_0
- openh264=2.6.0=hc22cd8d_0
- openjpeg=2.5.4=h55fea9a_0
- openjph=0.26.3=h8d634f6_0
@@ -326,7 +325,7 @@ dependencies:
- rav1e=0.7.1=h8fae777_3
- readline=8.3=h853b02a_0
- referencing=0.37.0=pyhcf101f3_0
- - requests=2.32.5=pyhcf101f3_1
+ - requests=2.33.0=pyhcf101f3_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_1
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- rfc3987-syntax=1.1.0=pyhe01879c_1
@@ -358,14 +357,14 @@ dependencies:
- tinycss2=1.4.0=pyhd8ed1ab_0
- tk=8.6.13=noxft_h366c992_103
- tomli=2.4.0=pyhcf101f3_0
- - tornado=6.5.4=py312h961da02_0
+ - tornado=6.5.5=py312h4c3975b_0
- tqdm=4.67.3=pyh8f84b5b_0
- traitlets=5.14.3=pyhd8ed1ab_1
- trame=3.12.0=pyhd8ed1ab_0
- - trame-client=3.11.3=pyhd8ed1ab_0
- - trame-common=1.1.2=pyhd8ed1ab_0
+ - trame-client=3.11.4=pyhd8ed1ab_0
+ - trame-common=1.1.3=pyhd8ed1ab_0
- trame-server=3.10.0=pyhd8ed1ab_0
- - trame-vtk=2.11.1=pyh932262d_0
+ - trame-vtk=2.11.5=pyh3504b2d_0
- trame-vuetify=3.2.1=pyhd8ed1ab_0
- typing-extensions=4.15.0=h396c80c_0
- typing_extensions=4.15.0=pyhcf101f3_0
@@ -378,7 +377,7 @@ dependencies:
- vtk=9.3.1=osmesa_py312hf4758c4_116
- vtk-base=9.3.1=osmesa_py312hc9bc066_116
- vtk-io-ffmpeg=9.3.1=osmesa_py312hf4758c4_116
- - wayland=1.24.0=hd6090a7_1
+ - wayland=1.25.0=hd6090a7_0
- wayland-protocols=1.47=hd8ed1ab_0
- wcwidth=0.6.0=pyhd8ed1ab_0
- webcolors=25.10.0=pyhd8ed1ab_0
@@ -419,7 +418,7 @@ dependencies:
- zeromq=4.3.5=h387f397_9
- zfp=1.0.1=h909a3a2_5
- zipp=3.23.0=pyhcf101f3_1
- - zlib=1.3.1=hb9d3cd8_2
+ - zlib=1.3.2=h25fd6f3_2
- zlib-ng=2.3.3=hceb46e0_1
- zstd=1.5.7=hb78ec9c_6
- pip:
@@ -435,17 +434,21 @@ dependencies:
- libclang==18.1.1
- markdown==3.10.2
- ml-dtypes==0.5.4
+ - mne-icalabel==0.8.1
+ - mpmath==1.3.0
- namex==0.1.0
- numpy==2.1.3
+ - onnxruntime==1.24.4
- opt-einsum==3.4.0
- optree==0.19.0
- protobuf==5.29.6
+ - sympy==1.14.0
- tensorboard==2.19.0
- tensorboard-data-server==0.7.2
- tensorflow==2.19.0
- tensorflow-probability==0.25.0
- termcolor==3.3.0
- tf-keras==2.19.0
- - werkzeug==3.1.6
+ - werkzeug==3.1.7
- wrapt==2.1.2
- osl-dynamics
diff --git a/envs/osld-tf-cuda.yml b/envs/osld-tf-cuda.yml
index 6fe66b64..00082986 100644
--- a/envs/osld-tf-cuda.yml
+++ b/envs/osld-tf-cuda.yml
@@ -31,6 +31,7 @@ dependencies:
- trame-vtk
- trame-vuetify
- pip:
+ - mne-icalabel[onnx]
- tensorflow[and-cuda]==2.19
- tensorflow-probability[tf]==0.25
- osl-dynamics
diff --git a/envs/osld-tf-macos.yml b/envs/osld-tf-macos.yml
index cab7fd0c..b7443b83 100644
--- a/envs/osld-tf-macos.yml
+++ b/envs/osld-tf-macos.yml
@@ -31,6 +31,7 @@ dependencies:
- trame-vtk
- trame-vuetify
- pip:
+ - mne-icalabel[onnx]
- tensorflow==2.16.1
- tensorflow-probability==0.24
- tf-keras==2.16
diff --git a/envs/osld-tf.yml b/envs/osld-tf.yml
index 399cd793..a4fa1249 100644
--- a/envs/osld-tf.yml
+++ b/envs/osld-tf.yml
@@ -31,6 +31,7 @@ dependencies:
- trame-vtk
- trame-vuetify
- pip:
+ - mne-icalabel[onnx]
- tensorflow==2.19
- tensorflow-probability[tf]==0.25
- osl-dynamics
diff --git a/envs/osld.yml b/envs/osld.yml
index 30e8d207..71896e3b 100644
--- a/envs/osld.yml
+++ b/envs/osld.yml
@@ -31,4 +31,5 @@ dependencies:
- trame-vtk
- trame-vuetify
- pip:
+ - mne-icalabel[onnx]
- osl-dynamics
diff --git a/examples/meg_preproc/1_preproc.py b/examples/meg_preproc/1_preproc.py
index 3a4a4de0..4c115c10 100755
--- a/examples/meg_preproc/1_preproc.py
+++ b/examples/meg_preproc/1_preproc.py
@@ -30,7 +30,7 @@ def process_session(id, info, logger, **kwargs):
raw_file = input_dir / info["subject"] / "meg" / info["file"]
raw = mne.io.read_raw_fif(raw_file, preload=True)
- raw = raw.crop(tmax=30)
+ raw = raw.crop(tmax=60)
logger.log("Filtering and downsampling...")
raw = raw.resample(sfreq=250)
@@ -51,8 +51,11 @@ def process_session(id, info, logger, **kwargs):
raw = preproc.detect_bad_channels(raw, picks="mag")
raw = preproc.detect_bad_channels(raw, picks="grad")
+ logger.log("Running ICA artefact rejection...")
+ raw, ica, ic_labels = preproc.ica_label(raw, picks="meg")
+
logger.log("Saving QC plots...")
- preproc.save_qc_plots(raw, plots_dir / id)
+ preproc.save_qc_plots(raw, plots_dir / id, ica=ica, ic_labels=ic_labels)
logger.log("Saving preprocessed data...")
preproc_out_dir = output_dir / "preprocessed"
diff --git a/examples/meg_preproc/README.md b/examples/meg_preproc/README.md
index 31d1d8f6..e18e3d6e 100755
--- a/examples/meg_preproc/README.md
+++ b/examples/meg_preproc/README.md
@@ -26,7 +26,7 @@ Run the scripts **in order**. Each script processes all sessions in parallel.
| Script | Step | Description |
|--------|------|-------------|
-| `1_preproc.py` | Preprocessing | Downsample (250 Hz), bandpass filter (1-45 Hz), notch filter (50/100 Hz), bad segment and bad channel detection |
+| `1_preproc.py` | Preprocessing | Downsample (250 Hz), bandpass filter (1-45 Hz), notch filter (50/100 Hz), bad segment/channel detection, ICA artefact rejection (based on MEGNet) |
| `2_surfaces.py` | Surface Extraction | Extract inner skull, outer skull and scalp surfaces from structural MRI using FSL BET |
| `3_coreg.py` | Coregistration | Coregister MEG to MRI using Polhemus headshape points |
| `4_source_recon_and_parc.py` | Forward Model, Source Reconstruction and Parcellation | Compute forward model (8 mm dipole grid), LCMV beamformer, parcellate voxel data, apply symmetric orthogonalisation |
@@ -108,6 +108,7 @@ plots/
│ ├── 1_sum_square.png
│ ├── 1_sum_square_exclude_bads.png
│ ├── 1_channel_stds.png
+│ ├── 1_ica_components.png
│ ├── 3_coreg.png
│ └── 4_psd_topo.png
├── sub-02_task-rest/
diff --git a/osl_dynamics/meeg/preproc.py b/osl_dynamics/meeg/preproc.py
index 4808f265..db15ed2c 100644
--- a/osl_dynamics/meeg/preproc.py
+++ b/osl_dynamics/meeg/preproc.py
@@ -2,14 +2,20 @@
import json
from pathlib import Path
-from typing import List, Optional, Union
+from typing import Any, List, Optional, Tuple, Union
import mne
import numpy as np
import matplotlib.pyplot as plt
+from mne.preprocessing import ICA
+from mne_icalabel import label_components
from scipy import stats
from scipy.ndimage.filters import uniform_filter1d
+# -------------------------------------------------------------------------
+# Artefact detection
+# -------------------------------------------------------------------------
+
def detect_bad_segments(
raw: mne.io.Raw,
@@ -348,6 +354,383 @@ def _gesd(
return out_mask
+# -------------------------------------------------------------------------
+# ICA artefact rejection
+# -------------------------------------------------------------------------
+
+
+def ica_label(
+ raw: mne.io.Raw,
+ picks: str = "mag",
+ n_components: int = 20,
+ method: str = "megnet",
+ threshold: float = 0.5,
+ random_state: int = 42,
+) -> Tuple[mne.io.Raw, Any, dict]:
+ """Automatic ICA artefact rejection using mne-icalabel.
+
+ Fits ICA on a bandpass-filtered copy of the data, labels components
+ using a pre-trained classifier, and removes artefact components from
+ the original data.
+
+ For MEG data, uses the MEGNet classifier. For EEG data, uses ICLabel.
+
+ Parameters
+ ----------
+ raw : mne.io.Raw
+ MNE Raw object.
+ picks : str, optional
+ Channel type to use for ICA. For Elekta MEG data, use ``"mag"``
+ (MEGNet was trained on magnetometer topographies).
+ n_components : int, optional
+ Number of ICA components. Should not exceed the data rank.
+ For MaxFiltered Elekta data (rank ~60), 20 is a safe default.
+ method : str, optional
+ Labelling method: ``"megnet"`` for MEG or ``"iclabel"`` for EEG.
+ threshold : float, optional
+ Probability threshold (0–1) for excluding a component. Components
+ labelled as artefact with probability above this threshold are
+ removed.
+ random_state : int, optional
+ Random seed for ICA reproducibility.
+
+ Returns
+ -------
+ raw : mne.io.Raw
+ Cleaned MNE Raw object.
+ ica : mne.preprocessing.ICA
+ Fitted ICA object with ``exclude`` set.
+ ic_labels : dict
+ Dictionary with keys ``"labels"`` (list of str) and
+ ``"y_pred_proba"`` (array of float).
+ Notes
+ -----
+ For EEG data, use ``picks="eeg"`` and ``method="iclabel"``::
+
+ raw, ica, ic_labels = preproc.ica_label(
+ raw, picks="eeg", method="iclabel", n_components=30,
+ )
+ """
+ print()
+ print("ICA artefact rejection")
+ print("----------------------")
+ print(f"Method: {method}")
+ print(f"Picks: {picks}")
+ print(f"Components: {n_components}")
+ print(f"Threshold: {threshold}")
+
+ # Filter a copy for ICA fitting (classifiers expect 1-100 Hz)
+ print("Filtering data copy (1-100 Hz) for ICA fitting...")
+ raw_fit = raw.copy().filter(l_freq=1.0, h_freq=100.0, verbose=False)
+
+ # ICLabel (EEG) requires average reference
+ if method == "iclabel":
+ raw_fit.set_eeg_reference("average", verbose=False)
+
+ # Fit ICA
+ print("Fitting ICA...")
+ ica = ICA(
+ n_components=n_components,
+ method="infomax",
+ fit_params=dict(extended=True),
+ random_state=random_state,
+ verbose=False,
+ )
+ ica.fit(raw_fit, picks=picks)
+
+ # Label components
+ print("Labelling components...")
+ ic_labels = label_components(raw_fit, ica, method=method)
+
+ labels = ic_labels["labels"]
+ probs = ic_labels["y_pred_proba"]
+
+ # Identify artefact components (exclude everything except brain/other)
+ # Note: MEGNet returns "brain/other" as a single label, while ICLabel
+ # returns "brain" and "other" separately
+ keep_labels = ["brain", "other", "brain/other"]
+ exclude_idx = []
+ for idx, (label, prob) in enumerate(zip(labels, probs)):
+ if label not in keep_labels:
+ if prob > threshold:
+ exclude_idx.append(idx)
+ print(f" ICA{idx:03d}: {label} ({prob:.2f}) -> excluded")
+ else:
+ print(
+ f" ICA{idx:03d}: {label} ({prob:.2f}) -> kept "
+ f"(below threshold)"
+ )
+
+ # Apply to original data
+ if len(exclude_idx) > 0:
+ print(f"Removing {len(exclude_idx)} artefact component(s)...")
+ ica.exclude = exclude_idx
+ ica.apply(raw)
+ else:
+ print("No artefact components found.")
+
+ return raw, ica, ic_labels
+
+
+def ica_ecg_eog_correlation(
+ raw: mne.io.Raw,
+ picks: str = "meg",
+ n_components: int = 40,
+ l_freq: float = 1.0,
+ h_freq: Optional[float] = None,
+ ecg_method: Optional[str] = "ctps",
+ ecg_threshold: Union[str, float] = "auto",
+ eog_measure: str = "correlation",
+ eog_threshold: float = 0.35,
+ random_state: int = 42,
+) -> Tuple[mne.io.Raw, Any, dict]:
+ """ICA artefact rejection using ECG/EOG correlation.
+
+ Fits ICA on a high-pass filtered copy of the data, identifies artefact
+ components by correlating with ECG and EOG signals, and removes them
+ from the original data. Follows the approach used in osl-ephys.
+
+ Uses ``picks="meg"`` by default so both magnetometers and gradiometers
+ are denoised. Does not require mne-icalabel.
+
+ Parameters
+ ----------
+ raw : mne.io.Raw
+ MNE Raw object.
+ picks : str, optional
+ Channel type to use for ICA. ``"meg"`` fits on both mags and grads.
+ n_components : int, optional
+ Number of ICA components. Should not exceed the data rank.
+ For MaxFiltered Elekta data (rank ~60), 40 is a safe default.
+ l_freq : float, optional
+ High-pass filter frequency for the ICA fitting copy.
+ h_freq : float, optional
+ Low-pass filter frequency for the ICA fitting copy.
+ ecg_method : str, optional
+ Method for ECG detection: ``"ctps"`` (cross-trial phase
+ statistics) or ``"correlation"``. Set to ``None`` to skip
+ ECG detection.
+ ecg_threshold : str or float, optional
+ Threshold for ECG component detection.
+ eog_measure : str, optional
+ Measure for EOG detection: ``"correlation"`` or ``"zscore"``.
+ eog_threshold : float, optional
+ Threshold for EOG component detection. When
+ ``eog_measure="correlation"``, this is an absolute correlation
+ threshold (e.g. 0.35). When ``eog_measure="zscore"``, this is
+ a z-score threshold (e.g. 3.0).
+ random_state : int, optional
+ Random seed for ICA reproducibility.
+
+ Returns
+ -------
+ raw : mne.io.Raw
+ Cleaned MNE Raw object.
+ ica : mne.preprocessing.ICA
+ Fitted ICA object with ``exclude`` set.
+ ic_labels : dict
+ Dictionary with keys ``"labels"`` (list of str) and
+ ``"y_pred_proba"`` (array of float), compatible with
+ :func:`plot_ica_components`.
+ Notes
+ -----
+ For EEG data, use ``picks="eeg"``. Note that synthetic ECG detection
+ only works with MEG magnetometers — for EEG data a dedicated ECG
+ channel must be present, otherwise set ``ecg_method=None``::
+
+ raw, ica, ic_labels = preproc.ica_ecg_eog_correlation(
+ raw, picks="eeg", n_components=30, ecg_method=None,
+ )
+ """
+ print()
+ print("ICA artefact rejection (ECG/EOG correlation)")
+ print("---------------------------------------------")
+ print(f"Picks: {picks}")
+ print(f"Components: {n_components}")
+
+ # Filter a copy for ICA fitting
+ print(f"Filtering data copy ({l_freq}-{h_freq} Hz) for ICA fitting...")
+ raw_fit = raw.copy().filter(l_freq=l_freq, h_freq=h_freq, verbose=False)
+
+ # Fit ICA
+ print("Fitting ICA...")
+ ica = ICA(
+ n_components=n_components,
+ method="fastica",
+ random_state=random_state,
+ verbose=False,
+ )
+ ica.fit(raw_fit, picks=picks)
+
+ # Detect ECG components
+ ecg_indices = []
+ ecg_scores = np.zeros(ica.n_components_)
+ if ecg_method is not None:
+ print("Detecting ECG components...")
+ try:
+ ecg_indices, ecg_scores = ica.find_bads_ecg(
+ raw_fit,
+ method=ecg_method,
+ threshold=ecg_threshold,
+ verbose=False,
+ )
+ for idx in ecg_indices:
+ print(f" ICA{idx:03d}: ecg (score={ecg_scores[idx]:.2f})")
+ if not ecg_indices:
+ print(" No ECG components found.")
+ except Exception as e:
+ print(f" ECG detection failed: {e}")
+
+ # Detect EOG components
+ eog_indices = []
+ eog_scores = np.zeros(ica.n_components_)
+ eog_chs = mne.pick_types(raw_fit.info, eog=True)
+ if len(eog_chs) == 0:
+ print("No EOG channel found, skipping EOG detection.")
+ else:
+ print("Detecting EOG components...")
+ try:
+ eog_indices, eog_scores = ica.find_bads_eog(
+ raw_fit,
+ measure=eog_measure,
+ threshold=eog_threshold,
+ verbose=False,
+ )
+ # eog_scores can be a list of arrays if multiple EOG channels
+ if isinstance(eog_scores, list):
+ eog_scores = np.max(np.abs(eog_scores), axis=0)
+ for idx in eog_indices:
+ print(f" ICA{idx:03d}: eog (score={eog_scores[idx]:.2f})")
+ if not eog_indices:
+ print(" No EOG components found.")
+ except Exception as e:
+ print(f" EOG detection failed: {e}")
+
+ # Combine and exclude
+ exclude_idx = sorted(set(ecg_indices + eog_indices))
+
+ # Capture pre-ICA PSD
+ psd_before_ica = raw.compute_psd(fmax=45)
+
+ if len(exclude_idx) > 0:
+ print(f"Removing {len(exclude_idx)} artefact component(s)...")
+ ica.exclude = exclude_idx
+ ica.apply(raw)
+ else:
+ print("No artefact components found.")
+
+ # Build ic_labels dict (same structure as ica_label output)
+ labels = []
+ probs = []
+ for idx in range(ica.n_components_):
+ if idx in ecg_indices and idx in eog_indices:
+ labels.append("ecg+eog")
+ probs.append(max(abs(ecg_scores[idx]), abs(eog_scores[idx])))
+ elif idx in ecg_indices:
+ labels.append("ecg")
+ probs.append(abs(ecg_scores[idx]))
+ elif idx in eog_indices:
+ labels.append("eog")
+ probs.append(abs(eog_scores[idx]))
+ else:
+ labels.append("brain")
+ probs.append(0.0)
+
+ ic_labels = {"labels": labels, "y_pred_proba": np.array(probs)}
+
+ return raw, ica, ic_labels
+
+
+def plot_ica_components(
+ ica: Any,
+ ic_labels: dict,
+) -> Optional[plt.Figure]:
+ """Plot excluded ICA component topographies with labels.
+
+ Creates a composite figure showing only the excluded ICA components
+ with their classification labels and probabilities.
+
+ Parameters
+ ----------
+ ica : mne.preprocessing.ICA
+ Fitted ICA object.
+ ic_labels : dict
+ Dictionary with keys ``"labels"`` and ``"y_pred_proba"``.
+
+ Returns
+ -------
+ fig : matplotlib.figure.Figure or None
+ The composite figure, or None if no components were excluded.
+ """
+ exclude_idx = ica.exclude
+ if len(exclude_idx) == 0:
+ print("No ICA components excluded — nothing to plot.")
+ return None
+
+ labels = ic_labels["labels"]
+ probs = ic_labels["y_pred_proba"]
+ n_excluded = len(exclude_idx)
+
+ # Create composite figure
+ n_cols = min(n_excluded, 5)
+ n_rows = (n_excluded + n_cols - 1) // n_cols
+ fig, axes = plt.subplots(
+ n_rows,
+ n_cols,
+ figsize=(5 * n_cols, 5 * n_rows),
+ )
+ if n_rows == 1 and n_cols == 1:
+ axes = np.array([[axes]])
+ elif n_rows == 1:
+ axes = axes[np.newaxis, :]
+ elif n_cols == 1:
+ axes = axes[:, np.newaxis]
+
+ for i, idx in enumerate(exclude_idx):
+ row, col = divmod(i, n_cols)
+ ax = axes[row, col]
+
+ # Plot individual component topography
+ comp_figs = ica.plot_components(picks=[idx], show=False)
+ if isinstance(comp_figs, list):
+ comp_fig = comp_figs[0]
+ else:
+ comp_fig = comp_figs
+ comp_fig.canvas.draw()
+ buf = comp_fig.canvas.buffer_rgba()
+ img = np.asarray(buf)
+ plt.close(comp_fig)
+
+ ax.imshow(img)
+ ax.set_axis_off()
+
+ label = labels[idx]
+ prob = probs[idx]
+ ax.set_title(
+ f"ICA{idx:03d}: {label} ({prob:.2f})",
+ fontsize=16,
+ color="red",
+ fontweight="bold",
+ )
+
+ # Hide unused axes
+ for i in range(n_excluded, n_rows * n_cols):
+ row, col = divmod(i, n_cols)
+ axes[row, col].set_axis_off()
+
+ fig.suptitle(
+ f"Excluded ICA Components ({n_excluded})",
+ fontsize=20,
+ )
+ fig.tight_layout()
+ return fig
+
+
+# -------------------------------------------------------------------------
+# Headshape decimation
+# -------------------------------------------------------------------------
+
+
def decimate_headshape_points(
raw: mne.io.Raw,
decimate_amount: float = 0.01,
@@ -628,19 +1011,30 @@ def _grid_average_decimate(
return np.array([np.mean(voxel_dict[key], axis=0) for key in voxel_dict])
+# -------------------------------------------------------------------------
+# QC plots
+# -------------------------------------------------------------------------
+
+
def save_qc_plots(
raw: mne.io.Raw,
output_dir: Union[str, Path],
show: bool = False,
+ ica: Any = None,
+ ic_labels: Optional[dict] = None,
) -> None:
"""Save preprocessing QC plots and summary.
Saves the following files to output_dir:
- - 1_summary.json: preprocessing summary stats
- - 1_psd.png: sensor-level PSD
- - 1_sum_square.png: sum-square time series
- - 1_sum_square_exclude_bads.png: sum-square excluding bad segments/channels
- - 1_channel_stds.png: channel standard deviation distributions
+
+ - ``1_summary.json``: preprocessing summary stats
+ - ``1_psd.png``: sensor-level PSD
+ - ``1_sum_square.png``: sum-square time series
+ - ``1_sum_square_exclude_bads.png``: sum-square excluding bad
+ segments/channels
+ - ``1_channel_stds.png``: channel standard deviation distributions
+ - ``1_ica_components.png``: ICA component topographies (if ``ica``
+ and ``ic_labels`` are provided)
Parameters
----------
@@ -650,6 +1044,11 @@ def save_qc_plots(
Directory to save plots to.
show : bool, optional
Whether to display the plots interactively. Default is False.
+ ica : mne.preprocessing.ICA, optional
+ Fitted ICA object. If provided along with ``ic_labels``, saves
+ ICA component topography plot.
+ ic_labels : dict, optional
+ ICA label dictionary from ``ica_label``.
"""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
@@ -666,6 +1065,13 @@ def save_qc_plots(
"bad_channels": raw.info["bads"],
"n_bad_channels": len(raw.info["bads"]),
}
+ if ica is not None and ic_labels is not None:
+ summary["ica_n_components"] = ica.n_components_
+ summary["ica_n_excluded"] = len(ica.exclude)
+ summary["ica_excluded_labels"] = [
+ f"{ic_labels['labels'][i]} ({ic_labels['y_pred_proba'][i]:.2f})"
+ for i in ica.exclude
+ ]
with open(output_dir / "1_summary.json", "w") as f:
json.dump(summary, f, indent=2)
@@ -695,6 +1101,16 @@ def save_qc_plots(
if not show:
plt.close("all")
+ # ICA component topographies (excluded components only)
+ if ica is not None and ic_labels is not None:
+ fig = plot_ica_components(ica, ic_labels)
+ if fig is not None:
+ fig.savefig(
+ output_dir / "1_ica_components.png", dpi=150, bbox_inches="tight"
+ )
+ if not show:
+ plt.close("all")
+
def plot_sum_square_time_series(
raw: mne.io.Raw,
diff --git a/osl_dynamics/meeg/report.py b/osl_dynamics/meeg/report.py
index ba4b3722..77c012b9 100644
--- a/osl_dynamics/meeg/report.py
+++ b/osl_dynamics/meeg/report.py
@@ -11,33 +11,67 @@
STEPS = {
1: {
"name": "Preprocessing",
- "files": [
- "1_psd.png",
- "1_sum_square.png",
- "1_sum_square_exclude_bads.png",
- "1_channel_stds.png",
+ "subpanels": [
+ {
+ "name": "PSD",
+ "files": ["1_psd.png"],
+ },
+ {
+ "name": "Sum-Square",
+ "files": ["1_sum_square.png"],
+ },
+ {
+ "name": "Sum-Square (excl. bads)",
+ "files": ["1_sum_square_exclude_bads.png"],
+ },
+ {
+ "name": "Channel Stds",
+ "files": ["1_channel_stds.png"],
+ },
+ {
+ "name": "ICA Components",
+ "files": ["1_ica_components.png"],
+ },
],
- "large": ["1_psd.png"],
},
2: {
"name": "Surfaces",
- "files": [
- "2_inskull.png",
- "2_outskin.png",
- "2_outskull.png",
+ "subpanels": [
+ {
+ "name": "Inner Skull",
+ "files": ["2_inskull.png"],
+ },
+ {
+ "name": "Outer Skull",
+ "files": ["2_outskull.png"],
+ },
+ {
+ "name": "Outer Skin",
+ "files": ["2_outskin.png"],
+ },
+ {
+ "name": "Outer Skin + Nose",
+ "files": ["2_outskin_plus_nose.png"],
+ },
],
},
3: {
"name": "Coregistration",
- "files": ["3_coreg.png"],
- "large": ["3_coreg.png"],
+ "subpanels": [
+ {
+ "name": "Coregistration",
+ "files": ["3_coreg.png"],
+ },
+ ],
},
4: {
"name": "Source Recon & Parcellation",
- "files": [
- "4_psd_topo.png",
+ "subpanels": [
+ {
+ "name": "Parcellation PSD",
+ "files": ["4_psd_topo.png"],
+ },
],
- "large": ["4_psd_topo.png"],
},
}
@@ -83,8 +117,6 @@
background: #fff;
padding: 20px;
border-radius: 0 0 6px 6px;
- max-height: calc(100vh - 160px);
- overflow-y: auto;
}
.tab-content.active {
display: block;
@@ -93,7 +125,7 @@
display: flex;
align-items: center;
gap: 10px;
- margin-bottom: 20px;
+ margin-bottom: 10px;
padding: 10px;
background: #f0f0f0;
border-radius: 6px;
@@ -123,23 +155,61 @@
color: #888;
margin-left: auto;
}
+.subpanel-nav {
+ display: flex;
+ align-items: center;
+ gap: 8px;
+ margin-bottom: 16px;
+ padding: 8px 10px;
+ background: #e8f0fe;
+ border-radius: 6px;
+}
+.subpanel-nav button {
+ padding: 4px 12px;
+ border: 1px solid #b0c4de;
+ background: #fff;
+ cursor: pointer;
+ border-radius: 4px;
+ font-size: 16px;
+ line-height: 1;
+}
+.subpanel-nav button:hover {
+ background: #dce8f5;
+}
+.subpanel-nav .subpanel-label {
+ font-size: 14px;
+ font-weight: bold;
+ color: #333;
+}
+.subpanel-nav .subpanel-counter {
+ font-size: 12px;
+ color: #888;
+ margin-left: auto;
+}
+.subpanel-nav .hint {
+ font-size: 11px;
+ color: #aaa;
+}
.session-panel {
display: none;
}
.session-panel.active {
display: block;
}
-.session-panel img {
+.subpanel {
+ display: none;
+}
+.subpanel.active {
+ display: block;
+}
+.subpanel img {
max-width: 100%;
- max-height: 250px;
+ max-height: calc(80vh - 280px);
display: block;
margin: 4px auto;
border: 1px solid #eee;
}
-.session-panel img.large {
- max-height: 400px;
-}
-.session-panel iframe {
+.subpanel iframe {
width: 100%;
height: 350px;
border: 1px solid #ddd;
@@ -207,10 +277,15 @@
JS = """
var sessions = SESSION_LIST;
var currentStep = 1;
-var currentIdx = {}; // per-step session index
-
-// Initialise each step to session 0
-STEP_NUMS.forEach(function(s) { currentIdx[s] = 0; });
+var currentIdx = {}; // per-step session index
+var currentSubpanel = {}; // per-step subpanel index
+var subpanelCounts = SUBPANEL_COUNTS; // {step: count}
+
+// Initialise each step to session 0, subpanel 0
+STEP_NUMS.forEach(function(s) {
+ currentIdx[s] = 0;
+ currentSubpanel[s] = 0;
+});
function switchTab(step) {
currentStep = step;
@@ -226,19 +301,52 @@
if (idx >= sessions.length) idx = sessions.length - 1;
currentIdx[step] = idx;
- // Hide all session panels for this step
var panels = document.querySelectorAll('#tab-' + step + ' .session-panel');
panels.forEach(p => p.classList.remove('active'));
- // Show the selected one
var panel = document.getElementById('step-' + step + '-session-' + idx);
if (panel) panel.classList.add('active');
- // Update input and counter
var input = document.getElementById('input-' + step);
var counter = document.getElementById('counter-' + step);
input.value = sessions[idx];
counter.textContent = (idx + 1) + ' / ' + sessions.length;
+
+ showSubpanel(step, currentSubpanel[step]);
+}
+
+function showSubpanel(step, spIdx) {
+ var count = subpanelCounts[step] || 1;
+ if (spIdx < 0) spIdx = 0;
+ if (spIdx >= count) spIdx = count - 1;
+ currentSubpanel[step] = spIdx;
+
+ // Hide all subpanels in the current session panel
+ var sessionIdx = currentIdx[step];
+ var panel = document.getElementById('step-' + step + '-session-' + sessionIdx);
+ if (!panel) return;
+ var subs = panel.querySelectorAll('.subpanel');
+ subs.forEach(s => s.classList.remove('active'));
+ var target = panel.querySelector('.subpanel[data-sp-idx="' + spIdx + '"]');
+ if (target) target.classList.add('active');
+
+ // Update subpanel nav label and counter
+ var label = document.getElementById('sp-label-' + step);
+ var spCounter = document.getElementById('sp-counter-' + step);
+ if (label && target) {
+ label.textContent = target.getAttribute('data-sp-name') || '';
+ }
+ if (spCounter) {
+ spCounter.textContent = (spIdx + 1) + ' / ' + count;
+ }
+}
+
+function prevSubpanel(step) {
+ showSubpanel(step, currentSubpanel[step] - 1);
+}
+
+function nextSubpanel(step) {
+ showSubpanel(step, currentSubpanel[step] + 1);
}
function prevSession(step) {
@@ -258,7 +366,6 @@
return;
}
}
- // Partial match: find first session containing the input
for (var i = 0; i < sessions.length; i++) {
if (sessions[i].toLowerCase().indexOf(val) !== -1) {
showSession(step, i);
@@ -268,7 +375,6 @@
}
document.addEventListener('keydown', function(e) {
- // Ignore if user is typing in the input
if (document.activeElement.tagName === 'INPUT') {
if (e.key === 'Enter') {
e.preventDefault();
@@ -282,6 +388,12 @@
} else if (e.key === 'ArrowRight') {
e.preventDefault();
nextSession(currentStep);
+ } else if (e.key === 'ArrowUp') {
+ e.preventDefault();
+ prevSubpanel(currentStep);
+ } else if (e.key === 'ArrowDown') {
+ e.preventDefault();
+ nextSubpanel(currentStep);
}
});
"""
@@ -299,14 +411,26 @@ def _build_summary(session_dir: Path) -> str:
pct = s["bad_percent"]
n_bad_ch = s["n_bad_channels"]
bad_chs = ", ".join(s["bad_channels"]) if s["bad_channels"] else "none"
- return (
+ html = (
f''
f'Duration: {total:.1f}s | '
f'Bad segments: '
f'{bad:.1f}s ({pct:.1f}%) | '
f'Bad channels ({n_bad_ch}): {bad_chs}'
- f"
"
)
+ if "ica_n_excluded" in s:
+ n_exc = s["ica_n_excluded"]
+ n_tot = s["ica_n_components"]
+ ica_labels = ", ".join(s.get("ica_excluded_labels", []))
+ if not ica_labels:
+ ica_labels = "none"
+ html += (
+ f" | "
+ f'ICA excluded ({n_exc}/{n_tot}): '
+ f"{ica_labels}"
+ )
+ html += ""
+ return html
def _embed_png(filepath: Path, css_class: str = "") -> str:
@@ -331,22 +455,24 @@ def _build_step_tab(
session_ids: List[str],
) -> Tuple[str, int, int]:
"""Build the HTML content for a single step tab."""
- files = step_info["files"]
- large_files = step_info.get("large", [])
- row_files = step_info.get("row_files", [])
- all_filenames = files + row_files
+ subpanels = step_info["subpanels"]
+
+ # Collect all files across subpanels
+ all_files = []
+ for sp in subpanels:
+ all_files.extend(sp["files"])
# Count how many sessions have at least one file for this step
done = 0
for session_id in session_ids:
session_dir = plots_dir / session_id
- if any((session_dir / f).exists() for f in all_filenames):
+ if any((session_dir / f).exists() for f in all_files):
done += 1
total = len(session_ids)
parts = []
- # Session navigator
+ # Session navigator (left/right arrows)
parts.append(f'')
parts.append(f'◀ ')
parts.append(
@@ -356,12 +482,26 @@ def _build_step_tab(
f'placeholder="Type session ID...">'
)
parts.append(f'▶ ')
- parts.append(
- f'' f"1 / {total} "
- )
+ parts.append(f'1 / {total} ')
parts.append("
")
- # Session panels (one per session, only one visible at a time)
+ # Subpanel navigator (up/down arrows) — only if more than 1 subpanel
+ if len(subpanels) > 1:
+ parts.append(f'')
+ parts.append(f'▲ ')
+ parts.append(
+ f'{subpanels[0]["name"]} '
+ )
+ parts.append(f'▼ ')
+ parts.append(
+ f'1 / {len(subpanels)} '
+ )
+ parts.append(f'↑↓ to switch plots ')
+ parts.append("
")
+
+ # Session panels
for idx, session_id in enumerate(session_ids):
session_dir = plots_dir / session_id
active = " active" if idx == 0 else ""
@@ -374,32 +514,31 @@ def _build_step_tab(
if step_num == 1:
parts.append(_build_summary(session_dir))
- for filename in files:
- filepath = session_dir / filename
- parts.append(f'{filename}
')
- if filepath.exists():
- if filename.endswith(".png"):
- css_class = "large" if filename in large_files else ""
- parts.append(_embed_png(filepath, css_class=css_class))
- elif filename.endswith(".html"):
- parts.append(_embed_html(filepath))
+ # Build subpanels
+ for sp_idx, sp in enumerate(subpanels):
+ sp_active = " active" if sp_idx == 0 else ""
+ sp_name = sp["name"]
+ # Check if any file in this subpanel exists
+ has_files = any((session_dir / f).exists() for f in sp["files"])
+
+ parts.append(
+ f''
+ )
+
+ if has_files:
+ for filename in sp["files"]:
+ filepath = session_dir / filename
+ if filepath.exists():
+ if filename.endswith(".png"):
+ parts.append(_embed_png(filepath))
+ elif filename.endswith(".html"):
+ parts.append(_embed_html(filepath))
+ else:
+ parts.append('
Pending
')
else:
parts.append('
Pending
')
- # Render row files (e.g. power maps in a horizontal row)
- if row_files:
- row_labels = step_info.get("row_labels", [])
- parts.append('
')
- for i, filename in enumerate(row_files):
- filepath = session_dir / filename
- parts.append('
')
- if i < len(row_labels):
- parts.append(f'
{row_labels[i]}
')
- if filepath.exists():
- parts.append(_embed_png(filepath))
- else:
- parts.append('
Pending
')
- parts.append("
")
parts.append("
")
parts.append("