Derive AcquisitionMetadata.n_channels from channel_types instead of storing it

docs/source/whats_new.rst

@@ -27,7 +27,7 @@ Enhancements
 
 API changes
 ~~~~~~~~~~~
-- None yet.
+- :attr:`moabb.datasets.metadata.AcquisitionMetadata.n_channels` is derived from ``channel_types`` instead of stored beside it. The two could disagree, and for 34 of the 147 catalogued datasets they did -- the field was being used with two meanings, some counting EEG electrodes only and others every recorded channel. It now consistently means the total, which is what each dataset's own ``sensors`` list and ``test_n_channels_matches_raw_data`` (which counts every non-``stim`` channel) already assumed: :class:`moabb.datasets.Dreyer2023A`, for instance, reported 27 against its own 32 sensors and now reports 32. Passing ``n_channels=`` to the constructor is no longer accepted (by `Bruno Aristimunha`_).
 
 Requirements
 ~~~~~~~~~~~~

moabb/datasets/Lee2019.py

@@ -325,7 +325,6 @@ class Lee2019_MI(Lee2019):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=1000.0,
-            n_channels=62,
             channel_types={"eeg": 62, "emg": 4},
             montage="standard_1005",
             hardware="BrainAmp",
@@ -610,7 +609,6 @@ class Lee2019_ERP(Lee2019):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=1000.0,
-            n_channels=62,
             channel_types={"eeg": 62, "emg": 4},
             montage="standard_1005",
             hardware="BrainAmp",
@@ -858,7 +856,6 @@ class Lee2019_SSVEP(Lee2019):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=1000.0,
-            n_channels=62,
             channel_types={"eeg": 62, "emg": 4},
             montage="standard_1005",
             hardware="BrainAmp",

moabb/datasets/Weibo2014.py

@@ -120,7 +120,6 @@ class Weibo2014(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=60,
             channel_types={"eeg": 60, "eog": 2, "misc": 2},
             montage="standard_1005",
             hardware="Neuroscan SynAmps2",

moabb/datasets/Zhou2016.py

@@ -72,7 +72,6 @@ class Zhou2016(BaseBIDSDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=14,
             channel_types={"eeg": 14},
             montage="standard_1020",
             sensor_type="EEG",

moabb/datasets/aguilera_rodriguez2025.py

@@ -154,7 +154,6 @@ class AguileraRodriguez2025(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=500.0,
-            n_channels=24,
             channel_types={"eeg": 24},
             hardware="mBrainTrain Smarting",
             reference="FCz",

moabb/datasets/alex_mi.py

@@ -55,7 +55,6 @@ class AlexMI(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=16,
             channel_types={"eeg": 16},
             reference="earlobe",
             software="Matlab/Simulink",

moabb/datasets/alphawaves.py

@@ -89,7 +89,6 @@ class Rodrigues2017(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=16,
             channel_types={"eeg": 16},
             montage="standard_1020",
             hardware="g.tec g.USBamp",

moabb/datasets/bbci_eeg_fnirs.py

@@ -337,7 +337,6 @@ class Shin2017A(BaseShin2017):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=30,
             channel_types={"eeg": 30, "eog": 2},
             montage="10-5",
             hardware="BrainAmp",
@@ -665,7 +664,6 @@ class Shin2017B(BaseShin2017):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=30,
             channel_types={"eeg": 30, "eog": 2},
             montage="10-5",
             hardware="BrainAmp",

moabb/datasets/bcicomp2020_upper_limb.py

@@ -195,7 +195,6 @@ class BCIComp2020UpperLimb(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=60,
             channel_types={"eeg": 60},
             montage="standard_1005",
             hardware="BrainAmp (BrainProducts GmbH)",

moabb/datasets/bcicomp2020_walking_erp.py

@@ -209,7 +209,6 @@ class BCIComp2020WalkingERP(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=_SFREQ,
-            n_channels=len(_CH_NAMES),
             channel_types={
                 "eeg": len(_SCALP_CHANNELS) + len(_EAR_CHANNELS),
                 "eog": len(_EOG_CHANNELS),

moabb/datasets/beetl.py

@@ -88,7 +88,6 @@ class Beetl2021_A(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=500.0,
-            n_channels=63,
             channel_types={"eeg": 63},
             sensors=[
                 "Fp1",
@@ -583,7 +582,6 @@ class Beetl2021_B(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=32,
             channel_types={"eeg": 32},
             sensors=[
                 "Fp1",

moabb/datasets/bnci/bnci_2003.py

@@ -172,7 +172,6 @@ class BNCI2003_004(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=100.0,
-            n_channels=118,
             channel_types={"eeg": 118},
             hardware="BrainAmp",
             reference=None,

moabb/datasets/bnci/bnci_2014.py

@@ -326,7 +326,6 @@ class BNCI2014_001(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=25,
             channel_types={"eeg": 22, "eog": 3},
             montage="custom",
             hardware="BrainAmp MR plus",
@@ -607,7 +606,6 @@ class for validation.  Participants had the task of performing sustained (5
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=15,
             channel_types={"eeg": 15},
             montage="Laplacian",
             hardware="g.USBamp",
@@ -794,7 +792,6 @@ class BNCI2014_004(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=3,
             channel_types={"eeg": 3, "eog": 3},
             montage="standard_1020",
             hardware="g.tec",
@@ -1034,7 +1031,6 @@ class BNCI2014_008(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=256.0,
-            n_channels=8,
             channel_types={"eeg": 8},
             montage="10-10",
             hardware="g.MOBILAB",
@@ -1216,7 +1212,6 @@ class BNCI2014_009(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=256.0,
-            n_channels=16,
             channel_types={"eeg": 16},
             montage="10-10",
             hardware="g.USBamp",

moabb/datasets/bnci/bnci_2015.py

@@ -484,7 +484,6 @@ class BNCI2015_001(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=13,
             channel_types={"eeg": 13},
             montage="10-20",
             hardware="g.tec",
@@ -643,7 +642,6 @@ class BNCI2015_003(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=256.0,
-            n_channels=8,
             channel_types={"eeg": 8},
             montage="standard_1005",
             hardware="BrainAmp",
@@ -873,7 +871,6 @@ class BNCI2015_004(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=256.0,
-            n_channels=30,
             channel_types={"eeg": 30},
             montage="10-20",
             hardware="g.tec",
@@ -1100,7 +1097,6 @@ class BNCI2015_006(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=64,
             channel_types={"eeg": 64},
             montage="10-10",
             hardware="Brain Products",
@@ -1469,7 +1465,6 @@ class BNCI2015_007(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=100.0,
-            n_channels=63,
             channel_types={"eeg": 63},
             montage="10-10",
             hardware="BrainAmp EEG amplifier",
@@ -1770,7 +1765,6 @@ class BNCI2015_008(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=63,
             channel_types={"eeg": 63},
             montage="10-10",
             hardware="Brain Products actiCAP",
@@ -2061,7 +2055,6 @@ class BNCI2015_009(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=60,
             channel_types={"eeg": 60, "eog": 2},
             montage="10-20",
             hardware="Brain Products 128-channel amplifier",
@@ -2251,7 +2244,6 @@ class BNCI2015_010(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=63,
             channel_types={"eeg": 63},
             montage="10-20",
             hardware="BrainAmp amplifiers",
@@ -2500,7 +2492,6 @@ class BNCI2015_012(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=63,
             channel_types={"eeg": 63},
             montage="10-20",
             hardware="Brain Products",
@@ -2786,7 +2777,6 @@ class BNCI2015_013(MNEBNCI):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=64,
             channel_types={"eeg": 64},
             montage="standard_1020",
             hardware="Biosemi ActiveTwo",

moabb/datasets/bnci/bnci_2016_002.py

@@ -317,7 +317,6 @@ class BNCI2016_002(BNCIBaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=59,
             channel_types={"eeg": 59, "emg": 1, "eog": 2, "misc": 7},
             montage="extended 10-20",
             hardware="BrainAmp",

moabb/datasets/bnci/bnci_2019.py

@@ -88,7 +88,6 @@ class BNCI2019_001(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=256.0,
-            n_channels=61,
             channel_types={"eeg": 61, "eog": 3},
             montage="10-5",
             hardware="g.tec",

moabb/datasets/bnci/bnci_2020.py

@@ -255,7 +255,6 @@ class BNCI2020_001(BNCIBaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=256.0,
-            n_channels=58,
             channel_types={"eeg": 58, "eog": 6},
             montage="5% grid system",
             hardware="g.tec USBamp/g.tec Ladybird",
@@ -826,7 +825,6 @@ class BNCI2020_002(BNCIBaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=250.0,
-            n_channels=30,
             channel_types={"eeg": 30, "eog": 2},
             montage="extended 10-20",
             hardware="BrainAmp DC Amplifier",

moabb/datasets/bnci/bnci_2022_001.py

@@ -436,7 +436,6 @@ class BNCI2022_001(BNCIBaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=256.0,
-            n_channels=64,
             channel_types={"eeg": 64, "eog": 3},
             montage="10-10",
             hardware="Biosemi ActiveTwo",

moabb/datasets/bnci/bnci_2024_001.py

@@ -298,7 +298,6 @@ class BNCI2024_001(BNCIBaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=500.0,
-            n_channels=60,
             channel_types={"eeg": 60, "eog": 4},
             montage="eogl1 eogl2 eogl3 eogr1 af7 af3 afz af4 af8 f7 f5 f3 f1 fz f2 f4 f6 f8 ft7 fc5 fc3 fc1 fcz fc2 fc4 fc6 ft8 t7 c5 c3 c1 cz c2 c4 c6 t8 tp7 cp5 cp3 cp1 cpz cp2 cp4 cp6 tp8 p7 p5 p3 p1 pz p2 p4 p6 p8 ppo1h ppo2h po7 po3 poz po4 po8 o1 oz o2",
             hardware="BrainVision",

moabb/datasets/bnci/bnci_2025.py

@@ -296,7 +296,6 @@ class BNCI2025_001(BNCIBaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=500.0,
-            n_channels=71,
             channel_types={"eeg": 60, "eog": 4, "misc": 7},
             montage="standard_1005",
             sensor_type="EEG",
@@ -1075,7 +1074,6 @@ class BNCI2025_002(BNCIBaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=200.0,
-            n_channels=60,
             channel_types={"eeg": 60, "eog": 4},
             montage="standard_1005",
             sensor_type="EEG",

moabb/datasets/braininvaders.py

@@ -466,7 +466,6 @@ class BI2012(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=128.0,
-            n_channels=16,
             channel_types={"eeg": 16},
             montage="standard_1020",
             hardware="NeXus-32 (MindMedia/TMSi)",
@@ -708,7 +707,6 @@ class BI2013a(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=16,
             channel_types={"eeg": 16},
             montage="standard_1020",
             hardware="g.USBamp (g.tec, Schiedlberg, Austria)",
@@ -942,7 +940,6 @@ class BI2014a(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=16,
             channel_types={"eeg": 16},
             montage="standard_1020",
             hardware="g.USBamp (g.tec, Schiedlberg, Austria)",
@@ -1144,7 +1141,6 @@ class BI2014b(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=32,
             channel_types={"eeg": 32},
             montage="standard_1020",
             hardware="g.USBamp (g.tec, Schiedlberg, Austria)",
@@ -1357,7 +1353,6 @@ class BI2015a(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=32,
             channel_types={"eeg": 32},
             montage="standard_1020",
             hardware="g.USBamp (g.tec, Schiedlberg, Austria)",
@@ -1558,7 +1553,6 @@ class BI2015b(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=32,
             channel_types={"eeg": 32},
             montage="standard_1020",
             hardware="g.USBamp (g.tec, Schiedlberg, Austria)",
@@ -1770,7 +1764,6 @@ class Cattan2019_VR(BaseDataset):
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=512.0,
-            n_channels=16,
             channel_types={"eeg": 16},
             montage="standard_1020",
             hardware="g.USBamp (g.tec, Schiedlberg, Austria)",

moabb/datasets/brandl2020.py

@@ -198,7 +198,6 @@ class 2 = right_hand. For the calibration run, codes are 11 (left)
     METADATA = DatasetMetadata(
         acquisition=AcquisitionMetadata(
             sampling_rate=1000.0,
-            n_channels=63,
             channel_types={"eeg": 63},
             hardware="2x BrainAmp (Brain Products)",
             reference="nose",