DEVELOPMENT... { "data_id": "43883", "name": "mne-sample-meg-auditory", "exact_name": "mne-sample-meg-auditory", "version": 1, "version_label": null, "description": "MEG data from auditory stimulation experiment using 305 sensors.\nThe design matrix\/forward operator is `data[:, :7498]`.\nThe measurements for left stimulation are `data[:, 7498:7583]`.\nThe measurements for right stimulation are `data[:, 7583:]`.\n\nThe data was generated with the following script:\n```\nimport mne\nimport numpy as np\nfrom mne.datasets import sample\nfrom mne.inverse_sparse.mxne_inverse import _prepare_gain\n# this script used MNE 1.0.2\n\ncondition = \"Right Auditory\"\ndata_path = sample.data_path() + '\/MEG\/sample'\nloose = 0\ndepth = 0.8\n\nfwd_fname = data_path + '\/sample_audvis-meg-eeg-oct-6-fwd.fif'\nave_fname = data_path + '\/sample_audvis-ave.fif'\ncov_fname = data_path + '\/sample_audvis-shrunk-cov.fif'\n\n# Read noise covariance matrix\nnoise_cov = mne.read_cov(cov_fname)\n# Handling forward solution\nforward = mne.read_forward_solution(fwd_fname)\ntargets = {}\n\nfor condition in [\"Left Auditory\", \"Right Auditory\"]:\n evoked = mne.read_evokeds(\n ave_fname, condition=condition, baseline=(None, 0))\n evoked.crop(tmin=0.04, tmax=0.18)\n evoked = evoked.pick_types(eeg=False, meg=True)\n\n # Handle depth weighting and whitening (here is no weights)\n forward, gain, gain_info, whitener, _, _ = _prepare_gain(\n forward, evoked.info, noise_cov, pca=False, depth=depth,\n loose=loose, weights=None, weights_min=None, rank=None)\n\n # Select channels of interest\n sel = [evoked.ch_names.index(name) for name in gain_info['ch_names']]\n M = evoked.data[sel]\n\n # Whiten data\n M = whitener @ M\n targets[condition] = M\n\n# gain is independent of condition:\ndata = np.hstack([gain, targets[\"Left Auditory\"], targets[\"Right Auditory\"]])\n```", "format": "arff", "uploader": "Robert Ruiz", "uploader_id": 30896, "visibility": "public", "creator": "MNE contributors", "contributor": "\"Mathurin Massias\"", "date": "2022-05-23 15:29:04", "update_comment": null, "last_update": "2022-05-23 15:29:04", "licence": "BSD (from MNE)", "status": "active", "error_message": null, "url": "https:\/\/old.openml.org\/data\/download\/22102789\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "mne-sample-meg-auditory", "MEG data from auditory stimulation experiment using 305 sensors. The design matrix\/forward operator is `data[:, :7498]`. The measurements for left stimulation are `data[:, 7498:7583]`. The measurements for right stimulation are `data[:, 7583:]`. The data was generated with the following script: ``` import mne import numpy as np from mne.datasets import sample from mne.inverse_sparse.mxne_inverse import _prepare_gain # this script used MNE 1.0.2 condition = \"Right Auditory\" data_path = sample.dat " ], "weight": 5 }, "qualities": { "NumberOfInstances": 305, "NumberOfFeatures": 7498, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 7498, "NumberOfSymbolicFeatures": 0, "PercentageOfSymbolicFeatures": 0, "AutoCorrelation": null, "PercentageOfNumericFeatures": 100, "PercentageOfMissingValues": 0, "PercentageOfInstancesWithMissingValues": 0, "PercentageOfBinaryFeatures": 0, "NumberOfBinaryFeatures": 0, "MinorityClassSize": null, "MinorityClassPercentage": null, "MajorityClassSize": null, "MajorityClassPercentage": null, "Dimensionality": 24.58360655737705 }, "tags": [], "features": [ { "name": "X6948", "index": "6948", "type": "numeric", "distinct": "305", 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