neuralset.extractors.neuro.EmgExtractor

class neuralset.extractors.neuro.EmgExtractor(*, event_types: Literal['Emg'] = 'Emg', aggregation: Literal['single', 'sum', 'mean', 'first', 'middle', 'last', 'cat', 'stack', 'trigger'] = 'single', allow_missing: bool = False, frequency: Literal['native'] | float = 'native', offset: float = 0.0, baseline: tuple[float, float] | None = None, picks: Annotated[tuple[str, ...], MinLen(min_length=1)] = ('emg',), apply_proj: bool = False, filter: tuple[float | None, float | None] | None = None, apply_hilbert: bool = False, notch_filter: float | list[float] | None = None, drop_bads: bool = False, mne_cpus: int = -1, infra: MapInfra = MapInfra(folder=None, cluster=None, logs='{folder}/logs/{user}/%j', job_name=None, timeout_min=120, nodes=1, tasks_per_node=1, cpus_per_task=10, gpus_per_node=None, mem_gb=None, max_pickle_size_gb=None, slurm_constraint=None, slurm_partition=None, slurm_account=None, slurm_qos=None, slurm_use_srun=False, slurm_additional_parameters=None, conda_env=None, workdir=None, permissions=511, version='1', keep_in_ram=True, max_jobs=128, min_samples_per_job=1, forbid_single_item_computation=False, mode='cached'), scaler: None | Literal['RobustScaler', 'StandardScaler'] = None, scale_factor: float | None = None, clamp: float | None = None, fill_non_finite: float | None = None, bipolar_ref: tuple[list[str], list[str]] | None = None, channel_order: Literal['unique', 'original'] = 'unique', allow_maxshield: bool = False)

EMG feature extractor.

Parameters:

picks (default = ("emg",)) – pick “emg” channels by default.