Artifact classification

Name: artifact
Category: Others
Dataset: Hamid2020 (TUAR)
Objective: Multilabel classification
Split: Leave-subjects-out

Usage

neuralbench eeg artifact
Show config.yaml
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

data:
  study:
    source:
      name: Hamid2020Tuar
    split:
      name: SklearnSplit
      split_by: subject
      valid_split_ratio: 0.2
      test_split_ratio: 0.2
      valid_random_state: 33
      test_random_state: 33
  target:
    =replace=: true
    name: LabelEncoder
    event_types: Artifact
    event_field: state
    return_one_hot: true
    aggregation: sum
    allow_missing: true
  trigger_event_type: Eeg
  start: -1.0
  duration: 3.0
  stride: 1.0
  stride_drop_incomplete: false
  summary_columns: [state]
compute_class_weights: true
brain_model_output_size: &brain_model_output_size 5
trainer_config.monitor: val/f1_score_macro
trainer_config.mode: max
loss:
  =replace=: true
  name: BCEWithLogitsLoss
metrics: !!python/object/apply:neuralbench.defaults.metrics.get_classification_metric_configs
  - *brain_model_output_size
  - multilabel

Description

The goal of the artifact classification task is to predict whether 3-second EEG sliding windows contain one or mode of the following five artifact types [Hamid2020]:

  • eye movement (“eyem”)

  • chewing (“chew”)

  • shivers (“shiv”)

  • muscle artifact (“musc”)

  • electrode-related artifacts, i.e. electrode pop, electrostatic, or lead artifact (“elec”)

Of note, the seizure annotations available in this dataset are not used in this task.

Dataset Notes

  • Following the formulation of the clinical event classification task, we adopt a multilabel classification approach to better model overlapping events. See clinical event classification.

  • For this, we break down the combined artifact events (“eyem_musc”, “musc_elec”, “eyem_elec”, etc.) into two separate events with the same start and duration.

References

[Hamid2020]

Hamid, Ahmed, et al. “The Temple University Artifact Corpus: An annotated corpus of EEG artifacts.” 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2020.