Mismatch negativity (MMN) classification

Name: mismatch negativity

Category: auditory neuroscience

Dataset: Kappenman2020Mmn (ErpCore2021_MMN)

Objective: Binary classification

Split: Leave-subjects-out

Usage

neuralbench eeg mismatch_negativity

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: Kappenman2021ErpMmn
    split:
      name: SklearnSplit
      split_by: subject
      valid_split_ratio: 0.2
      test_split_ratio: 0.2
      valid_random_state: 33
      test_random_state: 33
  neuro.baseline: [0.0, 0.2]
  target:
    =replace=: true
    name: LabelEncoder
    event_types: Stimulus
    event_field: description
    return_one_hot: true
    aggregation: trigger
  trigger_event_type: Stimulus
  start: 0.0
  duration: 1.0
  summary_columns: [description]
compute_class_weights: true
brain_model_output_size: &brain_model_output_size 2
trainer_config.monitor: val/bal_acc
trainer_config.mode: max
trainer_config.patience: 10
loss:
  name: CrossEntropyLoss
  kwargs:
    label_smoothing: 0.0
metrics: !!python/object/apply:neuralbench.defaults.metrics.get_classification_metric_configs
  - *brain_model_output_size

Description

The mismatch negativity (MMN) classification task involves classifying EEG epochs as responses to standard vs. deviant auditory stimuli. The MMN is an automatic brain response to unexpected changes in an otherwise regular auditory sequence, and is widely used as a marker of pre-attentive auditory change detection. We use the Kappenman2020Mmn dataset [Kappenman2020Mmn], part of the ERP CORE (Compendium of Open Resources and Experiments), which contains EEG data from 40 subjects passively listening to tone sequences with occasional deviant tones.

References

[Kappenman2020Mmn]

Kappenman, E. S., et al. “ERP CORE: An open resource for human event-related potential research.” NeuroImage 225 (2021): 117465.