N170 face perception classification

Name: n170

Category: visual neuroscience

Dataset: Kappenman2020N170 (ErpCore2021_N170)

Objective: Binary classification

Split: Leave-subjects-out

Usage

neuralbench eeg n170

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: Kappenman2021ErpN170
    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
loss:
  name: CrossEntropyLoss
  kwargs:
    label_smoothing: 0.1
metrics: !!python/object/apply:neuralbench.defaults.metrics.get_classification_metric_configs
  - *brain_model_output_size

Description

The N170 classification task involves distinguishing brain responses to faces vs. non-face stimuli from EEG recordings. The N170 is a face-selective visual ERP component peaking around 170 ms after stimulus onset over occipito-temporal electrodes, reflecting early stages of face perception. We use the Kappenman2020N170 dataset [Kappenman2020N170], part of the ERP CORE (Compendium of Open Resources and Experiments), which contains EEG data from 40 subjects viewing face and non-face images.

References

[Kappenman2020N170]

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