Motor imagery classification

Name: motor imagery
Category: brain-computer interfacing
Dataset: Stieger2021Continuous
Objective: Multiclass classification
Split: Leave-subjects-out

Usage

neuralbench eeg motor_imagery
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: Stieger2021Continuous
    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: Stimulus
    event_field: code
    return_one_hot: true
    aggregation: trigger
  trigger_event_type: Stimulus
  start: 0.0
  duration: 4.0
  summary_columns: [code]
compute_class_weights: true
brain_model_output_size: &brain_model_output_size 4
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 motor imagery classification task involves identifying different types of imagined motor movements from EEG recordings. Here, we use the Stieger2021 dataset [Stieger2021], which contains 64-channel EEG from 62 healthy participants who used motor imagery to control a cursor with continuous online visual feedback. The task has the following four classes:

  • Right hand

  • Left hand

  • Both hands

  • Rest

Additional Datasets

The following additional datasets from MOABB can also be used with this task:

  • Barachant2012 (AlexMI) – 8 subjects, 3 classes

  • Cho2017 – 52 subjects, 2 classes

  • Dornhege2004 (BNCI2003_004) – 5 subjects, 2 classes

  • Dreyer2023 – 87 subjects, 2 classes

  • Faller2012 (BNCI2015_001) – 12 subjects, 2 classes

  • GrosseWentrup2009 – 10 subjects, 2 classes

  • Leeb2007 (BNCI2014_004) – 9 subjects, 2 classes

  • Lee2019Mi – 54 subjects, 2 classes

  • Liu2024Imagery – 50 subjects (stroke patients), 2 classes

  • Schalk2004Bci (EEGMIDB) – 109 subjects, 4 classes (left fist, right fist, both fists, both feet)

  • Scherer2012 (BNCI2014_002) – 14 subjects, 2 classes

  • Schwarz2020 (BNCI2020_001) – 45 subjects, 3 classes

  • Shin2017A – 29 subjects, 2 classes

  • Tangermann2012 (BNCI2014_001) – 9 subjects, 4 classes

  • Wei2022A (Beetl2021_A) – 3 subjects, 4 classes

  • Wei2022B (Beetl2021_B) – 2 subjects, 4 classes

  • Zhou2016 – 4 subjects, 3 classes

To run with an alternate dataset:

neuralbench eeg motor_imagery --dataset schalk2004bci

References

[Stieger2021]

Stieger, James R., Stephen A. Engel, and Bin He. “Continuous sensorimotor rhythm based brain computer interface learning in a large population.” Scientific Data 8.1 (2021): 98.