Motor execution classification

Name: motor execution
Category: brain-computer interfacing
Dataset: Srisrisawang2024Simultaneous (BNCI2025_001)
Objective: Multiclass classification
Split: Leave-subjects-out

Usage

neuralbench eeg motor_execution
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: Srisrisawang2024Simultaneous
    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: 3.0
  summary_columns: [code]
compute_class_weights: true
brain_model_output_size: &brain_model_output_size 16
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 execution classification task involves identifying different types of executed motor movements from EEG recordings. Here, we use the Srisrisawang2024 dataset [Srisrisawang2024], which contains 60-channel EEG from 20 healthy participants performing discrete reaching movements. The 16 classes encode the joint combination of movement direction (up, down, left, right), speed (slow, fast), and distance (near, far).

Additional Datasets

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

  • Ofner2017 – 15 subjects, 7 classes (executed upper limb movements: elbow, forearm, hand)

  • Schirrmeister2017 (HGD) – 14 subjects, 4 classes (left hand, right hand, both feet, rest)

To run with an alternate dataset:

neuralbench eeg motor_execution --dataset schirrmeister2017

References

[Srisrisawang2024]

Srisrisawang, Nitikorn, and Gernot R. Müller-Putz. “Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study.” Journal of Neural Engineering 21.6 (2024): 066042.