Steady-state visually evoked potential (SSVEP) classification

Name: ssvep
Category: brain-computer interfacing
Dataset: Wang2017Benchmark (Wang2016)
Objective: Multiclass classification
Split: Leave-subjects-out

Usage

neuralbench eeg ssvep
Show config.yaml
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# 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: Wang2017Benchmark
    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]
brain_model_output_size: &brain_model_output_size 40
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 visual steady-state evoked potential (SSVEP) classification task involves identifying different visual stimulus frequencies from EEG recordings. SSVEPs are brain responses elicited by visual stimuli flickering at specific frequencies. In this task, we use the Wang2017 benchmark dataset [Wang2017], which contains 64-channel EEG from 34 healthy participants fixating on targets in a 40-class SSVEP speller using joint frequency-phase modulation (8-15.8 Hz with 0.2 Hz spacing).

Additional Datasets

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

  • Kalunga2016 – 12 subjects, 4 classes

  • Lee2019Ssvep – 54 subjects, 4 classes (5.45, 6.67, 8.57 and 12 Hz at four positions)

  • Nakanishi2015 – 9 subjects, 12 classes

  • Oikonomou2016A (MAMEM1) – 11 subjects, 5 classes

  • Oikonomou2016B (MAMEM2) – 11 subjects, 5 classes

  • Oikonomou2016C (MAMEM3) – 11 subjects, 5 classes

To run with an alternate dataset:

neuralbench eeg ssvep --datasets lee2019ssvep

References

[Wang2017]

Wang, Yijun, Xiaogang Chen, Xiaorong Gao, and Shangkai Gao. “A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 25.10 (2017): 1746-1752.