A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching

J Neural Eng. 2015 Jun;12(3):036004. doi: 10.1088/1741-2560/12/3/036004. Epub 2015 Apr 2.

Abstract

Objective: In order to increase the number of states classified by a brain-computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching.

Approach: The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs).

Main results: In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG-fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% ± 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature.

Significance: Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain Mapping / methods
  • Brain-Computer Interfaces*
  • Electroencephalography / methods*
  • Female
  • Hand Strength / physiology*
  • Humans
  • Imagination / physiology*
  • Male
  • Motor Cortex / physiology*
  • Multimodal Imaging / methods
  • Psychomotor Performance / physiology
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Spectroscopy, Near-Infrared / methods*
  • Stress, Mechanical
  • Systems Integration