Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application

In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features — mean, slope, variance, peak, skewness and kurtosis — are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic versus rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic versus rest for a two-class BCI.

Abstract licence: CC BY

1. 7.

   Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface · 2014

2. 30.

   fNIRS-based brain-computer interfaces: a review · 2015

3. 34.

   The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy · 2012

37 more references, whose wording the paper’s licence keeps private.

• Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces · 2018

• Measuring Mental Workload with EEG+fNIRS · 2017

• Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review · 2017

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