Decoding different working memory states during an operation span task from prefrontal fNIRS signals

Ting Chen; Cui Zhao; Xingyu Pan; Junda Qu; Jing Wei; Chunlin Li; Ying Liang; Xu Zhang

doi:10.1364/BOE.426731

Decoding different working memory states during an operation span task from prefrontal fNIRS signals

Biomed Opt Express. 2021 May 18;12(6):3495-3511. doi: 10.1364/BOE.426731. eCollection 2021 Jun 1.

Authors

Ting Chen^{1

2}, Cui Zhao^{1

2}, Xingyu Pan^{1

2}, Junda Qu^{1

2}, Jing Wei^{1

2}, Chunlin Li^{1

2}, Ying Liang^{1

2

3}, Xu Zhang^{1

2

4}

Affiliations

¹ School of Biomedical Engineering, Capital Medical University, Beijing, China.
² Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
³ yingliang@ccmu.edu.cn.
⁴ bme_zhangxu@163.com.

Abstract

We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.