Prefrontal haemodynamics based classification of inter‐individual working memory difference

Abstract

This Letter illustrates prefrontal haemodynamics as a neurovascular basis of inter‐personal working memory differences. A functional near‐infrared spectroscopy with sampling frequency ∼2 Hz is used to record the blood oxyhaemoglobin and deoxyhaemoglobin signals from 19 subjects engaged in working memory task of encoding and retrieval of ten symbol‐meaning association learning. The individual difference in working memory performance is classified by supervised learning‐based linear discriminant analysis and ensemble classifiers. Prior to the classification approach, individual performance is labelled as high, moderate and low on the basis of the performance index. The spontaneous haemodynamic activity and task‐evoked responses are marked as background and foreground signals, respectively, which are scaled by means of stream‐independent and stream‐dependent models. The classifiers' performance shows that the stream‐dependent model‐based feature construction‐classification improves classification accuracy to a major extent compared to the stream‐independent model and no gain model. To understand the neurovascular basis of the inter‐individual performance difference, diffused voxel plots are constructed. The voxel plots showed that concurrent activation of orbitofrontal and dorsolateral prefrontal cortex could have a possible association with persons' higher working memory performance.

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

“…In conclusion, existing studies suggest that the combination of multiple physiological signals can obtain better performance in mental workload detection compared with signal physiological signal. Nevertheless, there are still some limitation of the reported studies in the following aspects: the multiple physiological signals acquisition configuration was relatively complex [11,23] , only three or even less different levels of mental workload was considered [25][26][27], and the recognition accuracy was not ideal enough.…”

Working memory load recognition with deep learning time series classification

“…De et al obtained the mean, skewness, and kurtosis of blood oxyhemoglobin and deoxyhemoglobin signals of PFC, mean blood oxygenation and total blood volume. After feature selection, LDA could classify the three types of WMLs of healthy subjects with an accuracy of 88.9% [ 16 ].…”

Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications

“…In conclusion, existing studies suggest that the combination of multiple physiological signals can obtain better performance in mental workload detection compared with signal physiological signal. Nevertheless, there are still some limitations of the reported studies in the following aspects: the multiple physiological signals acquisition configuration was based on an excessive number of channels (i.e., 64 channels in the 10-20 system) [11,23], only three or even less different levels of mental workload were considered [25][26][27], and the recognition accuracy was not sufficiently high (i.e., accuracy value higher than 70%).…”