Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task - Tian, Yin; Zhang, Huiling; Xu, Wei; Zhang, Haiyong; Yang, Li; Zheng, Shuxing; Shi, Yupan (Frontiers in Human Neuroscience, 2017)
Spectral entropy, which is generated by using the power distribution of the Fourier-transformed EEG using the Shannon entropy, was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e. before and after training. According to Signed Residual Time (SRT) scores that based on response speed and accuracy track-off, twenty subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks. We found that spectral entropy derived from the retention period of WM on channel FC4 exhibited a high correlation with SRT scores. To this end, spectral entropy was used in support vector machine (SVM) classifier with linear kennel to differentiate these two groups. Receiver operating characteristics (ROC) analysis and leave-one out cross-validation (LOOCV) demonstrated that the averaged classification accuracy (CA) was 90.0% and 92.5% for intra-session and inter-session, respectively, indicating that spectral entropy could be used to distinguish these two different WM performance groups successfully. Furthermore, the support vector regression (SVR) prediction model with RBF kernel and the root-mean-square error of prediction (RMSEP) revealed that spectral entropy could be utilized to predict SRT scores on individual WM performance. After testing the changes in SRT scores and spectral entropy for each subject before and after training, we found that 16 in 20 subjects’ SRT scores was clearly promoted after training and 15 in 20 subjects’ SRT scores showed consistent changes with spectral entropy before and after training. The findings revealed that the spectral entropy could be a promising indicator to predict individual’s WM changes by training and further provide a novel application about working memory for Brain-Computer Interfaces (BCIs).