Abstract
Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), 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 based on response speed and accuracy trade-off, 20 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 classifier with linear kernel to differentiate these two groups. Receiver operating characteristics 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 prediction model with radial basis function kernel and the root-mean-square error of prediction 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 by short-time training, we found that 16 in 20 subjects’ SRT scores were 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 spectral entropy could be a promising indicator to predict individual’s WM changes by training and further provide a novel application about WM for brain–computer interfaces.
Highlights
Working memory (WM) was originally defined as a cognitive mechanism responsible for the temporal maintain and manipulation of new and stored memory information (Baddeley, 2012)
For Signed Residual Time (SRT) scores, the significant main effects of memory load were separately observed in intra-sessions, indicating that different memory-load tasks affected the behavioral performance on SRT scores regardless of before training and after training
For the change rates of SRT scores from session 1 to session 2, there were non-significant main effects of hand (F = 3.636, P = 0.072, η2p = 0.161) and memory loads (F = 0.028, P = 0.973, η2p = 0.002), indicating that the change rates reduced by training were not affected by both hand and memory-load effects
Summary
Working memory (WM) was originally defined as a cognitive mechanism responsible for the temporal maintain and manipulation of new and stored memory information (Baddeley, 2012). The original model of WM proposed by Baddeley included three subcomponents: the central. An additional component, namely the episodic buffer, was added to WM. This component could take information from the other three components and from long-term memory, from which a single episodic representation was created and temporarily preserved in the buffer (Baddeley, 2000). One person was able to memorize more information and manipulate the information more effectively than others (Baddeley, 2003). There is no doubt that if someone is confused, his or her memory ability will decline
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