Key result
Knowledge-based FSVR outperforms other models predicting working memory from EEG with an MSE of 0.6.
Why the study?
Working memory process serves as an indicator of brain disease and cognitive impairment, but evaluation methods based on EEG analysis require development.
EEG signals analyzed with fuzzy support vector regression can predict working memory ability with a mean square error of 0.6.
Answers are grounded in this paper and cite their sources.
EEG working memory models are hypothesis-generating; validation needed before any clinical role in cognitive assessment.
One's working memory process is a fundamental cognitive activity which often serves as an indicator of brain disease and cognitive impairment. In this research, the approach to evaluate working memory ability by means of electroencephalography (EEG) analysis was proposed. The result shows that the EEG signals of subjects share some characteristics when performing working memory tasks. Through correlation analysis, a working memory model describes the changes in EEG signals within alpha, beta and gamma waves, which shows an inverse tendency compared to Zen meditation. The working memory ability of subjects can be predicted using multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering and knowledge-based fuzzy support vector regression (FSVR), which reaches the mean square error of 0.6 in our collected data. The latter, designed based on the working memory model, achieves the best performance. The research provides the insight of the working memory process from the EEG aspect to become an example of cognitive function analysis and prediction.
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Lo et al. (2023) studied Working memory ability. Knowledge-based fuzzy support vector regression (FSVR) with EEG analysis vs. Multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering was evaluated on Mean square error of working memory ability prediction. Knowledge-based fuzzy support vector regression (FSVR) predicted working memory ability from EEG signals with a mean square error of 0.6, achieving the best performance among tested models.
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