Published papers

高齢化が進行する日本では、医療費の増加等の社会問題を解決するため心身の健康を長期的に保つことが必要である。そして、脳機能は心身の健康を長期的に保つために重要な要素の一つである。住宅内温熱環境と脳機能の関連を検討するための調査が2016年の冬季に実施された。その結果、居間と脱衣所の室間温度差が大きい住宅に住む者は、温度差が小さい住宅に住む者と比較し脳神経線維の質が低い傾向が示された。

The electroencephalogram (EEG) is an important technique that allows people to study brain signals. Classifying whether a person is concentrating or not is almost impossible with our naked eye. Thus, a system that could accurately distinguish these two categories is highly valued. Previously, the study to determine a mental state of concentration and non-concentration is based on the Fourier Transform. In this study, we employed Hilbert Huang Transform (HHT) to extract important features for this mental classification task. HHT consists of two step: (1) Using EMD and (2) Hilbert Transform. The EMD will decompose the signal into a collection of Intrinsic Mode Functions (IMF). Then Hilbert Transform is employed to obtain the IMF to compute the power spectrum to extract important features based on different subbands i.e. gamma, beta, alpha, theta and delta. An Extreme Learning Machine (ELM) is then used as a classifier to distinguish the two mental states. Four different sets of investigations were performed using the existing FFT and three investigated the HHT framework including single IMF, two and three IMFs. The results showed that the HHT with single IMF offers slight improvement compared to the existing FFT.