Hiroyasu Tomoyuki, Fukuhara Michihiro, Yokouchi Hisatake, Miki Mitsunori
Department of Life and Medical Sciences, Doshisha University, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1470-3. doi: 10.1109/EMBC.2012.6346218.
Functional near-infrared spectroscopy (fNIRS) is often used as a diagnostic method for mental illness, because the average patterns of changes in cerebral blood flow vary between such patients and healthy subjects. In addition, indoor environments and psychology alter the average patterns of changes in cerebral blood flow. These observations suggest that it may be possible to classify healthy subjects into different groups according to certain characteristics. The accuracy of fNIRS should be improved once it becomes possible to automatically determine the key factors affecting fNIRS data. The present study was performed first to determine whether there are differences in fNIRS data when the test subjects are classified into groups based on the scores related to task performances and questionnaires, and to determine whether there are differences in score when the test subjects are classified into groups based on fNIRS data. Differences were observed in fNIRS data between groups when the subjects were classified based on incorrect answers on the questionnaire and their degree of fatigue. In addition, there were differences in score between groups when the subjects were classified according to fNIRS data. These results suggested that subjects can be classified into groups automatically based on scores related to both task performance and fNIRS data.
功能近红外光谱技术（fNIRS）常被用作精神疾病的诊断方法，因为此类患者与健康受试者之间脑血流变化的平均模式存在差异。此外，室内环境和心理状态会改变脑血流变化的平均模式。这些观察结果表明，有可能根据某些特征将健康受试者分为不同组。一旦能够自动确定影响fNIRS数据的关键因素，fNIRS的准确性就应得到提高。本研究首先进行，以确定当根据与任务表现和问卷相关的分数将测试对象分组时，fNIRS数据是否存在差异，以及当根据fNIRS数据将测试对象分组时，分数是否存在差异。当根据问卷上的错误答案及其疲劳程度对受试者进行分组时，各小组之间的fNIRS数据存在差异。此外，当根据fNIRS数据对受试者进行分组时，各小组之间的分数也存在差异。这些结果表明，可以根据与任务表现和fNIRS数据相关的分数自动将受试者分为不同组。
