We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report dat...We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks.To quantify the potential of fNIRS for mental workload discrimination,we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin(HbO2)and deoxyhemoglobin(HbR)concentration changes associated with the working memory tasks.We have found classification success rates in the range of 44%-72%,which are significantly higher than the corresponding chance level(for random data)of 19.1%.This work shows the potential of fNIRS for mental workload classification,especially when more parameters(rather than just the amplitude of concentration changes used here)and more sophisticated classification algorithms(rather than the simple 3-nearest neighbor algorithm used here)are considered and optimized for this application.展开更多
基金supported by NSF Award IIS-0713506,and NIH Grant DA021817。
文摘We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks.To quantify the potential of fNIRS for mental workload discrimination,we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin(HbO2)and deoxyhemoglobin(HbR)concentration changes associated with the working memory tasks.We have found classification success rates in the range of 44%-72%,which are significantly higher than the corresponding chance level(for random data)of 19.1%.This work shows the potential of fNIRS for mental workload classification,especially when more parameters(rather than just the amplitude of concentration changes used here)and more sophisticated classification algorithms(rather than the simple 3-nearest neighbor algorithm used here)are considered and optimized for this application.
