A Study Of Human Ascending-descending Task Based On FNIRS

In order to restore the self-care ability of people with amputation, technology ofcontrolling artificial limbs by brain movement signal was mostly concerned recently.However, most artificial limbs people used require a long training term before they can beused, and the latency cannot be removed.In order to improve the performance of artificiallimbs, the brain computer interface (BCI) technology draws more attention recently.However, most studies conducted so far focused on movements with outside triggers orthat limited to a small part of body while most of the movements people make in real lifeare generated spontaneously. Functional Near Infrared Spectroscopy (fNIRS) is a newbrain imaging technology. It has a good advantage in time and spatial resolution as well asportability, which makes it extremely suitable in human spontaneous massive movementstudy. The main terms of this article are listed below:1) In this paper, ascending and descending a slope experiment in spontaneousmovement mode was designed. By applying functional near-infrared spectroscopy(fNIRS) technology, hemoglobin information of20-subjects was recorded andused to identify motion intention.2) A one-way ANOVA analysis was performed on hemoglobin information inmotor-related regions (primary motor cortex, prefrontal motor cortex,supplementary motor area, pre-supplementary motor area and dorsolateralprefrontal cortex), According to three different tasks (stop, movement begin andmovement switch), the hemoglobin concentration was compared betweenstop-begin tasks and begin-switch tasks. The channels with significant difference in different motion statuses in hemoglobin concentration were considered asactive channels.3) Based on the data in these channels, a Bp neural network was built to identify theintention of starting one movement task. The slop of the channels andconcentration difference of channel9was calculated as the input of NNW. It turnsout that this motion intention could be recognized around0.5s after the start ofone movement task with an accuracy of78%.4) A3-layers wavelet transformation with Db4as mother wavelet was conducted toextract statistical features of hemoglobin signals. Then a Fisher linear discriminantanalysis was performed to discriminate the extracted wavelet coefficients, andfurther to identify the motion intentions of ascending and descending slop.This article suggested a new way discriminating human spontaneous ascending anddescending movement modes based on cortical hemoglobin concentration, established aBp neural network to identify movement intention, and brought a new method todiscriminate two movement modes by Fisher classification algorithm.