Temporal representation of arm force direction using fNIRS signals

We investigated the possibility of creating a temporal representation of brain activity from fNIRS signals. In an experiment, subjects performed isometric arm movements in four directions, and fNIRS signals were measured over the primary motor area in the left hemisphere of their brain. We estimated the direction of the arm force from the fNIRS signals by using two classifiers: sparse linear regression (SLR) and support vector machine(SVM). Classification accuracy was approximately 70% with SLR. The temporal distribution of the features selected with SLR was the same as those selected with SVM. The results indicated that the fNIRS signals possibly included information about arm force direction in 4-6 [s] after stimulus onset and offset.