Classification of Movement Direction From Electroencephalogram During Working Memory Time

When humans perform cognitive tasks, it is necessary to hold information temporarily. This is done by a brain function called working memory (WM). Since WM is active during the whole time range from stimulus presentation to task execution, onset detection is unnecessary, in contrast to readiness potentials for movement. Therefore, it is possible to realize application in a brain-computer interface (BCI) in various tasks without onset detection by performing single-trial classification of electroencephalogram (EEG) signals during WM. The purpose of this research was to examine the possibility of WM application to BCI. We classified the EEG signals during WM-time that required the retention of movement direction information when performing a right arm movement in order of two instructed sequential target directions using a 3-layer neural network (3-NN). In classification based on the signal immediately after presentation of 1st target (WM1), the classification accuracy was significantly higher (62%) than chance level (50%). In addition, the accuracy was higher when providing the phase of the fast Fourier transform to the classifier as information rather than the spectrum. However, it could not be classified by WM requiring the retention of information regarding two tasks (WM2). In summary, these results suggest a possibility that single trial classification of EEG during the first WM (WM1) is possible, and that the WM information is included mainly in the phase. Future studies should aim at improving the classification accuracy by using other feature quantities and classifiers, and to examine classification of EEG in tasks other than arm movement. Furthermore, the relationship between WM and EEG distribution also needs to be investigated.