In this paper we explore the use of electrical biosignals measured on scalp
and corresponding to mental relaxation and concentration tasks in order to
control an object in a video game. To evaluate the requirements of such a
system in terms of sensors and signal processing we compare two designs. The
first one uses only one scalp electroencephalographic (EEG) electrode and the
power in the alpha frequency band. The second one uses sixteen scalp EEG
electrodes and machine learning methods. The role of muscular activity is also
evaluated using five electrodes positioned on the face and the neck. Results
show that the first design enabled 70% of the participants to successfully
control the game, whereas 100% of the participants managed to do it with the
second design based on machine learning. Subjective questionnaires confirm
these results: users globally felt to have control in both designs, with an
increased feeling of control in the second one. Offline analysis of face and
neck muscle activity shows that this activity could also be used to distinguish
between relaxation and concentration tasks. Results suggest that the
combination of muscular and brain activity could improve performance of this
kind of system. They also suggest that muscular activity has probably been
recorded by EEG electrodes.Comment: International Conference of the IEEE EMBS (2011
