Using Scalp Electrical Biosignals to Control an Object by Concentration and Relaxation Tasks: Design and Evaluation

TL;DR

This study investigates using scalp electrical biosignals, including EEG and facial muscle activity, to control a video game through mental relaxation and concentration, comparing simple and complex sensor setups.

Contribution

It introduces and evaluates a dual-design system for brain-computer interface control using minimal and extensive EEG sensors combined with machine learning.

Findings

100% success with 16-electrode design

70% success with single-electrode design

Muscular activity can distinguish relaxation from concentration

Abstract

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.