2011/11/08 by Laurent George, George, Laurent, Fabien Lotte +5
Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Neural and Behavioral Psychology Studies #Other Computer Science (cs.OH)
paper · pdf · doi:10.48550/arxiv.1111.5285
openalex publication_date 2011/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we explore the use of electrical biosignals measured on scalp\nand corresponding to mental relaxation and concentration tasks in order to\ncontrol an object in a video game. To evaluate the requirements of such a\nsystem in terms of sensors and signal processing we compare two designs. The\nfirst one uses only one scalp electroencephalographic (EEG) electrode and the\npower in the alpha frequency band. The second one uses sixteen scalp EEG\nelectrodes and machine learning methods. The role of muscular activity is also\nevaluated using five electrodes positioned on the face and the neck. Results\nshow that the first design enabled 70% of the participants to successfully\ncontrol the game, whereas 100% of the participants managed to do it with the\nsecond design based on machine learning. Subjective questionnaires confirm\nthese results: users globally felt to have control in both designs, with an\nincreased feeling of control in the second one. Offline analysis of face and\nneck muscle activity shows that this activity could also be used to distinguish\nbetween relaxation and concentration tasks. Results suggest that the\ncombination of muscular and brain activity could improve performance of this\nkind of system. They also suggest that muscular activity has probably been\nrecorded by EEG electrodes.\n