Adapting the p300 brain-computer interface technology to assess condition of anorexia nervosa patients

Ganin IP1, Kosichenko EA1, Sokolov AV2,3, Ioannisyanc OM2, Arefev IM2, Basova AYa2,3, Kaplan AYa1
About authors

1 Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia

2 Scientific-practical Children's and Adolescents Mental Health Center n.a. G. Sukhareva, Moscow, Russia

3 Pirogov Russian National Research Medical University, Moscow, Russia

Correspondence should be addressed: Ilya P. Ganin
Leninskie gory 1, bl. 12, ap. 246, Moscow, 119234; ur.liam@ninagpi

About paper

Author contribution: all authors participated in the experiment planning; Ganin IP — immediate research activities, data analysis and interpretation, literature analysis, manuscript authoring; Kosichenko EA — immediate research activities, literature analysis, data analysis; Sokolov AV — immediate research activities, data interpretation, text editing; Ioannisyanc OM — diagnosing and selection of patients for the study; Arefev IM — support of experiments, data interpretation; Basova AYa — data interpretation, text editing; Kaplan AYa — data interpretation.

Received: 2018-10-08 Accepted: 2019-03-27 Published online: 2019-04-10

Brain-computer interface based on the P300 wave (P300 BCI) allows activating a given command according to the electroencephalogram (EEG) response to a predetermined relevant stimulus. The same algorithm enables detecting a subjectively important item (i.e., one triggering emotional response) in an environment even without actively drawing attention to it. Such systems allow assessing the personal significance of certain information, which can be used in the diagnostics of disorders of emotional perception or value system, e.g., eating disorders. This study aimed to investigate the EEG responses of anorexia nervosa patients (diagnosis F50.0, n = 12, age 11–16 years) to the stimuli with different perceived emotional significance, as well as to validate application of P300 BCI to detect the focus of attention to subjectively important stimuli. The inclusion criteria were: diagnosed anorexia nervosa (diagnosis F50.0); active rehabilitation. We registered the EEG while presenting images with different content to the patients. The event-related potentials (ERP) were detected and analyzed with the help of MATLAB 7.1 (MathWorks; USA). Statistica 7.0 software (StatSoft; USA) was used for statistical analysis of the data. We have discovered that in passive viewing paradigm, images of body parts of emaciated people among other images caused ERP with higher amplitude than images of food. Moreover, the accuracy of detection was higher for images of body parts: 89% against 59%, respectively. Thus, we have proven the validity of applying P300 BCI to detect covert emotional foci of attention and added to the existing knowledge about the mechanisms of development of anorexia nervosa.

Keywords: brain-computer interface (BCI), electroencephalogram (EEG), event-related potentials (ERP), visual attention, P300 wave, eating disorders, anorexia nervosa