A comparison study between different social interaction in multi-user Brain-Computer Interface (BCI) gaming: competitive vs. collaborative

Putri, Finda Dwi (2020) A comparison study between different social interaction in multi-user Brain-Computer Interface (BCI) gaming: competitive vs. collaborative. PhD thesis, University of Glasgow.

Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available in this service.


The growing interest in BCI gaming has initiated the possibility of multi-user BCI games, which require the involvement of two or more players with integrated brain signals to a BCI application. Multi-user BCI is a complex process, with challenges surrounding the technical aspects and the behavioural aspects all should be carefully considered. Several studies have tested multi-user BCI games based on different types of control paradigms. However, most of the studies focused on the technical aspects of the game and the information related to the neurological changes caused by the BCI gaming interaction is still relatively limited. The aim of this thesis is to investigate the electrophysiological changes that occur in the brain, due to interactive multi-user BCI gaming, where the BCI paradigm used is based on the alpha band non-verbalised operant conditioning.

Forty able-bodied healthy participants were involved in the multi-user BCI gaming experiments that were divided into two main experiments i.e. competitive and collaborative gaming. The BCI game was presented as two bars on each side of the screen with a seesaw placed in between. The bars are controlled by the changes of relative alpha power (RA) of each player, recorded from Pz. When one bar is higher than the other, the seesaw will be tilted down towards the side with a higher bar. A pair of players were asked to control their assigned bar to achieve a different goal for each gaming interaction. In competitive gaming, the players were asked to compete by upregulating their RA (which consequently increases their bar) and the goal was to increase the bar 10% above their opponent’s bar for at least 1s to gain one score. In collaborative gaming, the players were asked to collaborate in balancing the seesaw (thus, balancing their bars) and the goal was to reach a similar bar level within the range of ±5% from each other for at least 0.5 s in order to score. Offline analyses were performed on the EEG data, including power spectral density, signal complexity, source localisation, and brain connectivity analyses. Brain connectivity analysis was performed by using Granger Causality (GC) and Directed Transfer Function (DTF). GC was performed to estimate both intra- and inter-brain connectivity in the time domain, and DTF was performed to estimate intra-brain connectivity in the frequency domain. Additionally, qualitative data analysis was performed via the NASA-TLX workload questionnaire.
The results revealed that different types of interaction produced a different kind of response in the brain activity of the players. These responses include the different fluctuation and the different spatial distribution of alpha power, the changing source localisation pattern, and the changes in intra- and inter-brain connectivity. It was found that the level of dominance between players also produced different brain activity responses, in both gaming interactions. Additionally, it was revealed that the level of posterior alpha power and intra-brain connectivity during resting state can be used to predict the gaming performance. Overall, the thesis has contributed to providing the information regarding the brain’s cortical activity changes during different kinds of multi-user BCI gaming interaction, which is expected to benefit the development of multi-user BCI games and for other methodological considerations in designing multi-user BCI application.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Brain-Computer Interface, BCI gaming, multi-user BCI, EEG, hyperscanning.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
T Technology > T Technology (General)
Colleges/Schools: College of Science and Engineering > School of Engineering > Biomedical Engineering
Supervisor's Name: Vuckovic, Dr. Aleksandra
Date of Award: 2020
Embargo Date: 30 September 2022
Depositing User: Ms. Finda Dwi Putri
Unique ID: glathesis:2020-81744
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 16 Nov 2020 17:41
Last Modified: 17 Nov 2020 11:23
URI: https://theses.gla.ac.uk/id/eprint/81744
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