Evaluation of an Adaptive Game that Uses EEG Measures Validated during the Design Process as Inputs to a Biocybernetic Loop

Abstract

Biocybernetic adaptation is a form of physiological computing whereby real-time data streaming from the brain and body is used by a negative control loop to adapt the user interface. This article describes the development of an adaptive game system that is designed to maximize player engagement by utilizing changes in real-time electroencephalography (EEG) to adjust the level of game demand. The research consists of four main stages: (1) the development of a conceptual framework upon which to model the interaction between person and system; (2) the validation of the psychophysiological inference underpinning the loop; (3) the construction of a working prototype; and (4) an evaluation of the adaptive game. Two studies are reported. The first demonstrates the sensitivity of EEG power in the (frontal) theta and (parietal) alpha bands to changing levels of game demand. These variables were then reformulated within the working biocybernetic control loop designed to maximize player engagement. The second study evaluated the performance of an adaptive game of Tetris with respect to system behavior and user experience. Important issues for the design and evaluation of closed-loop interfaces are discussed.

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

“…very low (Hopstaken et al, 2015); similarly, models of behavioral self-regulation (Carver and Scheier, 2000) argue that task goals can be adjusted downward (i.e., lower levels of performance are tolerated as acceptable) or even abandoned if goal attainment is perceived to be impossible. There is evidence that increased likelihood of failure is associated with deactivation of the PFC (Durantin et al, 2014;Ewing et al, 2016;Fairclough et al, 2019), for operational performance where failure can often jeopardize the safety of oneself and others, increased likelihood of failure can also provoke strong emotional responses that are associated with stress and cognitive interference (Sarason et al, 1990), which can function as distractors from task activity in their own right (Dolcos and McCarthy, 2006;Qin et al, 2009;Gärtner et al, 2014).…”

“…MPFC (Harrivel et al, 2013;Durantin et al, 2015) DLPFC (Harrivel et al, 2013) DLPFC (Durantin et al, 2014;Fairclough et al, 2019) Left PFC (Kalia et al, 2018) occipital lobe (Kojima and Suzuki, 2010) EEG α power over occipital sites (Gouraud et al, 2018) (α and (β power (auditory stimuli) (Braboszcz and Delorme, 2011) (θ power (auditory stimuli) (Braboszcz and Delorme, 2011) N1 (Kam et al, 2011) N4 (O'Connell et al, 2009) P1 (Kam et al, 2011) P2 (Braboszcz and Delorme, 2011) P3 (Schooler et al, 2011) frontal θ power (Gärtner et al, 2014) P3 (Dierolf et al, 2017) frontal (θ power and parietal (α power (Ewing et al, 2016;Fairclough and Ewing, 2017) Event Related Coherence between midfrontal and right-frontal electrodes (Carrillo-De-La-Pena and García-Larrea, 2007) (α band power (Mathewson et al, 2009) P1 (Pourtois et al, 2006;Mathewson et al, 2009) P2 (Mathewson et al, 2009) N170 (Pourtois et al, 2006) P3 (Pourtois et al, 2006;Mathewson et al, 2009) N1 (Callan et al, 2018;Dehais et al, 2019a,b) P3 (Puschmann et al, 2013;Scannella et al, 2013;Giraudet et al, 2015b;Dehais et al, 2019a,b) (α power in IFG (Dehais et al, 2...…”

“…In the future, it is assumed that aircraft designers will implement adaptive automation technology that takes over from the pilots by either inhibiting their inputs on the flight deck or performing automated evasive actions (e.g., automatic pullup) to prevent from perseveration. A complementary approach is to modulate task difficulty to maintain the task challenging but achievable while preventing the occurrence of task withdrawal (Ewing et al, 2016) or mind wandering (Freeman et al, 2004;Ewing et al, 2016). The online modulation of the tasks does not necessarily reduce the difficulty of the task.…”

Serious Games: The Future of Psychotherapy? Proposal of an Integrative Model

“…In contrast, for the “passive” approach, the real-time data streaming is used to optimize the user interface [7]. A passive BCI does not require a learning period, but it does improve the interaction between the subject and the game by adapting the content, structure, theme, and gameplay of an SG according to the variables measured.…”

“…The ultimate goal is to increase the motivation of the participant and to improve the gaming experience. For example, the degree of difficulty of the game can be based on the EEG signal related to the level of attention of the subject [7]. …”