Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs.

Objective.While electroencephalography (EEG)-based brain-computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user's ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill.Approach.Using unsupervisedK-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user's movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metricstaskDistinctandrelativeTaskInconsistencyto assess, respectively, a user's ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks.Main results.Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between thetaskDistinctandrelativeTaskInconsistencymetrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy.Significance.Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.

Psychological interventions of virtual gamification within academic intrinsic motivation: A systematic review.

BACKGROUND: Students constantly seek ways to improve productivity within academia. With the advancement of technology in the recent decade, virtual implementations may provide additional support for student productivity, particularly during the COVID-19 pandemic with online learning. One of the virtual realms for motivation include gamification, which has potential as an effective tool to further bolster an individual's source of intrinsic motivation. METHODS: Qualitative and quantitative studies were extracted from APA PsycInfo, ProQuest, and IEEE for relevance to virtual gamification and intrinsic motivation. Studies were reviewed based on a pre-determined and piloted screening tool. Included studies were published between 1990 and 2020 in English within Asia, North America, or Europe. Only systematic reviews, randomized control trials (RCTs), meta-analysis, and grey literature were included. Study screening, extraction, and quality appraisals using the Mixed Methods Appraisal Tool (MMAT) were performed independently among two authors. Disagreements following reconciliation between two authors were settled by a third author. Heterogeneity in study designs, outcomes, and measurements precluded meta and statistical analyses; thus, a qualitative analysis of studies was provided. RESULTS: Based on the appraised articles, gamification improves intrinsic motivation through badges, social interactions, points, and leaderboards. Experimental studies also displayed a correlation between learning behaviour. CONCLUSION: The data exhibited an increase in intrinsic motivation due to gamification features, which can be integrated within a virtual context to enhance motivation with potential for application towards online learning settings.