Studying the generalisability of cognitive load measured with EEG

Predicting cognitive load with EEG using Riemannian geometry-based features

Objective. We show that electroencephalography (EEG)-based cognitive load (CL) prediction using Riemannian geometry features outperforms existing models. The performance is estimated using Riemannian Procrustes Analysis (RPA) with a test set of subjects unseen during training.Approach. Performance is evaluated by using the Minimum Distance to Riemannian Mean model trained on CL classification. The baseline performance is established using spatial covariance matrices of the signal as features. Various novel features are explored and analyzed in depth, including spatial covariance and correlation matrices computed on the EEG signal and its first-order derivative. Furthermore, each RPA step effect on the performance is investigated, and the generalization performance of RPA is compared against a few different generalization methods.Main results. Performances are greatly improved by using the spatial covariance matrix of the first-order derivative of the signal as features. Furthermore, this work highlights both the importance and efficiency of RPA for CL prediction: it achieves good generalizability with little amounts of calibration data and largely outperforms all the comparison methods.Significance. CL prediction using RPA for generalizability across subjects is an approach worth exploring further, especially for real-world applications where calibration time is limited. Furthermore, the feature exploration uncovers new, promising features that can be used and further experimented within any Riemannian geometry setting.

Rehabilitation in digital environments – biophysiologically motivated gamification

Nowadays, the process of cognitive or motor rehabilitation is mostly implemented in a traditional form. Paper-pencil cognitive exercises or physical exercises with instruments still dominate over digital environments. However, they require constant supervision by professionals, whose availability is relatively decreasing in an ageing society. Lack of supervision, in turn, results in a loss of motivation to exercise or, at the very least, ineffective, sometimes incorrect, exercise. In addition, traditional rehabilitation mechanisms are often repetitive and tedious. Sometimes a lack of supervision or routine results in a failure to adapt the challenges to the user's current needs. Digital environments and modern technology have much to offer in this regard. One aspect is gamification mechanisms, which work well in video games and allow players to be engaged for hours in challenges of modulated difficulty. Another aspect is the rapidly developing biosensors and tracking systems that allow the user's activity and biophysiological parameters to be monitored in real time. However, the combination of the benefits of technology and gamification stimulus mechanisms must be done in strict accordance with the user's capabilities in order to make the challenges constructive rather than destructive for the user's body and mental conditions. Ongoing monitoring of effort and mental workload and their synchronization with fatigue in the digital environment, supported by motivational gamification mechanisms, form the foundation of the correct and controlled exercises and rehabilitation.

Metaphorical or Straightforward? Comparing the Effectiveness of Different Types of Social Media Advertising

The existing studies have analyzed the advertising effects of metaphorical advertisements and straightforward adverts in traditional advertising media. However, their advertising effects on social media are still unclear. To address this issue, this study uses eye-tracking and questionnaires to examine two types of social media tourism advertising—metaphorical and straightforward in posts with both high and low popularity. This within-subject (n = 55) experiment was designed to examine the effects of social media tourism advertising types on visual attention and tourism intention and to identify the moderating role of post popularity based on the elaboration likelihood model (ELM). We found that advertising types had no significant effect on visual attention, but metaphorical advertisements increased tourism intention compared with straightforward adverts. Furthermore, we found that the level of post popularity moderated the effect of advertising types on visual attention. Specifically, metaphorical adverts in highly popular posts attracted more visual attention in the advertising text area and in the whole advert. Straightforward adverts in posts with low popularity attracted more visual attention in the advertising text area. This research advances the current literature by exploring the effects of social media tourism advertising types and has implications for managers deciding on strategies for social media tourism marketing.