Exploration of Brain-Computer Interaction for Supporting Children’s Attention Training: A Multimodal Design Based on Attention Network and Gamification Design

Abstract

Recent developments in brain–computer interface (BCI) technology have shown great potential in terms of estimating users’ mental state and supporting children’s attention training. However, existing training tasks are relatively simple and lack a reliable task-generation process. Moreover, the training experience has not been deeply studied, and the empirical validation of the training effect is still insufficient. This study thusly proposed a BCI training system for children’s attention improvement. In particular, to achieve a systematic training process, the attention network was referred to generate the training games for alerting, orienting and executive attentions, and to improve the training experience and adherence, the gamification design theory was introduced to derive attractive training tasks. A preliminary experiment was conducted to set and modify the training parameters. Subsequently, a series of contrasting user experiments were organized to examine the impact of BCI training. To test the training effect of the proposed system, a hypothesis-testing approach was adopted. The results revealed that the proposed BCI gamification attention training system can significantly improve the participants’ attention behaviors and concentration ability. Moreover, an immersive, inspiring and smooth training process can be created, and a pleasant user experience can be achieved. Generally, this work is promising in terms of providing a valuable reference for related practices, especially for how to generate BCI attention training tasks using attention networks and how to improve training adherence by integrating multimodal gamification elements.

State-of-the-Art on Brain-Computer Interface Technology

“…collected from EEG recordings can also be analyzed to assess cognitive abilities such as attention span or memory recall speed. Figure 3 illustrates that there are four distinct "rhythms" of the human brain, which can be categorized based on their frequency: δ delta (0.1-4 Hz), θ theta (4-7.5 Hz), α alpha (7.5-12 Hz), β beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and γ gamma (over 30 Hz). It is important to note that these rhythms differ in amplitude as well as frequency.…”

“…Additionally, these devices are also being used to correct and prevent a variety of diseases. On the other hand, non-invasive neural interfaces have been gaining traction in the gaming industry [16][17][18][19]. As more types of neurogadgets become available, it is possible that this sector will experience a revolution.…”

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

“…Detecting and understanding such patterns is paramount in studies pertaining to sleep disorders or cognitive processes during rest. These signals can be used to generate an understanding of the correlation of the cognitive processes of attention [6,7], memory [8], language [9], and emotion [10][11][12][13][14]. The delineation between raw EEG data and the derivative insights obtained from Quantitative EEG (QEEG) is an important construct.…”