A Wearable Brain--Computer Interface for Mitigating Car Sickness via Attention Shifting

Abstract

Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyses electroencephalography (EEG) data via a convolutional neural network to assess the users mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the users attention from physiological discomfort towards the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposed the first large-scale validated, nonpharmacological and wearable intervention method and system for car sickness, with the potential to transform the travel experiences of hundreds of millions of people suffering from car sickness, which also represents a new application of BCI technology.