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 closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs mindfulness meditation with a wearable headband, the BCI collects and analyses electroencephalography (EEG) data using a convolutional neural network to assess the user’s mindfulness state and provide real-time audio-visual feedback. This approach might efficiently redirect the users attention from physiological discomfort towards BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the mindfulness BCI was rigorously evaluated in two real world experiments: short and long car rides, with a large cohort of over 100 participants susceptible to car sickness. Remarkably, over 84% of participants rated the mindfulness BCI intervention as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed pre-frontal beta relative power as a neurobiological signature of car sickness, which provided mechanistic insight into the efficacy of the mindfulness BCI combining attention shift and sensory conflict theory for car sickness. This study proposed the first nonphar-macological, wearable and effective car sickness intervention method and system with potential to transform the travel experience of hundreds of millions suffering from car sickness, also representing a new application of BCI technology.
Competing Interest Statement
The authors have declared no competing interest.
