Flavor-induced sensory satisfaction is critical for food acceptance and market success. However, traditional sensory evaluation methods, relying heavily on subjective assessments, often fail to accurately reflect real-time, objective neural processing underlying complex multisensory flavor experiences. This limitation highlights the need for innovative methods that objectively quantify how flavors are perceived and integrated within the brain. In this review, we first examine the neural pathways underlying flavor perception, focusing on how gustatory, olfactory, and oral somatosensory inputs interact with reward and hedonic networks to form integrated flavor experience. Building on this foundation, we then outline the latest strategies for developing flavor-oriented brain-computer interface (flavor-BCI), summarizing key features of various neuroimaging techniques and associated technical implementation workflows. Finally, we assess emerging applications of flavor-BCI in sensory assessment and consumer decision-making and identify opportunities and challenges for future food design and product development. Flavor perception begins with parallel encoding of chemical stimuli in the primary gustatory and olfactory cortices and in trigeminal pathways. These signals are subsequently integrated in higher order regions, forming a distributed neural network across cortical, limbic, and subcortical structures that support flavor recognition, hedonic appraisal, and motivated eating. Flavor-BCI systems record neural activity from these regions using electrophysiology or neuroimaging and apply advanced algorithms to decode neural representations, translating them into objective sensory outputs. Relative to traditional evaluations, this approach enables real-time, precise quantification of flavor experience. Flavor-BCI thus offers promising avenues for intelligent sensory evaluation and novel human-machine interactions.
Yang et al. (2026) studied this question.