Advances in Brain-Computer Interfaces for Taste Perception: Current Insights and Future Directions

Human taste perception is a complex multisensory process that integrates chemical, emotional, and cognitive responses within the brain. Traditional methods for evaluating taste rely on subjective reporting, which limits reproducibility and accuracy. Brain-Computer Interface (BCI) technology provides an objective solution by decoding neural activity associated with taste perception using non-invasive techniques such as EEG and fNIRS. The research contribution aims to deliver an extensive overview of the latest advancements in BCI-oriented taste research, emphasizing various applications, methodological frameworks, and potential future pathways that connect the domains of neuroscience and sensory technology. This review examines the use of EEG and fNIRS modalities for signal acquisition, preprocessing, feature extraction, and classification across 36 studies conducted between 2020 and 2025. These works employ both traditional algorithms and deep learning models, including SVM, CNNs, and Transformer-based frameworks, to decode neural signatures of basic tastes and multisensory interactions. Results show that BCIs have successfully identified distinct brain responses for sweet, sour, salty, bitter, and umami stimuli. They have also been applied in multisensory integration, hedonic evaluation, consumer behavior analysis, clinical diagnosis of taste disorders, and affective monitoring. However, challenges remain in signal noise, dataset standardization, and model interpretability. In conclusion, BCIs represent a promising and interdisciplinary approach for objectively studying and enhancing human taste perception through the integration of neuroscience, engineering, and artificial intelligence.