Review of EEG Based Taste Classification Methods

This review covers the new field of using technology like EEG (brain-wave reading) and BCI (Brain Computer Interface) to objectively classify taste, offering a more reliable alternative to traditional methods. It analyzes studies from 2015 to 2024 detailing the steps signal acquisition preprocessing feature extraction and classification algorithms. A key finding from 2015 is the high accuracy (over 98\%) in distinguishing sweet and sour tastes using specific features. The review also highlights recent advances like deep learning and channel optimization that maintain high accuracy while reducing computational power. It examines the pros and cons of different research designs including electrode placement and classification results. Ultimately the article discusses the potential of this technology as a reliable objective tool for the food industry quality control clinical applications and advanced assistive technologies. By far the most pressing problems, namely, inter, individual variability and the limited number of taste categories studied so far, lead to very precise directions for future work. Among these are the imperative to broaden research to cover a wider spectrum of tastes, to develop portable EEG systems and to improve cross subject generalization capabilities. This paper is a comprehensive guide for students and researchers alike, helping them to comprehend the complex functioning of such systems, and their potential impact on society.