Machine Learning-based EEG Applications and Markets
Weiqing Gu, Bohan Yang, Ryan Chang

TL;DR
This paper provides a comprehensive survey of EEG applications driven by machine learning, analyzing the current market ecosystem, research trends, limitations, and future opportunities across six key application areas.
Contribution
It offers a detailed comparison between research developments and market applications in EEG, highlighting current limitations and future research directions in the context of machine learning.
Findings
Growing EEG market driven by machine learning applications.
Increased availability of EEG data enhances research and market growth.
Future research will lead to more robust EEG devices and applications.
Abstract
This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of improving neurology for patient care through knowledge discovery and machine learning data science algorithm development. This effort leads to various kinds of EEG developments and currently forms a new EEG market. This paper attempts to do a comprehensive survey on the EEG market and covers the six significant applications of EEG, including diagnosis/screening, drug development, neuromarketing, daily health, metaverse, and age/disability assistance. The highlight of this survey is on the compare and contrast between the research field and the business market. Our survey points out the current limitations of EEG and indicates the future direction of…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces
