Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence(AI).If the AI understands the mental state behind a user’s decision,it can learn more appropriat...Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence(AI).If the AI understands the mental state behind a user’s decision,it can learn more appropriate decisions even in unclear situations.We introduce the Brain-AI Closed-Loop System(BACLoS),a wireless interaction platform that enables human brain wave analysis and transfers results to AI to verify and enhance AI decision-making.We developed a wireless earbud-like electroencephalography(EEG)measurement device,combined with tattoo-like electrodes and connectors,which enables continuous recording of high-quality EEG signals,especially the error-related potential(ErrP).The sensor measures the ErrP signals,which reflects the human cognitive consequences of an unpredicted machine response.The AI corrects or reinforces decisions depending on the presence or absence of the ErrP signals,which is determined by deep learning classification of the received EEG data.We demonstrate the BACLoS for AIbased machines,including autonomous driving vehicles,maze solvers,and assistant interfaces.展开更多
基金supported by the National Research Foundation (NRF)funded by the Korean government (MSIT) (NRF2019M3C7A1032076,and NRF-2020M3C1B8016137)funded by SKKU Research Project (S-2021-2151-000 International A).
文摘Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence(AI).If the AI understands the mental state behind a user’s decision,it can learn more appropriate decisions even in unclear situations.We introduce the Brain-AI Closed-Loop System(BACLoS),a wireless interaction platform that enables human brain wave analysis and transfers results to AI to verify and enhance AI decision-making.We developed a wireless earbud-like electroencephalography(EEG)measurement device,combined with tattoo-like electrodes and connectors,which enables continuous recording of high-quality EEG signals,especially the error-related potential(ErrP).The sensor measures the ErrP signals,which reflects the human cognitive consequences of an unpredicted machine response.The AI corrects or reinforces decisions depending on the presence or absence of the ErrP signals,which is determined by deep learning classification of the received EEG data.We demonstrate the BACLoS for AIbased machines,including autonomous driving vehicles,maze solvers,and assistant interfaces.
