On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces

被引:3
作者
Fabietti, Marcos [1 ]
Mahmud, Mufti [1 ,2 ]
Lotfi, Ahmad [1 ]
机构
[1] Nottingham Trent Univ, Dept Comp Sci, Clifton Lane, Nottingham NG11 8NS, England
[2] Nottingham Trent Univ, Med Technol Innovat Facil, Clifton Lane, Nottingham NG11 8NS, England
来源
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2021年
关键词
EEG; embedded systems; hardware processing; neural networks; signal processing; EEG ACQUISITION SOC; CLASSIFICATION-SYSTEM; FEATURE-EXTRACTION; SEIZURE; PROCESSOR;
D O I
10.1109/IJCNN52387.2021.9533413
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The improvement of hardware for the acquisition and processing of electroencephalography (EEG) has made its portability become a reality. This allows for studies to be carried outside lab settings, as well as many commercial applications. As recordings are done over extended periods, these devices generate large volumes of data, mainly if the neuronal activity is recorded through multiple channels. Machine learning (ML) techniques allow to effectively analyse and use this data for a wide range of applications. However the portability of these techniques can be challenging. In this article, we set out to review over 40 relevant articles where ML techniques in a diverse set of EEG applications that have successfully been incorporated into portable systems.
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页数:8
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