A penny-sized wearable wireless EEG recorder

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Electroencephalography (EEG) is a test to record electrical activity in the brain by small electrodes through signals on the scalp. Due to its non-invasiveness, EEG recording has been widely used in a variety of applications, including sleep study, mental health monitoring, brain-computer interface, etc. However, mainstream clinical recording systems are still tethered with wet electrodes that require special preparation, limiting the setup to well-equipped labs and preventing the collection of longitudinal data. In this thesis, we summarized the special characteristics of EEG signals and presented a distributed recording system based on a penny-sized 1.2-gram (battery included) wearable wireless EEG recorder for long-term non-intrusive mental health monitoring. The core of the wearable EEG recorder is a custom designed chip, each with two signal-acquisition front-ends and a frequency-division multi-access (FDMA) transmitter. The signal-acquisition circuit is based on a direct recording 2nd-order ΣΔ ADC, which has an input-referred noise of 0.82 μVrms, a peak ENOB of 9.4 bits and an auto-ranging and fast-settling algorithm to cope with large instantaneous artifacts and interferences. The FDMA transmitter uses OOK modulation and supports in total 32 concurrent data telemetry channels, 12 of which are in the 915-MHz ISM band. This enables in total 24 concurrent recording sites in ISM band, and 64 sites if the experiment is in an isolated environment outside of FCC regulation. System functionality is also validated by comparing it to a clinical-grade instrument in two common EEG experiments, eye-closed alpha wave test and visual event-related potential (ERP) measurement. Similar performance has been observed between the two setups, proving our new system's potential to transform EEG measurement from uncomfortable tethered setup in the clinic, to non-intrusive long-term mental health monitoring at home.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2022; ©2022
Publication date 2022; 2022
Issuance monographic
Language English


Author Chen, Cheng
Degree supervisor Poon, Ada Shuk Yan
Thesis advisor Poon, Ada Shuk Yan
Thesis advisor Pauly, John (John M.)
Thesis advisor Wong, S. Simon
Degree committee member Pauly, John (John M.)
Degree committee member Wong, S. Simon
Associated with Stanford University, Department of Electrical Engineering


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Cheng Chen.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis Ph.D. Stanford University 2022.
Location https://purl.stanford.edu/gq941tf0508

Access conditions

© 2022 by Cheng Chen
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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