Ryan Kaveh

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-56

May 1, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-56.pdf

In recent decades, wearable health monitors have grown from crude heart rate sensors to all-in-one devices that can track steps, motion, location, arrhythmia, electrocardiogram, blood oximetry, and calorie usage. More recent work has focused on developing wearables that provide non-invasive neural recording (electroencephalography - EEG) for focus, stress, and drowsiness monitoring. As wrist-worn wearables run out of features to add, head/ear worn EEG wearables offer new ways to provide users with helpful and actionable wellness information. The problem blocking widespread neural wearables is that existing devices are bulky, uncomfortable, require single-use wet electrodes, and often require training in everyday scenarios.

This thesis details an end-to-end design process for low-profile, dry-electrode neural recording hearables that can record EEG inside the ear. Starting from the EEG signal basics, this work will walk through the modeling, design, and verification of the three parts of an Ear EEG system: the electrodes, the neural recording system, and the downstream processing and classification software. Particular focus is placed on maximizing user comfort and the ease of manufacture without hurting system performance.

All of these topics will be based on constructing a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. Different earpiece manufacturing processes will be showcased for prototyping (using 3D printing and electroless plating) and production at scale (using vacuum forming and spray coating). The performance of this system will be evaluated with human subject trials that recorded spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, auditory steady-state response (ASSR), and drowsiness detection.

Advisors: Rikky Muller

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BibTeX citation:

@phdthesis{Kaveh:EECS-2023-56,
    Author= {Kaveh, Ryan},
    Title= {Ear EEG: Sensors and Systems for User-generic Neural Hearables},
    School= {EECS Department, University of California, Berkeley},
    Year= {2023},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-56.html},
    Number= {UCB/EECS-2023-56},
    Abstract= {In recent decades, wearable health monitors have grown from crude heart rate sensors to all-in-one devices that can track steps, motion, location, arrhythmia, electrocardiogram, blood oximetry, and calorie usage. More recent work has focused on developing wearables that provide non-invasive neural recording (electroencephalography - EEG) for focus, stress, and drowsiness monitoring. As wrist-worn wearables run out of features to add, head/ear worn EEG wearables offer new ways to provide users with helpful and actionable wellness information. The problem blocking widespread neural wearables is that existing devices are bulky, uncomfortable, require single-use wet electrodes, and often require training in everyday scenarios.

This thesis details an end-to-end design process for low-profile, dry-electrode neural recording hearables that can record EEG inside the ear. Starting from the EEG signal basics, this work will walk through the modeling, design, and verification of the three parts of an Ear EEG system: the electrodes, the neural recording system, and the downstream processing and classification software. Particular focus is placed on maximizing user comfort and the ease of manufacture without hurting system performance.

All of these topics will be based on constructing a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. Different earpiece manufacturing processes will be showcased for prototyping (using 3D printing and electroless plating) and production at scale (using vacuum forming and spray coating). The performance of this system will be evaluated with human subject trials that recorded spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, auditory steady-state response (ASSR), and drowsiness detection.},
}

EndNote citation:

%0 Thesis
%A Kaveh, Ryan 
%T Ear EEG: Sensors and Systems for User-generic Neural Hearables
%I EECS Department, University of California, Berkeley
%D 2023
%8 May 1
%@ UCB/EECS-2023-56
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-56.html
%F Kaveh:EECS-2023-56