Jingjie Li (李竞捷)
Center for Neural Science, New York University
Neural Science, New York University Shanghai

Electroencephalography(EEG) signal plays a significant role in guiding linical treatment and running scientific research in neuroscience, cognitive science, and psychology, etc. since it contains rich brain activity related to physiological and pathological information. However, broader usage of the mainstream commercial EEG acquisition devices is limited given its relevantly large size, strict shielding, and power supply requirement and expensive cost. Our research aimed to design and implement a low-cost, portable EEG real-time monitoring devices which need to be completely functional for EEG signal acquisition and display. The main work we've done is summarized below:
Firstly, we accomplished the design and implementation of the portable EEG acquisition hardware. To begin with, we selected proper acquisition probes, core Front-End IC(Texas Instrument ADS1299), data processor IC(Texas Instrument TMS320F28335) and electrical isolation (both for power and signal) solutions. After that, we completed the design of surrounding circuit wiring before completing the development of the embedded program in the data processor IC.
Secondly, we accomplished the design and implementation of signal real-time processing and displaying software. Using Processing IDE written in $Java$ Language, we developed a software which could display real-time EEG waveform received from EEG signal acquisition hardware. We furthermore added a real-time spectrum analyzing and displaying function which could visualization both spectrum and frequency-based band energy distribution.
Thirdly, we accomplished the implementation, debugging and testing of the portable EEG monitoring system. The detailed testing physical and electrical characteristics of our EEG system was tested under laboratory conditions was done after the accomplishment of the manufacturing of the hardware device. Our testing results revealed a decent signal meansuring performance: a 99dB CMMR and >50dB SNR. To simulate real EEG signal measuring, we also generated small sin-wave signals and fed it into our measuring devices. We finally measured multiple real electrophysiology signals using our EEG monitoring system to evaluate the function of our designed system. The results collected from real EEG and ECG measurements suggested that our devices are capable of human electrophysiology signal acquisition and monitoring. However further evaluation onto the EEG signal acquisition quality needs to be done because of the difficulty on verifying the authenticity of the EEG signal we collected given the randomness essence of the EEG signal.
To sum up, the EEG acquisition hardware system we accomplished in this article satisfied the requirements for portability given it's relevantly small size(L:9.26cm* W:7.74cm* H:1.5cm) and the requirements for EEG signal measuring given its decent acquisition performance. Also, our EEG system was equipped to decent signal processing ability, unabridged function from signal acquisition to waveform display and saving. Moreover, the building cost of our EEG system was low. Therefore, our design provided a small-size, low-cost solution for mobile medical EEG monitoring or mobility-required neuroscience EEG research.