AGGARWAL, Geetika, DAI, Xuewu, SAATCHI, Reza and BINNS, Richard (2020). BER Performance comparison of EEG Healthcare system using 8-pixel, 16-pixel OLED screen and DSLR camera. In: CHAKI, Jyotismita, DEY, Nilanjan and DE, Debashis, (eds.) Smart Biosensors in Medical Care. Advances in ubiquitous sensing applications for healthcare . Elsevier, 107-123.
Abstract
Electroencephalography (EEG), is predominantly used in healthcare to analyse the brain
activity through sensors or electrodes placed on scalp. The conventional EEG systems based
on Radio Frequency (RF) technology used in Healthcare are tedious, involve long preparation
times and suffer from electromagnetic interference (EMI). Furthermore, in hospitals
specifically Intensive Care Units (ICUs), EMI from RF devices might cause an adverse effect
on both patient’s health and medical devices. Therefore, in context to the flaws of RF
technology in Healthcare in addition to frequency crunch, a subdivision of OWC, known as
VLC, is preferred. The captivating dual functioning of communication and lightning of VLC
deploying LEDs in addition to low cost, no eavesdropping, EMI free, has enhanced the scope
of VLC in numerous applications such as Indoor localization, Underwater communications,
Vehicle to Vehicle communications and many more. VLC systems use either photodiode as a
receiver or camera. The use of camera in VLC systems as receiver reduces the infrastructure
tariff substantially because of no requirement of additional amplifiers, filters etc unlike VLC
systems, where PD is the receiver. In that viewpoint, this chapter experimentally demonstrates
the BER (bit -error- rate) performance comparison of novel EEG Healthcare system using 8-
pixel and 16-pixel OLED screen and DSLR camera as transmitter and receiver respectively.
The lab tests were conducted at 30fps with both 8-pixel and 16-pixel OLED screen. The
proposed system achievable bit rate was 2.8kbps and the error free transmission up to 1.75m
and 2.25m with 8-pixel and 16-pixel OLED screen respectively. The experimental results,
showed that there is a trade-off between the pixel size and BER, as with smaller pixel size, bits
transmitted per frame is enhanced in comparison to larger pixel size, however BER
significantly increase for smaller pixel size when compared the BER results of 8 and 16 pixel
respectively. Due to system on chip solution, low cost, low power design, free from EMI, the
proposed system prototype has the potential to be deployed in 5G in RF sensitive areas such
as hospitals and future technology for remote or wireless brain monitoring amongst patients
for EEG applications in Brain Computer Interface
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