BER Performance comparison of EEG Healthcare system using 8-pixel, 16-pixel OLED screen and DSLR camera

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