# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 5 Experiments and Results
### Experimental Set Up and Hardware Description  
This section illustrates the experimental work, Printed Circuit Board (PCB), and the hardware design. The experiments were performed in a realistic medical scenario. Additive White Gaussian Noise (AWGN) was the dominant noise, but it did not affect the performance of the system due to the camera being used as the receiver. The camera had an inbuilt filter which reduces the noise automatically during videos or image capture. Additionally, the burring effect could be decreased or reduced to minimum or zero by focusing the camera, hence a camera is easily implemented without hardware modifications. Cameras already have an inbuilt filter installed which filters unwanted noise, and the manual focusing or defocusing ability makes the camera an attractive option for communication purposes. Focusing of the camera reduces the blurring and the effects of ambient noise without the need for extra hardware, unlike traditional VLC systems where photodiodes are used as the receiver. Hence, the communication in VLC systems using LED lights installed in the infrastructure and a camera as the receiver has a lot of potential in EEG applications and hospitals, especially in RF restricted areas. The experimental equipment used is listed in Table 2 and Figure 5 states the experimental set up. A laptop was used for pre-processing and offline processing, both in MATLAB. The EEG data bits after signal processing in MATLAB were uploaded to the microcontroller using a USB cable through MBED software, by use of a laptop. The DD-160128 FC OLED [52] screen, used as the transmitter, had a length of 23.024 mm and a weight of 3.6 g. As per the data sheets, the OLED screen is a matrix of rows and columns. The voltage of 2.8 V was needed for the OLED screen evaluation board. In order to reduce the voltage from 3.3 to 2.8 V, a switching board or potentiometer was designed through KICAD software, the hardware design is shown in Figure 6. \begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment** & **Model** \\ \hline OLED screen & DD-160128 FC-1A \\ Camera frame rate & 30 fps (frame per second) \\ Camera & Canon, android, Thorlabs \\ Voltage power supply & 2.8 V for logic \\ Coding and design software requirement & KICAD, C, MATLAB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Equipment requirement. Figure 4: (**a**) Electroencephalography (EEG) signal; and (**b**) normalized EEG signal. In the proposed system, the interference from lights was reduced by switching on the desired system light within the EEG room. Usually, in the EEG room, only the patient and the doctor are present. The LOS condition minimizes the interferences, additionally LED lights do not interfere with electronic devices, nor does not cause hazard to patient's health, unlike RF. Blurring in the camera was reduced by focusing the camera and the dominant ambient noise had no effect on images or video captured as the camera had an inbuilt filter. The use of a camera as the receiver is the optimum solution as it reduces the hardware cost, unlike in traditional VLC systems where photodiodes are used as the receiver, which need hardware modifications, amplifiers, and filters. The preamble of 40 bits was used to distinguish between the frames and the useful data bit information transmitted. If the pixel size is 32 then, based on the data sheets, the OLED screen will be formed by five rows and six columns, thus forming a total of 30 blocks in one single frame of the OLED screen. The 30 blocks can be named as symbols, where each symbol or block represents a single binary bit transmitted using OOK modulation. The ARM processor was chosen due to low cost, easy availability, and being the most readily-used embedded systems. The FRDM-KL25Z ARM processor works on a voltage of 5 V and was powered by connecting to the PC using a USB cable. The EEG signal  
Figure 5: Experimental set up. Figure 6: Hardware description.