Page 97 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 3 – Internet of Bio-Nano Things for health applications

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 3

Experiment-2
Experiment-1
Fig. 4 – Software control observation
Fig. 3 – Hardware implementation observation

Additionally, brainwave data has also been Additionally, the noise factors are incorporated in
implemented in [20] for the control system of the distortion of the quality of the raw data. The
scrolling through computer applications. Thus, the invariable muscular movements were
user has been able to surf through a news feed or incorporated as noise artefacts that required
webpage while within the focus state as discussed further extensive processing. Hence, the
earlier. In Fig. 4, 4(a) shows the FFT plot of the EEG constraints over the potential values were
data, 4(b) and 4(c) shows the head region plots challenging hindrances while attempting to
during the monitoring and controlling of the virtual accumulate the desired frequency ranges in order
screen. For Fig. 4(a), on the head scalp near the to implement certain requirements as per the
occipital region one channel was attached to respective cases.
acquire the visualization data. As indicated in 4(a), Furthermore, the motion control of the car
the ranges of potential spikes are over a wider experiment suggested that the slightest deviation
range of frequency. However, the head region plots of the correspondence between the frontal and the
display activity in the occipital region which wasn't parietal lobe can cause a significant disparity in the
as visible in the previous section. The mapping of motion of the car. Similarly, the cursor control
the EEG data between the frontal and occipital lobe system may lack stability caused by equivalent
has enabled the opportunity to control the cursor factors. Hence, any inconsistency of focused
by comparison of the action potentials. Further cognitive functioning may disrupt the entire
precision of the control can be enabled through control system regardless of the system outcome.
deep learning techniques of the brainwave data. Nonetheless, our analysis establishes the fact that
The fundamental aspiration of the paper was to specific tasks can be performed up to a certain
successfully acquire brainwave data with the degree by utilizing EEG brainwave data. For further
assistance of the OpenBCI Cyton board in real-time. accuracy of the implementation of the feedback
Upon the initial observation of the collected data, it generation, more precision is required through
emerged as a burst of reading due to the machine learning approaches.
continuous brain activity. Since the brain is
continuously generating signals, the data
accumulation in the aspired range for application
has been considerably challenging.

© International Telecommunication Union, 2021 85