Page 94 - 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

Fig. 1 – System architecture [20]

experiments we have administered thus far- 3.2 Data processing
essentially monitors the variations in EEG data
during the motion of the arm or eye. It aspires to As per Fig. 1, the acquired brainwave data requires
justify the objective of our paper by comparing the extensive steps of processing and optimization
focus state and cognition to the developments in before it can be analyzed. Thus, the discussion in
physical movement. The experiments directed in [10] embodies the construction of the anticipated
[21] have a comparable approach to that of ours. data processing operations through the user
However, the control of an exoskeleton requires an interface. Our foremost concern is the noise
expanded range of focus-control mechanisms interferences associated with the raw brainwave
which employs a considerable number of data. Ordinarily, the non-invasive method
electrodes. In [21], the connectivity of the contributes to the factors of noise since brainwave
extensive driver circuits is maintained by the frequencies are not being directly extracted.
16-electrodes channels that provide higher Natural obstacles existing between the electrodes
accuracy of brainwave data. and the brain, i.e. the skull, skin, hair, etc. as well as
artefacts (muscular movements) contribute to the
As discussed earlier, the inherent performance of noises [19]. To optimize the data, the user interface
the brain associated with the intellectual and applies a Signal-to-Noise Ratio (SNR) for
comprehensive features remains consistent strengthening the signal that is estimated by
regardless of the capability of muscle control [6]. applying the root-mean-square [10, 18].
Although the subject of our paper exhibits the Furthermore, signal filtering and sampling
functionality of a healthy brain, the constrained facilitate the selection of the desired ranges of data
ranges of the accumulated EEG data has facilitated to be executed accordingly. The sampling theory of
the execution of the applications as designed. Nyquist-Shannon ensures that the accumulated
Consequently, [6] administered the control of EEG signals are sampled at a frequency of 250 Hz
brainwave frequency in the ranges 0.15-5 Hz in the by the user interface applications [19]. Upon the
time-span of 300-600ms by the commonly used passage of the frequencies through the
P300 in the BCI system. On a similar account, Butterworth filters, the aspired ranges are
[10, 14, 17-19] represents the manipulation of the separated for assigned requisitions. Additionally,
acquired brainwave data in the targeted the presence of electronic devices that emit
bandwidth for the respective allocations. electromagnetic waves of 50-60 Hz can cause
distortion. Hence, to eliminate the specific
bandwidth, notch filters are applied in the system.

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