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Top1. Introduction
The Big Data biological processes have very complex procedures, which imply neural as well as hormonal stimuli and responses. These biomedical signals generally represent a collective electrical signal attained from any organ, signifying a physical variable of interest. To store and handle these Big Data different technologies are frequently applied in the biomedical and health-care field (Luo & Zhao, 2016) to facilitate health-care activities. The energy management for real-time Big Data is a critical issue. Thus, energy and performance trade-off in resource optimized model design for Big Data is discussed in (E. Baccarelli & Stefa, 2016).
The Biomedical Big Data cover a wide range of the following signal: electrooculogram (EOG), electroneurogram (ENG), electrogastrogram (EGG), phonocardiogram (PCG), carotid pulse (CP), vibromyogram(VMG), vibroarthogram(VAG), electrocardiogram (ECG), electroencephalogram (EEG), and electromyography (EMG). However, most widely used biomedical signals in healthcare applications are ECG, EEG, EMG, and EOG (Jiang & Lin, 2007), (Mowla & Paramesran, 2015).
The EEG signal is able to track changes within millisecond time-span, and is a good tool for analyzing brain activity (Urigüen & Zapirain, 2015). Moreover, this EEG signal is preferred to other signals. Certain physiological signal such as SET tracks changes in the blood circulation and positron emission (PET) measures the change in metabolism which is indirect indicators of electrical activity belonging to the brain, while EEG specifically tests the electrical activity of the brain. This software will assist in pre-processing (Roy & Shukla, 2019), (Bigdely & Robbins, 2016) of the EEG data to enable data sharing, archiving, large-scale machine learning/data mining and (meta-) analysis.
Usually, EEG Signals can be classified based on their frequency, amplitude and shape. The most common classification is based on the frequency of EEG signals (i.e. alpha, beta, theta, and delta) (Chen & Householder, 2018). Figure 1 shows the brain rhythms arranged according to increased frequencies. The brain waves with their frequency band and the corresponding brain activities are revealed in Table 1.
Table 1. Electroencephalography (EEG) Signal Frequency Bands.
| Name | Frequency Band (Hz) | Predominantly Brain Activity |
| Delta | 0.5 to 4 | Sleeping |
| Theta | 4 to 8 | Dreaming, Meditation |
| Alpha | 8 to 13 | Relaxation |
| Beta | 13 to 36 | Alert/Working
Problem Solving |
| Gamma | 36 to 100 | Multisensory semantic matching Perceptual function |