**2. System architecture**

The proposed BCI system is integrated as EEG signals extracting subsystem through the Emotiv EPOC chip, g.SAHARAbox system, and g.SAHARA electrodes. The g.SAHARAbox system and g.SAHARA electrodes are shown in **Figure 1**. The system's electrodes are dry manner and nonintrusive conductive system that allows 16 EEG channels to be embedded into the input of EPOC chip at the same time. The electrode locations C3, C4, and Cz based on the international 10–20 system, shown in **Figure 2**, were used to extract EEG signals, while locations A1 and A2 were used as reference points. For the MI-EEG signals, two motion-imagination brain signals were recognized, respectively. One is "imagining right-hand action" and the other is "imagining left-hand action." In order to establish a sampling model, we captured 9-s EEG signals for every imagining action from every channel. And, the extracted brainwave signal is transformed through DWT to obtain the spectrums in frequency domain. Then, the frequency feature was calculated and classified into different categories by using LSTM and GRNN.

In order to speed up the processing of DWT and update the classification performance in the deep learning algorithms, the NVIDIA Jetson TK1 is used in the proposed system. In the platform, NVIDIA Tegra K1 SoC is embedded with a super computing core NVIDIA Kepler. So that it is a high-speed computing system for rapid development and deployment in computer vision, robotics, medical applications, and more. Additionally, an FPGA module named Xilinx Virtex4 XC4VFX12 is also applied to control external system such as electric wheelchair.

A Motor-Imagery BCI System Based on Deep Learning Networks and Its Applications

http://dx.doi.org/10.5772/intechopen.75009

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The concept of wavelet was proposed by Jean Morlet in 1981. In this chapter, The Daubechies wavelet, proposed by Dr Daubechies in 1988 [20], was used to extract the features from EEG signals. It is often used in signal compression, digital signal analysis and noise filtering, and so on. In Daubechies wavelet, several series db wavelets can get better performance in signal analysis. In this chapter, db4 wavelets were used to extract main features from EEG signals. Multiresolution analysis in the WT algorithm was proposed by Mallat [21] in 1989. When a signal resolution has a high-degree variation in a proper area, it is difficult to get detailed features while the multiresolution strategy can decompose the lower layer signal to get more information. Therefore, the decomposed low-frequency signal can be decomposed continuously to display more features. However, the decomposed iterations of the signal are so many to make the number of samples so few that results in less obvious characteristics

Therefore, the number of signal decomposition layer is limited. In the wavelet decomposition, the original signal is input to a low-pass filter g[k] and a high-pass filter h[k], respectively. The low-pass filter retains the consistency of the original signal, and the high-pass filter reserves the variability of the original data. Discrete wavelet transform can be combined with wavelet function and scale function. In the low-frequency part, it has a high frequency resolution and low temporal resolution, while there was a lower frequency resolution and a higher time resolution in the high-frequency part. The discrete wavelet transform decomposition and recombination is shown in **Figure 3** and the multiresolution analysis in

The left half is wavelet decomposition, after the high-pass and low-pass decomposition and then downsampling to get two groups of detailed signal and the approximate signal. The right half in **Figure 3**, the decomposition of the series for the rise of sampling, and then through the high-frequency synthesis filter and low-frequency synthesis filter can be reconstructed.

**3. Discrete wavelet transform**

of the signal.

the WT is shown in **Figure 4**.

**Figure 3.** Discrete wavelet decomposition and reconstruction.

**Figure 1.** The subsystems in the proposed BCI: (a) g.SAHARAbox system and (b) g.SAHARA electrodes.

**Figure 2.** Locations C3, C4, and Cz are used in the 10–20 system.

In order to speed up the processing of DWT and update the classification performance in the deep learning algorithms, the NVIDIA Jetson TK1 is used in the proposed system. In the platform, NVIDIA Tegra K1 SoC is embedded with a super computing core NVIDIA Kepler. So that it is a high-speed computing system for rapid development and deployment in computer vision, robotics, medical applications, and more. Additionally, an FPGA module named Xilinx Virtex4 XC4VFX12 is also applied to control external system such as electric wheelchair.
