**6. Experimental results**

In this chapter, C3, Cz, and C4 are used to capture brainwave signals. Each subject wore an Ultracortex helmet connected with g.tec dry electrode and Emotiv EPOC chip to record MI-EEG signals including to imagine right-hand and left-hand movements. Each imaginary action was consumed 9 s for a data set. The EEG signals were extracted 28 times and transformed by wavelet transform to obtain their features. Therefore, we can obtain 140 sets for 5 subjects and these data sets were divided into 112 groups for training and 28 groups for testing. The experimental data acquisition process is down to obtain a data set every 9 s with an interval of 2 min. The waiting time is set on the first 2 s, then a stimulus signal was sound indicating that the testing process is started and a cross sign "+" is displayed for 1 s. Then, the left or right arrow is displayed to hint a subject imaging the moving of left or right hand. The sampling rate is 128 Hz for the acquisition process.

In this chapter, LSTM and GRNN are used as the EEG classifiers. MI-EEG features were extracted for C3, Cz, and C4 and classified into two groups. Therefore, the neurons of input and output layers of LSTM and GRNN were set three and two, respectively. In order to obtain

**Figure 8.** The performance competition between GRNN and LSTM.

z*<sup>t</sup>* = *σ*(*wx*,*<sup>z</sup>* ∗ *xt* + *wh*,*<sup>z</sup>* ∗ *ht*−<sup>1</sup> + *bz*) (7)

r*<sup>t</sup>* = *σ*(*wx*,*<sup>r</sup>* ∗ *xt* + *wh*,*<sup>r</sup>* ∗ *ht*−<sup>1</sup> + *br*) (8)

*<sup>t</sup>* = tanh(*wx*,*<sup>h</sup>* ∗ *xt* + *wh*,*<sup>r</sup>* ∗ (*rt* ∗ *ht*−1) + *bh*) (9)

*<sup>t</sup>* (10)

is the vector of update

) ∗ *h*˜

is the output vector in hidden layer, *zt*

In this chapter, C3, Cz, and C4 are used to capture brainwave signals. Each subject wore an Ultracortex helmet connected with g.tec dry electrode and Emotiv EPOC chip to record MI-EEG signals including to imagine right-hand and left-hand movements. Each imaginary action was consumed 9 s for a data set. The EEG signals were extracted 28 times and transformed by wavelet transform to obtain their features. Therefore, we can obtain 140 sets for 5 subjects and these data sets were divided into 112 groups for training and 28 groups for testing. The experimental data acquisition process is down to obtain a data set every 9 s with an interval of 2 min. The waiting time is set on the first 2 s, then a stimulus signal was sound indicating that the testing process is started and a cross sign "+" is displayed for 1 s. Then, the left or right arrow is displayed to hint a subject imaging the moving of left or right hand. The

In this chapter, LSTM and GRNN are used as the EEG classifiers. MI-EEG features were extracted for C3, Cz, and C4 and classified into two groups. Therefore, the neurons of input and output layers of LSTM and GRNN were set three and two, respectively. In order to obtain

*h*˜

**Figure 7.** The block diagram of GRNN.

22 Evolving BCI Therapy - Engaging Brain State Dynamics

is the input vector, *ht*

where *xt*

gate, and *r*

*t*

**6. Experimental results**

*ht* = *zt* ∗ *ht*−<sup>1</sup> + (1 − *zt*

sampling rate is 128 Hz for the acquisition process.

is the vector of reset gate, respectively.


**Table 1.** The accuracy rates of different strategies for BCI Competition 2003.

better performance for classification, the hidden layer is set into 7 neurons, and therefore, we can obtain the length of MI-EEG characteristic sequence being 15, while the channel number of MI-EEG-based BCI is 3. In order to evaluate the classification results and obtain a reliable and stable model, this model performs 500 cross validation to calculate the classification accuracy. In 2009, Smith [23] indicated that the nervous system is significantly important to integration of information and to the range of behaviors in which the system can stably engage and among which the system can flexibly switch. However, the nervous system, the body, and the environment each possess their own complex intrinsic dynamics, and these are always in continuous interaction with each other. Human intelligence reveals both remarkable stability and nimble flexibility. Stability emerges from the incorporation of the past into the present. Flexibility, requires an abandonment of (or selection among) past ways, a shifting of responses to meet new circumstances. For the consideration of stability and flexibility, the proposed methods are compared to other strategies based on "BCI Competition 2003" [24]. The experimental results are shown in **Table 1**. From **Table 1**, we can find that the proposed method can get better performance than others. Additionally, the GRNN is better than the LSTM with 2.67% and 5 ms in the performances of accuracy and classification speed that is shown in **Figure 8**.

Increasing the level number of DWT can directly reduce length of the EEG signals. If the db4 DWT is still used, the extracted signals will lose some features. Thus, reducing the DWT levels can retain more features in the original EEG signals. Increasing the number of hidden layers is due to the increased complexity of the input EEG signals. The more hidden layers are conducive to processing the data with higher complexity. However, too many hidden layers will cause the network to be difficult to converge during the learning process. In this section, additional one layer is added into hidden layer for obtaining better convergence properties. The classification accuracy rates for db4 wavelets by LSTM and GRNN networks with seven layers in hidden layer are shown in **Figure 9**, while the classification accuracy rates for db2 wavelets by LSTM and GRNN networks with eight layers in hidden layer are shown in **Figure 10**. From **Figures 9** and **10**, we can find that the accuracy rates of test data are obviously increased and nearby the accuracy rates of training data for both LSTM and

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

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

25

Then, two BCI systems have respectively embedded LSTM and GRNN with db2 wavelets and eight hidden layers are applied to control an electric wheelchair. They can smoothly control an electric wheelchair and the GRNN model can always get better performance than the LSTM.

**Figure 10.** The accuracy rates in LSTM and GRNN with db2 wavelets and eight hidden layers. (a) LSTM. (b) GRNN.

GRNN networks.
