**7. Applications to control an electric wheelchair**

In this section, the proposed BCI system was applied to control an electric wheelchair. During the online experiment, each subject wore the EEG acquisition system with integrated g.SAHARAsys and EPOC chip in the proposed BCI system. Additionally, the EEG signal for eye blinking was added in order to easily control an electric wheelchair to go ahead or emergency stop. For MI-EEG signals, imagining left hand and right hand are translated into turning wheelchair left and right as well as the eye blinking signal is converted into going ahead/emergency stopping. For the purpose of speeding up the extraction and processing EEG signals, the sapling interval was adjusted to 1 s. But these modifications result in losing a few features. Therefore, the db4 wavelet is adjusted to two levels as well as additional one layer is added into hidden layer of LSTM and GRNN networks.

**Figure 9.** The accuracy rates in LSTM and GRNN with db4 wavelets and seven hidden layers. (a) LSTM. (b) GRNN.

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 GRNN networks.

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

In this section, the proposed BCI system was applied to control an electric wheelchair. During the online experiment, each subject wore the EEG acquisition system with integrated g.SAHARAsys and EPOC chip in the proposed BCI system. Additionally, the EEG signal for eye blinking was added in order to easily control an electric wheelchair to go ahead or emergency stop. For MI-EEG signals, imagining left hand and right hand are translated into turning wheelchair left and right as well as the eye blinking signal is converted into going ahead/emergency stopping. For the purpose of speeding up the extraction and processing EEG signals, the sapling interval was adjusted to 1 s. But these modifications result in losing a few features. Therefore, the db4 wavelet is adjusted to two levels as well as additional one

**Figure 9.** The accuracy rates in LSTM and GRNN with db4 wavelets and seven hidden layers. (a) LSTM. (b) GRNN.

the performances of accuracy and classification speed that is shown in **Figure 8**.

**7. Applications to control an electric wheelchair**

24 Evolving BCI Therapy - Engaging Brain State Dynamics

layer is added into hidden layer of LSTM and GRNN networks.

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.
