## 3 Experiments and Results
### CSP Using New Combinations of Signals  
During the test period of 9 s, the signals in the image period only contained good information. Our paper presents a time-frequency analysis of EEG signals in the image period, which shows the outstanding performance of the image period with the time window on the running image scale. ICA breaks down the channel data into interdependent components based on the training data. The number of ICA components was automatically selected based on the classification function in the training method (measured with mutual accuracy), which is physiologically appropriate because the neural sources of the ICA components are likely to be mutually independent and independent of ambient noise. Thus, significant neural components associated with movement/imaging are relatively amplified compared with unrelated neural components and noise ([PERSON] et al., 2009, [25]). To demonstrate ICA-amplified neural signals, we created four samples of ICA topography. Many aids were identified from the sensory areas and formed a circular shape, which reflects our system's emphasis on motor signals. The ICA spatial filter attenuates the EEG signal in some parts of the scalp and amplifies the EEG signal in other parts of the scalp. To visualize typical signals amplified by the ICA spatial filter, ICA topographic diagrams were created for one person (person number one), and two mental images were created as graphs. To classify the two images, diagrams show which areas of the scalp have been weakened and which have been strengthened. A classification is made up of each chart because the weight and number of ICA components differ between classifications owing to different training data (Figure 18). Figure 17: Electrode montage corresponding to the international 10–20 system. Figure 19 shows a frequency diagram with the power of the spectrum in the frequency range 8-30 Hz. It examines 16 channels for the two lip-reading concepts \"A\" and \"M\" in EEGLAB. The power spectrum was obtained between 10 and 20 Hz in the frequency range 8-22 Hz. Between the two intervals, it is slightly less and returns to the original state. These intervals are 14-14 Hz and 22-20 Hz. Then, between 24 and 22 Hz, the power of the spectrum increased to 40 Hz. First, between 25 Hz and 24 Hz, it decreases slightly at a slow rate. At approximately 25 Hz, it decreases with a steep slope and reaches 20. In the range of 28-26, it goes up and down slightly, and then the frequency returns to the power of the previous intervals, which indicates the power of the spectrum in this range, which increases during imaging. Figure 19: Maps of power frequency at 25 Hz with different channels between 16 and 36 Hz. Figure 18: ICA topography maps on 16 channels between 8 and 30 Hz. For comparison with previous work (speech imagery and mental tasks) [101], Channels C3 and C4 near Broca's area and Wernicke's area were selected to analyze event-related spectral perturbation (ERSP). Using complex Morlet wavelets, a single trial involved superimposing the energy spectrum by ERSP. EEGLAB was used to plot ERSP in this dissertation [100]. The ERSPs related to the two channels are presented for Subject 1 in the second and third steps in Figure 24.  
For comparison with previous work (speech imagery and mental tasks) [101], as in another article in the field of imagination, channel signals were referenced, and all 16 channels were selected for all areas of the brain, particularly the Broca and Wernicke regions. Using the complex Morlet wavelet, the experiment had an extraordinary energy spectrum by ERDS. CSP was used to draw the ERDS in this study. Figure 25 shows the ERDS in the frequency range of 23-27 Hz between 0 and 4 s. The imagination period for each activity was examined separately and simultaneously for two images, and different energy strengths were observed for all three expressed cases.