**1. Introduction**

Electroencephalogram (EEG) signals are one of the most widely used types of biomedical signals for Brain-Computer Interfaces (BCIs), owing to their portability, high time resolution, ease of acquisition, and cost-effectiveness as compared to other brain

activity monitoring techniques [1–3]. There are four typical EEG-based BCI paradigms: steady-state visual-evoked potentials (SSVEP), slow cortical potentials (SCP), the P300 component of evoked potentials, and sensory-motor rhythms (SMR) [4–6].

The SSVEP signal is a periodic response to a visual stimulator modulated at a frequency greater than 6 Hz [7] or 4 Hz [8]. The amplitude and phase characteristics of the SSVEP depend on stimulus intensity and frequency. SSVEP events can be repeatedly produced if the stimuli are provided under controlled conditions [9]. For instance, staring at a flickering light that flashes at a constant frequency stimulates the human visual pathway. The flickering frequency is radiated throughout the brain. This stimulation produces electrical signals in the brain at the base frequency of the flashing light, as well as at its harmonics [10]. Practically, there is a marked reduction in the power of the SSVEP signals from the second harmonics onwards. This has been attributed to the low signal-to-noise ratio of the SSVEP signals at high frequencies and can be accounted for the brain dynamics that act as a low pass filter [11].

The analysis of EEG signals using machine learning (ML) methods is developed to help physicians in accurate diagnosis and provides fast and valid tools in assistive applications designed for individuals. Among the various approaches available in the literature, the Wavelet Transform (WT) has proven to be an effective time-frequency analysis tool for analyzing transient signals [12, 13]. Various wavelet families are available to define and adapt signal characteristics [14]. However, choosing an appropriate mother wavelet is very important for the analysis of these signals. Research studies to date for EEG-signal classification using the wavelet technique have mostly been done using the Daubechies (Db) family. The maximum accuracy achieved in this study was 95.00% [15]. However, in this study, although the signal was suitable for Discrete Wavelet Transformation (DWT), analysis was performed using the Continuous Wavelet Transformation (CWT) method. Furthermore, in the same study, analysis was performed for a single frequency. In this chapter, a detailed analysis was performed using multiple frequencies. Also, in Ref. [16], the SSVEP signal was used for a single wavelet type (Db40), but no mother wavelet selection was made. Thus, the mother wavelet selection for SSVEP is still an unanswered question.

The research presented in this chapter is especially about selecting the most suitable wavelet function for signal analysis of SSVEP signals, detailed investigation of energy, entropy, and variance attributes, and examining the appropriate frequency(s) for SSVEP based BCI design.

There is not any, to our knowledge, in-depth study on the selection of stimulation frequencies. It was noticed that higher accuracy rates could be obtained for pattern recognition by examining the frequency selection and the differences between the frequencies. The frequency or frequencies that might result in higher higher accuracy rates and time advantages are considered to help design user-friendly BCI systems. Due to the shortcomings in the literature mentioned above, this study was considered to be conducted.
