**2. Materials and methods**

#### **2.1 Data recording process and users**

In this study, the dataset (AVI SSVEP Dataset) containing SSVEP signals designed and recorded by Adnan Vilic was used [17]. The data set contains data that include EEG measurements of healthy individuals (three men and one woman having ages range

*Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System… DOI: http://dx.doi.org/10.5772/intechopen.98335*

from 27 to 32) looking at the flickering target to trigger responses of SSVEP signals at different frequencies, and the data set used for this study is publicly available. Using the standard international 10–20 system for electrode placement, the reference electrode is positioned in Fz with the signal electrode in Oz and Fpz in the ground electrode. In this experiment, individuals had been seated 60 cm away from a monitor staring at a single flashing target whose color changed rapidly from black to white. The test stimulus was a flashing box at seven different frequencies (6–6.5 - 7 - 7.5 - 8.2 - 9.3 - 10 Hz) presented on the monitor. The data set comprises of four sessions with four different participants. Each trial in a session lasts 30 seconds and participants take a short break between trials. Experiments were repeated at least three times for each frequency.

In **Figure 1**, a) the raw signal stimulated at a frequency of 10 Hz and b) the power spectrum density computed signal (with its 1st and 2nd harmonics) are shown.

#### **2.2 Feature extraction**

It is possible to define the neurophysiology of the human visual system, the neuronal activity of the visual cortex is replaced by visual stimulation, and variations of the brain response related to the features of the visual stimulus such as brightness, contrast and frequency [18]. Neurons in the visual cortex synchronize their flickering to the frequency of blinking of the visual stimulus. SSVEP signals are generated when visual stimuli are repeatedly presented, creating almost sinusoidal oscillations [19]. Applying a visual stimulus flashing at a constant frequency increases the energy of brain activities comparing to the case of applying a constant visual stimulus [7]. The strongest response occurs in the visual cortex of brain (occipital), but other areas of brain are also activated to different degrees [8, 9]. SSVEP marks can be detected even for narrow frequency bands around the visual stimulation frequency with signal processing methods that take advantage of the specific features of the signal such as timing, frequency, and rhythm [20]. For this reason, this study is designed on accepted signal processing strategies that validate the comprehensive scenarios analyzed.

#### *2.2.1 Time-domain based feature extraction*

The SSVEP time-domain features are extracted from available literature information in the original field of the EEG signal. **Table 1** describes the relevant and

**Figure 1.**

*a) SSVEP raw signal b) power spectrum of the 10 Hz stimulated SSVEP signal and topography.*


**Table 1.**

*EEG time-domain features (EEG signal is represented by x, and* Fð Þ<sup>t</sup> <sup>i</sup> *stands for the EEG features computed from x).*

distinctive SSVEP time-domain features we identified. These features are based on the amplitude (e.g. average amplitude change value, root mean square, interquartile ranges, etc.) and statistical changes of the EEG signal (e.g., mean, variance, skewness, and kurtosis, etc.) [20].
