Brain-computer interface (BCI) systems that use Electroencephalography (EEG) signal processing based on the Steady State Visual Evoked Potential (SSVEP) method are helpful in assistive technologies because they allow for non-invasive communication and control of external devices. When visual stimuli on the screen flash at specific frequencies, SSVEP signals are produced. These signals are commonly used because of their robustness and high signal-to-noise ratio. In systems for assistive communication devices, the aim is to respond to the EEG patterns using two occipital channels (O1 and O2) and achieve response detection through simple signal processing. This research utilizes publicly available EEG datasets. Filtering, Fast Fourier Transform (FFT), and machine learning-based classification are some of the signal processing techniques to extract and classify SSVEP responses from EEG signals. This research has significant implications in the design of efficient BCI systems, human–computer interaction, neurorehabilitation, and assistive technologies.