This study investigated performance characteristics and channel selection strategies for single-flicker steady-state visual evoked potential (SSVEP) brain–computer interfaces (BCIs) using minimal recording channels. SSVEP clustering patterns from seven subjects, who focused on four static targets while being exposed to a central 15 Hz stimulus, were analyzed. Using a single-channel approach, signal energy patterns were examined, and principal component analysis (PCA) was performed, which explained over 90% of the data variance. The Calinski–Harabasz Index quantified state separability, identifying channels and comparisons with maximum clustering efficiency. The results demonstrate the feasibility of implementing single-flicker SSVEP BCIs with reduced recording channels, contributing to more practical and efficient BCI systems.

Background/Objectives: Steady-State Visual Evoked Potential-based Brain–Computer Interfaces face a critical trade-off between system accuracy and user visual fatigue. To address this challenge, the objective of this study was to determine how the spatial manipulation of stimulus size modulates the full spectral dynamics of the Electroencephalogram, encompassing both the periodic oscillatory response and the aperiodic (1/f) background noise. Methods: Twenty-two healthy subjects completed a sustained visual attention task using a competitive stimulus paradigm (20 Hz and 30 Hz) presented in three spatial dimensions (Small, Medium, and Big). Parieto-occipital brain signals were decomposed using the spectral parameterization algorithm (SpecParam) to extract frequency-specific visually evoked response power and the aperiodic slope, while visual fixation was continuously monitored via eyetracking. Results: Increasing stimulus size induced a statistically significant gain in the power of the attended signal (Target) without increasing the response of the peripheral distractor. Simultaneously, larger stimuli produced a significant increase in the aperiodic slope during 20 Hz attention and visual rest, suggesting increased cortical inhibition and a reduction in broadband neural activity. This aperiodic modulation was not observed at 30 Hz. Conclusions: The improvement in Signal-to-Noise Ratio with increasing stimulus size arises from a dual neurophysiological mechanism: enhancement of the periodic evoked response together with a reduction in background neural noise.