<![CDATA[Steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) provide a non-invasive method for hands-free device control. However, their practical applications are limited by reliance on costly laboratory-grade electroencephalography (EEG) systems. This study addresses this gap by designing and evaluating a real-time, six-command SSVEP-BCI for drone navigation using a consumer-grade EEG headset. An adaptive processing pipeline was developed to extract spectral and spatial features, which were classified using Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models. Analysis of data from 30 participants revealed that the RF classifier achieved an optimal balance between performance and speed, with a high classification accuracy of 87.24% and a low computational latency of 0.09 seconds, resulting in a high information transfer rate (ITR) of 35.0 bits/min. In contrast, the ANN was insufficiently accurate, and SVM performance was marginal. These findings demonstrate the viability of low-cost, multi-command SSVEP-BCIs for applications in assistive technology, teleoperation, and human-computer interaction.]]>
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