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A brain-computer interface is a communication and control system that translates neural activity recorded directly from the brain into control signals that operate external devices without requiring the involvement of peripheral nerves or muscles. The system acquires electrophysiological signals—most commonly scalp-recorded electroencephalography, but also invasive cortical recordings, intracortical microelectrode arrays, or magnetoencephalography—and processes them through signal amplification, artifact removal, feature extraction, and classification algorithms to identify the user's intended command ^[1]. The distinguishing characteristic is that the output command is derived purely from brain signals, bypassing the normal motor output pathway, and the user learns to modulate specific neural patterns—such as sensory motor rhythms, P300 event-related potentials, or slow cortical potentials—to select among discrete control options. The closed-loop interaction includes feedback to the user, typically visual, auditory, or tactile, that informs them of the decoded command, enabling operant conditioning to improve signal modulation over training sessions. Signal classification algorithms range from linear discriminant analysis and support vector machines to deep neural networks, and the performance metric is information transfer rate measured in bits per minute ^[2]. The conceptual basis is that intentional cognitive states produce distinguishable spatiotemporal patterns of brain activity that can be decoded in real time, and the interface establishes a direct pathway from intention to action independent of neuromuscular output ^[3].