Brain-Computer Interfaces in Human-Computer Interaction

Abstract

Brain–computer interfaces (BCIs) enable direct communication between neural activity and external devices, offering novel input channels that expand and redefine human–computer interaction (HCI). Originally developed from early electroencephalography (EEG), BCIs have evolved to include both invasive and implantable systems, with applications extending beyond clinical settings into rehabilitation, assistive communication, and interactive entertainment. This paper provides a concise survey of BCIs within HCI. The review begins by introducing neuroscience and signal-processing foundations, including signal acquisition—such as EEG, electrocorticography (ECoG) and intracortical recordings—as well as preprocessing, feature extraction, and decoding methods. Subsequently, representative application domains are explored, including: (1) assistive technologies, exemplified by brain-controlled wheelchairs, prosthetics, neurorehabilitation systems; (2) communication systems, such as P300 spellers, intracortical spelling interfaces, and emerging speech neuroprostheses; and (3) entertainment and gaming applications, including neurogaming and VR/AR integration. The discussion then turns to emerging trends—AI-enhanced decoding, wearable and wireless form factors, and multimodal integration—along with cross-cutting challenges such as neural data privacy, user autonomy and consent, usability, and regulation. The paper concludes with design implications for HCI and proposes a research agenda for safe, reliable, and equitable BCI deployment.