Interaction Bandwidths of Non-Invasive BCI for Interactive AI

Abstract

Brain-computer interface (BCI), particularly non-invasive consumer-grade EEG systems, have recently attracted renewed attention as advances in artificial intelligence (AI) are shaping a new interaction paradigm: Interactive AI. However, there remains limited clarity regarding the types of interactions that non-invasive BCI can realistically and reliably support outside clinical settings. Existing work in human factors and neuroergonomics has demonstrated the use of BCI for motor imagery control, cognitive, and assistive applications, while these approaches are often focused on decoding and accuracy rather than on their impact at the interaction level.This paper proposes an interaction-oriented framework that characterizes non-invasive BCI not as a direct communication channel for explicit user intent but as a contextual helper defined at the interaction level, leveraging available low-bandwidth channels more effectively within Interactive AI systems. We distinguish between control paradigms and indirect semantic alignment approaches mediated by AI, using contemporary large language and vision-language models (LLMs and VLMs). Drawing on prior work in applied human factors and an exploratory prototype using a consumer-grade EEG device, we illustrate how cognitive-state signals can be incorporated as adaptive inputs rather than command signals.An applied prototype further demonstrates how interactive AI behavior can be gated based on cognitive workload and engagement, highlighting feasibility and design implications while remaining mindful of performance. The findings highlight the potential of consumer BCI for human-centered adaptation. By reframing BCI integration in terms of interaction bandwidth, this work contributes a design-oriented perspective for developing cognitively aligned next-generation human-AI systems with the Interactive AI paradigm.

Keywords: Interactive AI, BCI

DOI: 10.54941/ahfe1007399