But do we need high bandwidth? Applications and scaling challenges of invasive brain-computer interfaces

Invasive brain-computer interfaces (iBCIs) have expanded from single to thousands of channels, primarily driven by the goal to restore autonomy and social participation for people with severe neurological impairment. This article evaluates whether this increase in bandwidth (here, the aggregate neural data stream) aligns with clinical benefit or yields diminishing returns against rising challenges. The application landscape reveals that performance typically improves with rising channel count. However, the performance curve also depends on other factors such as task complexity, the evaluation metric, spatial redundancy, and decoder capacity. For today's clinical goals (reliable communication and functional motor restoration), moderate bandwidth already suffices when coupled with model-based priors, structured output spaces, and shared-control architectures; next-horizon goals, e.g. unconstrained natural speech, embodied dexterity, and cognitive restoration, however, require abundant sampling but remain constrained by biological, technical, and ethical hurdles, with the engineering trilemma of bandwidth, power, and latency as the primary bottleneck for fully implantable systems. Solving this requires a shift towards low-power on-implant processing to handle increasing neural datastreams. Looking forward, the field is increasingly orienting toward solutions that balance risk and resolution. Large-scale micro-electrocorticography (µECoG) arrays represent such an approach and complement intracortical strategies, aiming to resolve the long-standing trade-off between invasiveness and bandwidth in clinically viable iBCIs.