ArticleFrontiers in human neuroscience2026
Single-subject auditory ERP-BCI performance enhancement in ALS via an AI coding assistant prompt.
Article in Frontiers in human neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Auditory event-related potential (ERP) brain-computer interfaces (BCIs) offer communication support for individuals with amyotrophic lateral sclerosis (ALS) who eventually progress to completely locked-in states. However, individual-specific BCI pipeline optimization is technically demanding and time-consuming, leaving substantial room for performance improvement in practice. A central challenge is increasing selection speed while maintaining reliable classification accuracy, since slower selections reduce the sense of agency and undermine the motivational and feedback dynamics essential for sustained BCI use. Methods: We investigated whether an AI coding assistant could address this challenge for individual patients. A three-class auditory ERP-BCI was optimized for a single ALS patient using Claude Code (Anthropic, Inc.), which iteratively generated and evaluated 23 optimization scripts over approximately 24 hours with minimal human-in-the-loop oversight. The resulting AI-Designed ERP classifier (AIDE) was evaluated on 189 EEG trials spanning 3.5 years using five cross-validation strategies. Results: For the baseline models, halving the stimulus repetitions to shorten selection time degraded classification accuracy; AIDE prevented this degradation, achieving 85.03% mean cross-validation accuracy (selection time 17 s; ITR 2.92 bits/min). This doubled the information transfer rate from 1.43 to 2.92 bits/min. Accuracy exceeded 84% across four of five cross-validation strategies. Feature space visualization revealed that the AI autonomously selected and combined EEG features established in prior studies into an effective discriminative architecture, without domain-specific algorithmic guidance from the human researcher. In addition, online test confirmed 66.7% accuracy for AIDE versus 50.0% for the baseline model. Discussion: These findings provide proof of concept that single-subject BCI performance can be improved via a single prompt, offering an efficient pathway to individualized optimization in clinical and research settings.
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