Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset

• URL: http://arxiv.org/abs/2510.21038v2
• Date: Thu, 30 Oct 2025 10:23:32 GMT
• Title: Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset
• Authors: Gereon Elvers, Gilad Landau, Oiwi Parker Jones,
• Abstract summary: Keywords Spotting (KWS) is a privacy-aware intermediate task for brain-computer interfaces.<n>We release an updated version of the pnpl library with word-level dataloaders and Colab-ready tutorials.
• Score: 1.497166779417398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Non-invasive brain-computer interfaces (BCIs) are beginning to benefit from large, public benchmarks. However, current benchmarks target relatively simple, foundational tasks like Speech Detection and Phoneme Classification, while application-ready results on tasks like Brain-to-Text remain elusive. We propose Keyword Spotting (KWS) as a practically applicable, privacy-aware intermediate task. Using the deep 52-hour, within-subject LibriBrain corpus, we provide standardized train/validation/test splits for reproducible benchmarking, and adopt an evaluation protocol tailored to extreme class imbalance. Concretely, we use area under the precision-recall curve (AUPRC) as a robust evaluation metric, complemented by false alarms per hour (FA/h) at fixed recall to capture user-facing trade-offs. To simplify deployment and further experimentation within the research community, we are releasing an updated version of the pnpl library with word-level dataloaders and Colab-ready tutorials. As an initial reference model, we present a compact 1-D Conv/ResNet baseline with focal loss and top-k pooling that is trainable on a single consumer-class GPU. The reference model achieves approximately 13x the permutation baseline AUPRC on held-out sessions, demonstrating the viability of the task. Exploratory analyses reveal: (i) predictable within-subject scaling - performance improves log-linearly with more training hours - and (ii) the existence of word-level factors (frequency and duration) that systematically modulate detectability.

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