TangentFuse: Low-Latency MEG Speech Activity Detection via Riemannian Covariance - CNN Fusion
TL;DR
A hybrid MEG speech/non-speech classifier that combines a geometry-aware covariance branch with temporal neural streams and a shrinkage covariance matrices and map them to a Riemannian tangent space around a reference mean is described.
Abstract
Speech activity recognition in MEG-based non-invasive BCI systems provides a reliable speech gate that can trigger downstream decoders only when speech-related neural activity is present. Such a gate can help users interact with assistive devices in continuous settings. While MEG provides excellent temporal resolution, many MEG speech activity classifiers do not fully exploit available spatial information. We describe a hybrid MEG speech/non-speech classifier that combines a geometry-aware covariance branch with temporal neural streams. We compute shrinkage covariance matrices (SPD) and map them to a Riemannian tangent space around a reference mean; a logistic regression classifier operates on these features. A limited sensor array defines the region-of-interest component, while the final system adds a residual temporal fusion layer over aligned probability streams. The final decision rule, including fusion weights, thresholds, and sequence-level post-processing, is selected on validation only and then applied unchanged to frozen test. On a large within-subject MEG corpus, the final system achieved validation macro-F1 of 0.8972 and frozen-test macro-F1 of 0.8914. This provides a compute-efficient research prototype for MEG-based speech gating.