Researchers have developed a more interpretable magnetoencephalography (MEG) decoding architecture designed to retrieve perceived speech from non-invasive recordings by targeting wav2vec 2.0 audio embeddings. To improve interpretability over previous CLIP-style models, the authors replaced flattened spatial attention with spherical harmonics mapped to the 3D MEG helmet geometry and reduced subject-specific representations from 270 to 25 branches. Each branch incorporates a temporal filter to align with specific neuronal sources in space and time, while ocular and cardiac artifacts are removed to prevent stimulus-locked shortcuts. This redesigned model achieved a Top-1 accuracy of 39.75 ± 0.34% against 1,005 candidates on the MEG-MASC dataset, utilizing approximately 20 times fewer decoder parameters than previous methods. Source mapping reveals that decoder weights align with speech-perception networks, showing left-lateralized branches that carry higher-frequency rhythmic components. Through MEG occlusion experiments, the study determined that 15 of 19 stimulus features contribute to retrieval, with silence, sound intensity, vowels, and acoustic onsets exerting the strongest influence. Furthermore, the research found that while the wav2vec target can be compressed to twelve learned dimensions without sacrificing accuracy, aggressive temporal compression significantly degrades performance. Finally, the study demonstrated that narrative structure is vital, as substituting MEG activity from random word lists into narrative segments actually improved retrieval, suggesting coherent speech provides more recoverable information.