Mantra recitation is a phonetic-cognitive practice that is thought to engage auditory, phonological, attentional, and motor networks in a graded way across three traditional modes of engagement — passive listening (Shravana), vocal chanting (Vachika Japa), and silent mental recitation (Manasa Japa). Because no synchronized human EEG–fNIRS corpus for the Vishnu Sahasranama (a thousand-name Sanskrit hymn) was available to us, we test the feature-level statistical and classification stages of a proposed multimodal acquisition-and-analysis pipeline on a physiologically parameterized synthetic benchmark dataset rather than on real human recordings; every quantitative result reported below therefore describes the behaviour of the computational pipeline on simulated data, not an empirical finding about human neurocognition.

A 90-synthetic-subject, within-subject benchmark was generated for a three-condition protocol (Listening, Vocal Chanting, Silent Recitation), with EEG (19-channel, 5-band Welch power) and fNIRS (204-channel ΔHbO/ΔHbR) features simulated jointly per synthetic subject from a documented generative model (subject random-effect plus condition effect plus measurement noise; fixed random seed; full parameters and code in the Supplementary Material), yielding a 503-dimensional fused feature vector per condition epoch. Condition effects were compared with repeated-measures ANOVA and Bonferroni-corrected paired-sample post-hoc tests, and three-class discrimination (Listening vs. Vocal Chanting vs. Silent Recitation) was tested with Random Forest and SVM classifiers under subject-grouped, stratified 5-fold cross-validation, separately for EEG-only, fNIRS-only, and fused features.

In the synthetic dataset, repeated-measures ANOVA showed significant condition effects for frontal theta (F(2,178) = 26.06, p < 0.001, partial η ² = 0.226), alpha (F = 46.06, p < 0.001, η ² = 0.341), beta (F = 216.84, p < 0.001, η ² = 0.709), and gamma (F = 19.62, p < 0.001, η ² = 0.181), but not delta (F = 1.45, p = 0.237, ns). Beta power was selectively elevated during Vocal Chanting only (Listening vs. Silent Recitation was non-significant, p = 0.655). Simulated prefrontal HbO differed strongly by condition (F = 335.29, p < 0.001, η ² = 0.790), with a non-monotonic hierarchy of Vocal Chanting (0.0548 ± 0.0128 mM) > Silent Recitation (0.0361 ± 0.0077 mM) > Listening (0.0251 ± 0.0056 mM). Three-class classification on the fused 503-dimensional feature set reached 97.0% (Random Forest) and 95.9% (SVM) mean cross-validated accuracy, a 3.0–2.2 percentage-point gain over the better single-modality model, with prefrontal HbO/HbR features contributing the largest share of Random-Forest discriminative importance (83%).

Within this synthetic benchmark, the proposed multimodal pipeline recovers internally consistent, condition-specific electrophysiological and hemodynamic patterns and supports above-chance, fusion-benefiting classification of the three engagement modes. These results validate only the feature-level analysis stages (statistics and classification), not the raw-signal acquisition or preprocessing stages, and motivate — but do not yet constitute — human neurocognitive evidence; a human EEG–fNIRS cohort study is required before any claim about actual mantra-chanting neurophysiology can be made.