Wearable vibrotactile devices are increasingly used in virtual reality, teleoperation and neurorehabilitation, but objective EEG evaluation of glove-mediated touch remains limited. We compared EEG recorded during natural object interaction with EEG recorded when tactile feedback was reproduced through a vibrotactile smart glove. Data were collected with an eight-channel wireless headset while participants interacted with three object types (bottle, cube, and sphere) in natural-touch and glove-mediated conditions. An exploratory model trained on natural-touch data and tested on glove-mediated trials yielded rounded cross-condition accuracies of 83%, 78%, and 68% for bottle vs. rest, cube vs. rest, and sphere vs. rest, respectively. These findings suggest that some object-related EEG patterns may carry across conditions, but they should not be interpreted as evidence of physiological equivalence. Supplementary analyses using repeated-run evaluation, band-power and ERD/ERS summaries, temporal-window inspection, and channel ablation were included as cautious interpretability checks. The results underscore the need for larger subject-independent studies, stronger artifact-control pipelines, formal statistical testing, and richer haptic conditions before asserting equivalence to natural touch.

Congenital heart disease (CHD) is a major cause of infant morbidity and mortality, and timely diagnosis remains difficult where advanced imaging is not consistently available. Automated phonocardiogram (PCG) screening can support early triage, but pediatric multiclass classification is limited by subtle acoustic differences and class imbalance. This study proposes a five-class heart-sound framework (Normal, ASD, PDA, PFO, and VSD) using multi-domain feature fusion and a convolutional recurrent neural network (CRNN). A four-channel representation combining Log-Mel, PCEN-Mel, Gramian Angular Summation Field (GASF), and Markov Transition Field (MTF) is modeled with a CNN encoder, bidirectional GRU, and dual temporal pooling. Recordings are segmented with uniform 50% overlap and evaluated under a strict subject-wise protocol with macro-F1-guided model selection. Across five runs on the pediatric ZCHSound dataset, the five-class model achieves a mean macro F1-score of 78.61 ± 1.00% and a mean per-subject accuracy of 87.94 ± 0.87%. A dedicated Normal-versus-Abnormal model reaches 95.74% accuracy. These results suggest that multi-domain fusion with sequence-aware modeling is a promising approach for pediatric CHD auscultation support.