arXiv subjects

Meghna Roy Chowdhury

Publications and source records attributed to Meghna Roy Chowdhury.

7 recordsLinked to original sources

arXiv2026

Design-Space Exploration of Distributed Neural Networks in Low-Power Wearable Nodes

Wearable devices increasingly support intelligent applications such as augmented reality, continuous health monitoring, and vision-based interaction. However, the neural networks (NNs) required by these applications often exceed the memory and energy budgets of wearable nodes. Cloud offloading reduces local computation but increases communication demand and can introduce latency and privacy concerns. TinyML reduces model complexity, potentially sacrificing accuracy without resolving the memory demands of vision workloads. This paper presents DistNN, a hardware-software framework for distributed NN inference that partitions execution between an ultra-low-power (ULP) wearable node and a nearby resource-rich hub, such as a smartphone. The node processes early NN layers and sends intermediate feature maps to the hub, which executes the remaining layers. To choose the split point, we propose a platform-independent figure of merit (FoM) based on data volume (DV) and cumulative computation cost (CC). The FoM captures the central trade-off: earlier splits reduce node computation but increase communication, whereas later splits reduce transmitted data but increase node computation. We also derive conditions for a net node-side energy benefit from distribution and demonstrate that the target autoencoder (AE) workloads cannot run monolithically on representative wearable microcontrollers. To enable ULP execution, we synthesize a custom node using low-precision fixed-point arithmetic. Post-synthesis estimates place DistNN below the 10 mW node-power target, with average node-compute power approximately 11x lower than that of the recent ML ASIC baselines evaluated. Across convolutional NNs and AEs, DistNN preserves task quality, achieving a structural similarity index (SSIM) of approximately 0.90 for image reconstruction with low-precision node features and a resource-rich hub decoder.

cs.ET↗

arXiv2026

BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classification

The growing use of electroencephalography (EEG) motivates automated analysis that is accurate, transferable, and deployable on constrained hardware. Recent EEG foundation models learn general representations from large-scale pretraining, but their size and computational cost limit edge and wearable deployment. We introduce BRIDGE-EEG, an efficient multi-task EEG classification pipeline that preserves the benefits of pretraining while reducing model size. A unified preprocessing scheme maps heterogeneous recordings with different channel counts, montages, and sampling rates to a device-agnostic 62-channel time--frequency representation. We pretrain an SE-ResNet18 teacher (11.84 M parameters) with SimCLR on unlabeled EEG from five heterogeneous datasets, then compress it into SE-ResNet8 (1.56 M) and SE-ResNet4 (0.48 M) students using task-agnostic and task-specific distillation. We evaluate six benchmarks spanning abnormality detection, motor imagery, and emotion recognition. For abnormality detection and emotion recognition, the students achieve accuracy comparable to or better than several recent EEG foundation models with 10--1,000$\times$ more parameters. Motor imagery shows a remaining representation gap, highlighting the importance of pretraining diversity. Inference profiling on a server GPU, desktop CPU, and NVIDIA Jetson Orin Nano shows up to 3.0$\times$ lower edge energy per inference (15.64 mJ vs. 46.67 mJ). The compact models further support future deployment on MCU-class wearables.

cs.HC↗

arXiv2026

From Muscle to Text with MyoText: sEMG to Text via Finger Classification and Transformer-Based Decoding

Surface electromyography (sEMG) provides a direct neural interface for decoding muscle activity and offers a promising foundation for keyboard-free text input in wearable and mixed-reality systems. Previous sEMG-to-text studies mainly focused on recognizing letters directly from sEMG signals, forming an important first step toward translating muscle activity into text. Building on this foundation, we present MyoText, a hierarchical framework that decodes sEMG signals to text through physiologically grounded intermediate stages. MyoText first classifies finger activations from multichannel sEMG using a CNN-BiLSTM-Attention model, applies ergonomic typing priors to infer letters, and reconstructs full sentences with a fine-tuned T5 transformer. This modular design mirrors the natural hierarchy of typing, linking muscle intent to language output and reducing the search space for decoding. Evaluated on 30 users from the emg2qwerty dataset, MyoText outperforms baselines by achieving 85.4% finger-classification accuracy, 5.4% character error rate (CER), and 6.5% word error rate (WER). Beyond accuracy gains, this methodology establishes a principled pathway from neuromuscular signals to text, providing a blueprint for virtual and augmented-reality typing interfaces that operate entirely without physical keyboards. By integrating ergonomic structure with transformer-based linguistic reasoning, MyoText advances the feasibility of seamless, wearable neural input for future ubiquitous computing environments.

cs.LG↗

arXiv2025

SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks

Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise artifacts, missing data, and high annotation costs. We introduce SSL-SE-EEG, a framework that integrates Self-Supervised Learning (SSL) with Squeeze-and-Excitation Networks (SE-Nets) to enhance feature extraction, improve noise robustness, and reduce reliance on labeled data. Unlike conventional EEG processing techniques, SSL-SE-EEG} transforms EEG signals into structured 2D image representations, suitable for deep learning. Experimental validation on MindBigData, TUH-AB, SEED-IV and BCI-IV datasets demonstrates state-of-the-art accuracy (91% in MindBigData, 85% in TUH-AB), making it well-suited for real-time BCI applications. By enabling low-power, scalable EEG processing, SSL-SE-EEG presents a promising solution for biomedical signal analysis, neural engineering, and next-generation BCIs.

eess.SP↗

arXiv2025

Predicting and Understanding College Student Mental Health with Interpretable Machine Learning

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning models lacking transparency, and the tendency of existing approaches to provide aggregated insights at the population level rather than individualized understanding. To tackle these challenges, this paper presents I-HOPE, the first Interpretable Hierarchical mOdel for Personalized mEntal health prediction. I-HOPE is a two-stage hierarchical model that connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. We evaluate I-HOPE on the College Experience Study, the longest longitudinal mobile sensing dataset. This dataset spans five years and captures data from both pre-pandemic periods and the COVID-19 pandemic. I-HOPE achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, I-HOPE distills complex patterns into interpretable and individualized insights, enabling the future development of tailored interventions and improving mental health support. The code is available at https://github.com/roycmeghna/I-HOPE.

cs.LG↗

arXiv2025

A Computational Harmonic Detection Algorithm to Detect Data Leakage through EM Emanation

Unintended electromagnetic emissions, called EM emanations, can be exploited to recover sensitive information, posing security risks. Metal shielding, used by defense organizations to prevent data leakage, is costly and impractical for widespread use. This issue is particularly significant for IoT devices due to their sheer volume and varied deployment environments. Therefore, there is a research need for an automated detection method to monitor facilities and address data leakage promptly. To resolve this challenge, in the preliminary version of this work [1], a CNN-based detection method was proposed using HDMI cable emanations that provided ~95% accuracy up to 22.5 m but had limitations due to training data. In this extended version, we augment the initial study by collecting and characterizing emanation data from IoT devices, everyday electronics, and cables. We propose a harmonic-based emanation detection method by developing a computational harmonic detection algorithm. The proposed method addresses the limitations of the CNN-based method and provides ~100% accuracy not only for HDMI emanation (compared to ~95% in the earlier CNN method) but also for all other tested devices and cables. Finally, it has also been tested in different environments to prove its efficacy in practical scenarios.

cs.CR↗

arXiv2025

Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors

The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among young adults, while offering numerous benefits, has also been linked to negative outcomes such as addiction and regret, significantly impacting well-being. Leveraging the longest longitudinal dataset collected over four college years through passive mobile sensing, this study is the first to examine the relationship between students' smartphone unlocking behaviors and their mental health at scale in real-world settings. We provide the first evidence demonstrating the predictability of phone unlocking behaviors for mental health outcomes based on a large dataset, highlighting the potential of these novel features for future predictive models. Our findings reveal important variations in smartphone usage across genders and locations, offering a deeper understanding of the interplay between digital behaviors and mental health. We highlight future research directions aimed at mitigating adverse effects and promoting digital well-being in this population.

cs.CY↗