MENDR: Manifold Explainable Neural Data Representations

Foundation models for electroencephalography (EEG) signals have recently demonstrated success in learning generalized representations of EEGs, outperforming specialized models in various downstream tasks. However, many of these models lack transparency in their pretraining dynamics and offer limited insight into how well EEG information is preserved within their embeddings. For successful clinical integration, EEG foundation models must ensure transparency in pretraining, downstream fine-tuning, and the interpretability of learned representations. Current approaches primarily operate in the temporal domain, overlooking advancements in digital signal processing that enable the extraction of deterministic and traceable features, such as wavelet-based representations. We propose MENDR (Manifold Explainable Neural Data Representations), a filter bank-based EEG foundation model built on a novel Riemannian Manifold Transformer architecture to resolve these issues. MENDR learns symmetric positive definite matrix embeddings of EEG signals and is pretrained on a large corpus comprising over 4,000 hours of EEG data, decomposed via discrete wavelet packet transforms into multi-resolution coefficients. MENDR significantly enhances interpretability by visualizing symmetric positive definite embeddings as geometric ellipsoids and supports accurate reconstruction of EEG signals from learned embeddings. Evaluations across multiple clinical EEG tasks demonstrate that MENDR achieves near state-of-the-art performance with substantially fewer parameters, underscoring its potential for efficient, interpretable, and clinically applicable EEG analysis.

Monitoring Health Using IoT and Thingspeak

Quality of health is greatly impacted by the quality of measurement, hence active monitoring of patient's vitals status greatly affects recovery and improves prognosis. In this paper, we proposed an IoT health based monitoring system which comprises of a wearable wristband mounted on the patient wrist to eliminate the concept of patient's being static. The wearable wristband composed of a digital temperature sensor and a pulse rate sensor can remotely monitor health status and utilizing Thingspeak result viewed via a web app or delivered on a smartphone to a specialist who is in a remote location. In order to achieve high accuracy in readings, the signal measured from the pulse rate sensor was passed through a two stage Hp-Lp circuit to eliminate DC offset and noise. In addition, a scaling factor of 10.41909 was formulated and used to convert the digital output from the pulse rate sensor into a more accurate representation of the patient heat rate readings. The results displayed by the proposed system proved the system to be well suited and reliable for healthcare monitoring.