We describe the realization of a fully-electronic label-free temperature-controlled biosensing platform aimed to overcome the Debye screening limit over a wide range of electrolyte salt concentrations. It is based on an improved version of a 90 nm CMOS integrated circuit featuring a nanocapacitor array, readout and A/D conversion circuitry, and an FPGA-based interface board with NIOS II soft processor. We describe the chip's processing, the mounting, the microfluidics, the temperature control system, as well as the calibration and compensation procedures to reduce systematic errors, which altogether make up a complete quantitative sensor platform. Capacitance spectra recorded up to 50-70 MHz are shown and successfully compared to predictions by FEM numerical simulations in the Poisson-Drift-Diffusion formalism. They demonstrate the ability of the chip to reach high upper frequency of operation, thus overcoming the low-frequency Debye screening limit at nearly physiological salt concentrations in the electrolyte, and allowing for detection of events occurring beyond the extent of the electrical double layer. Furthermore, calibrated multi-frequency measurements enable quantitative recording of capacitance spectra, whose features can reveal new properties of the analytes. The scalability of the electrode dimensions, inter-electrode pitch and size of the array make this sensing approach of quite general applicability, even in a non-bio context (e.g. gas sensing).

This paper introduces BrainFuseNet, a novel lightweight seizure detection network based on the sensor fusion of electroencephalography (EEG) with photoplethysmography (PPG) and accelerometer (ACC) signals, tailored for low-channel count wearable systems. BrainFuseNet utilizes the Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), an innovative loss function incorporating sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The BrainFuseNet-SSWCE approach successfully detects 93.5% seizure events on the CHB-MIT dataset ( 76.34% sample-based sensitivity), for EEG-based classification with only four channels. On the PEDESITE dataset, we demonstrate a sample-based sensitivity and false positive rate of 60.66% and 1.18 FP/h, respectively, when considering EEG data alone. Additionally, we demonstrate that integrating PPG signals increases the sensitivity to 61.22% (successfully detecting 92% seizure events) while decreasing the number of false positives to 1.0 FP/h. Finally, when ACC data are also considered, the sensitivity increases to 64.28% (successfully detecting 95% seizure events) and the number of false positives drops to only 0.21 FP/h for sample-based estimations, with less than one false alarm per day when considering event-based estimations. BrainFuseNet is resource-friendly and well-suited for implementation on low-power embedded platforms, and we evaluate its performance on GAP9, a state-of-the-art parallel ultra-low power (PULP) microcontroller for tiny Machine Learning applications on wearables. The implementation on GAP9 achieves an energy efficiency of 21.43 GMAC/s/W, with an energy consumption per inference of only 0.11 mJ at high performance ( 412.54 MMAC/s). The BrainFuseNet-SSWCE method demonstrates effective and accurate seizure detection on heavily imbalanced datasets while achieving state-of-the-art performance in the false positive rate and being well-suited for deployment on energy-constrained edge devices.

We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on the CHB-MIT dataset, and include explorations of different classification approaches (Support Vector Machines, Random Forest, Extra Trees, AdaBoost) and different pre/post-processing techniques to maximize sensitivity while guaranteeing no false alarms. We analyze global and subject-specific approaches, considering all 23-electrodes or only 4 temporal channels. For 8 s window size and subject-specific approach, we report zero false positives and 100% sensitivity. These algorithms are parallelized and optimized for a parallel ultra-low power (PULP) platform, enabling 300h of continuous monitoring on a 300 mAh battery, in a wearable form factor and power budget. These results pave the way for the implementation of affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patient and caregiver requirements.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance. Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h. We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.

Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusiveness, comfort, reliability, and data privacy. To address these challenges, this paper presents GAPSES, a novel smart glasses platform designed for unobtrusive, comfortable, and secure acquisition and processing of electroencephalography (EEG) and electrooculography (EOG) signals.We introduce a direct electrode-electronics interface within a sleek frame design, with custom fully dry soft electrodes to enhance comfort for long wear. The fully assembled glasses, including electronics, weigh 40 g and have a compact size of 160 mm × 145 mm. An integrated parallel ultra-low-power RISC-V processor (GAP9, Greenwaves Technologies) processes data at the edge, thereby eliminating the need for continuous data streaming through a wireless link, enhancing privacy, and increasing system reliability in adverse channel conditions. We demonstrate the broad applicability of the designed prototype through validation in a number of EEG-based interaction tasks, including alpha waves, steady-state visual evoked potential analysis, and motor movement classification. Furthermore, we demonstrate an EEG-based biometric subject recognition task, where we reach a sensitivity and specificity of 98.87% and 99.86% respectively, with only 8 EEG channels and an energy consumption per inference on the edge as low as 121 μJ. Moreover, in an EOG-based eye movement classification task, we reach an accuracy of 96.68% on 11 classes, resulting in an information transfer rate of 94.78 bit/min, which can be further increased to 161.43 bit/min by reducing the accuracy to 81.43%. The deployed implementation has an energy consumption of 40 μJ per inference and a total system power of only 12.4 mW, of which only 1.61% is used for classification, allowing for continuous operation of more than 22 h with a small 75 mAh battery.

The need for continuous monitoring of cardiorespiratory activity, blood pressure, bladder, muscle motion analysis, and more, is pushing for research and development of wearable ultrasound devices. In this context, there is a critical need for highly configurable, energy-efficient wearable ultrasound systems with wireless access to raw data and long battery life. Previous exploratory works have primarily relied on bulky commercial research systems or custom-built prototypes with limited and narrowly-focused field applicability. This paper presents TINYPROBE, a novel multi-modal wearable ultrasound platform. TINYPROBE integrates a 32-channel ultrasound RX/TX frontend, including TX beamforming (64 V _pp excitations, 16 delay profiles) and analog-to-digital conversion (up to 30 Msps, 10 bit), with a WiFi link (21.6 Mbps, UDP), for wireless raw data access, all in a compact (57 × 35 × 20 mm) and lightweight (35 g) design. Employing advanced power-saving techniques and optimized electronics design, TINYPROBE achieves a power consumption of < 1W for imaging modes (32 ch., 33 Hz) and < 1.3W for high-PRF Doppler mode (2 ch., 1400 Hz). This results in a state-of-the-art power efficiency of 44.9 mW/Mbps for wireless US systems, ensuring multi-hour operation with a compact 500 mAh Li-Po battery. We validate TINYPROBE as a versatile, general-purpose wearable platform in multiple in-vivo imaging scenarios, including muscle and bladder imaging, and blood flow velocity measurements.