45 research outputs found
Investigation and Development of CMOS Pixelated Nanocapacitor Biosensors for Quantitative Analyses
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology
Brain-machine interfaces (BMIs) have emerged as a transformative force in
assistive technologies, empowering individuals with motor impairments by
enabling device control and facilitating functional recovery. However, the
persistent challenge of inter-session variability poses a significant hurdle,
requiring time-consuming calibration at every new use. Compounding this issue,
the low comfort level of current devices further restricts their usage. To
address these challenges, we propose a comprehensive solution that combines a
tiny CNN-based Transfer Learning (TL) approach with a comfortable, wearable EEG
headband. The novel wearable EEG device features soft dry electrodes placed on
the headband and is capable of on-board processing. We acquire multiple
sessions of motor-movement EEG data and achieve up to 96% inter-session
accuracy using TL, greatly reducing the calibration time and improving
usability. By executing the inference on the edge every 100ms, the system is
estimated to achieve 30h of battery life. The comfortable BMI setup with tiny
CNN and TL paves the way to future on-device continual learning, essential for
tackling inter-session variability and improving usability
BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing
Wearable biosignal processing applications are driving significant progress
toward miniaturized, energy-efficient Internet-of-Things solutions for both
clinical and consumer applications. However, scaling toward high-density
multi-channel front-ends is only feasible by performing data processing and
machine Learning (ML) near-sensor through energy-efficient edge processing. To
tackle these challenges, we introduce BioGAP, a novel, compact, modular, and
lightweight (6g) medical-grade biosignal acquisition and processing platform
powered by GAP9, a ten-core ultra-low-power SoC designed for efficient
multi-precision (from FP to aggressively quantized integer) processing, as
required for advanced ML and DSP. BioGAPs form factor is 16x21x14 mm and
comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless
Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an
accelerometer; and a shield including an analog front-end (AFE) for ExG
acquisition. Finally, the system also includes a flexibly placeable
photoplethysmogram (PPG) PCB with a size of 9x7x3 mm and a rechargeable
battery ( 12x5 mm). We demonstrate BioGAP on a Steady State Visually
Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We
achieve 3.6 uJ/sample in streaming and 2.2 uJ/sample in onboard processing
mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW
with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW
allowing for an operation time of 15 h.Comment: 7 pages, 9 figures, 1 table, accepted for IEEE COINS 202
A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime
A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring
Surface electromyography (sEMG) is a well-established approach to monitor
muscular activity on wearable and resource-constrained devices. However, when
measuring deeper muscles, its low signal-to-noise ratio (SNR), high signal
attenuation, and crosstalk degrade sensing performance. Ultrasound (US)
complements sEMG effectively with its higher SNR at high penetration depths. In
fact, combining US and sEMG improves the accuracy of muscle dynamic assessment,
compared to using only one modality. However, the power envelope of US hardware
is considerably higher than that of sEMG, thus inflating energy consumption and
reducing the battery life. This work proposes a wearable solution that
integrates both modalities and utilizes an EMG-driven wake-up approach to
achieve ultra-low power consumption as needed for wearable long-term
monitoring. We integrate two wearable state-of-the-art (SoA) US and ExG
biosignal acquisition devices to acquire time-synchronized measurements of the
short head of the biceps. To minimize power consumption, the US probe is kept
in a sleep state when there is no muscle activity. sEMG data are processed on
the probe (filtering, envelope extraction and thresholding) to identify muscle
activity and generate a trigger to wake-up the US counterpart. The US
acquisition starts before muscle fascicles displacement thanks to a triggering
time faster than the electromechanical delay (30-100 ms) between the
neuromuscular junction stimulation and the muscle contraction. Assuming a
muscle contraction of 200 ms at a contraction rate of 1 Hz, the proposed
approach enables more than 59% energy saving (with a full-system average power
consumption of 12.2 mW) as compared to operating both sEMG and US continuously.Comment: 4 pages, 5 figures, 1 table, 2023 IEEE International Ultrasonics
Symposiu
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
Epilepsy is a prevalent neurological disorder that affects millions of
individuals globally, and continuous monitoring coupled with automated seizure
detection appears as a necessity for effective patient treatment. To enable
long-term care in daily-life conditions, comfortable and smart wearable devices
with long battery life are required, which in turn set the demand for
resource-constrained and energy-efficient computing solutions. In this context,
the development of machine learning algorithms for seizure detection faces the
challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new
lightweight seizure detection network, and Sensitivity-Specificity Weighted
Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and
specificity, to address the challenge of heavily unbalanced datasets. The
proposed EpiDeNet-SSWCE approach demonstrates the successful detection of
91.16% and 92.00% seizure events on two different datasets (CHB-MIT and
PEDESITE, respectively), with only four EEG channels. A three-window majority
voting-based smoothing scheme combined with the SSWCE loss achieves 3x
reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for
implementation on low-power embedded platforms, and we evaluate its performance
on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power
(PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves
an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference
of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best
ARM Cortex-based solutions by approximately 160x in energy efficiency. The
EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection
performance on heavily imbalanced datasets, while being suited for
implementation on energy-constrained platforms.Comment: 5 pages, 4 tables, 1 figure, Accepted at BioCAS 202
Non metric study on the validity of homo heidelbergensis
Includes bibliographical references (pages [89]-101).M.A. (Master of Arts
Calibration of High-Frequency Impedance Spectroscopy Measurements with Nanocapacitor Arrays
High frequency impedance spectroscopy (HFIS) biosensors based on nano-electrode arrays (NEA)
demonstrated the capability to overcome the screening limits set by the Electrical Double Layer
(EDL), thus enabling label-free detection and imaging of analytes far above the sensor surface
[1,2]. In order to achieve quantitatively accurate results, a precise understanding and modeling of
the signal transduction chain is necessary. With reference to the CMOS array platform in [1],
capacitance is measured by CBCM. Hence, the nanoelectrodes are alternatively charged and
discharged by two switch transistors (Fig.1, a), which are activated by non-overlapping clocks with
typically 1 ns floating time between the two phases. The column readout circuits integrate and
average over multiple cycles the charging current to obtain a capacitance information. The output
signal is interpreted in terms of a switching capacitance (CSW), modeled by charge-pump analysis
of an equivalent C-RC circuit excited by a square wave (EDL capacitance CS in series to a parallel
RECE representing the bulk electrolyte [1]; CS, RE and CE are extracted with the biosensor simulator
ENBIOS [3]), good agreement is obtained between experiments and simulations over a broad
range of frequencies and electrolyte salt concentrations [1]. Residual discrepancies, however,
require explanation and this is the main contribution of our abstract. To this end, we firstly, consider
the role of leakage currents (ILEAK) in the sensor cell (due to subthreshold conduction of the inactive
switch). The leakage current implies overestimating the column current IM (and hence the
capacitance). Due to the large number of cells connected on each column, a value as large as
20pA is estimated for ILEAK, and measurements are corrected by compensating for it. Then, we
consider the voltage waveforms at the nanoelectrode, as obtained by Spice simulations with
Predictive Technology Models (PTM) of the sensor cell readout circuit (Fig.1 (b) for a 10mM
electrolyte). Charge repartition between the nanoelectrode\u2019s node and CGS/CGD capacitance of the
switching transistors during the float time distorts the otherwise square-waveform. For electrolytes
with high salt concentration this effect is mitigated (due to the larger load capacitance). To account
for this effect, we extract the harmonic content of the waveform by Fourier expansion of the
waveform (Fig.1, b). Then, ENBIOS simulations at all harmonic frequencies are used to reconstruct
the capacitance response to the actual waveform (CF). Fig.1 (c) compares experiments (corrected
for leakage) and simulations (CSW or CF). The impact of leakage is modest, whereas CF exhibits an
improved agreement with experiments at high frequency, where waveform glitches are more
relevant. These corrections highlight the importance of leakage and harmonic content of the input
waveforms to achieve quantitatively accurate interpretation of NEA HFIS biosensor experiments.
Further work is necessary to extend these results to electrolytes with physiological salinity
On the Response of Nanoelectrode Impedance Spectroscopy Measures to Plant, Animal, and Human Viruses
A simplified lumped geometrical and electrical model for the high-frequency impedance spectroscopy (HFIS) response of nanoelectrodes to T=3 capsids and full viruses is developed starting from atomistic descriptions, in order to test the theoretical response of a realistic HFIS CMOS biosensor platform to different viruses. Capacitance spectra are computed for plant (cowpea chlorotic mottle virus), animal (rabbit haemorrhagic disease virus), and human (hepatitis A virus) viruses. A few common features of the spectra are highlighted, and the role of virus charge, pH, and ionic strength on the expected signal is discussed. They suggest that the frequency of highest sensitivity at nearly physiological concentrations (100 mM) is within reach of existing HFIS platform designs