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$^3$ 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$^3$ and a rechargeable battery ($\phi$ 12x5 mm$^2$). 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 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

A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime

In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance— The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario