A 0.21-ps FOM Capacitor-Less Analog LDO with Dual-Range Load Current for Biomedical Applications

This paper presents an output capacitor-less low-dropout regulator (LDO) with a bias switching scheme for biomedical applications with dual-range load currents. Power optimization is crucial for systems with multiple activation modes such as neural interfaces, IoT and edge devices with varying load currents. To enable rapid switching between low and high current states, a flipped voltage follower (FVF) configuration is utilized, along with a super source follower buffer to drive the power transistor. Two feedback loops and an on-chip compensation capacitor Cc maintain the stability of the regulator under various load conditions. The LDO was implemented in a 65nm CMOS process with 1.5V input and 1.2V output voltages. The measured quiescent current is as low as 3uA and 50uA for the 0-500uA and 5-15mA load current ranges, respectively. An undershoot voltage of 100mV is observed when the load current switches from 0 to 15mA within 80ns, with a maximum current efficiency of 99.98%. Our design achieved a low Figure-of-Merit of 0.21ps, outperforming state-of-the-art analog LDOs

Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to regulate seizures, or alerting patients of potential episodes. Next-generation seizure detection systems heavily rely on high-accuracy machine learning-based classifiers to detect the seizure onset. Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal. We empirically demonstrate, for the first time, that converting raw EEG signals to appropriate embeddings can significantly boost the performance of seizure detection algorithms. Importantly, we show that embedding features, which converts the raw EEG into an alternative representation, is beneficial for various machine learning models such as Logistic Regression, Multi-Layer Perceptron, Support Vector Machines, and Gradient Boosted Trees. The experiments were conducted on the CHB-MIT scalp EEG dataset. With the proposed EEG feature embeddings, we achieve significant improvements in sensitivity, specificity, and AUC score across multiple models. By employing this approach alongside an SVM classifier, we were able to attain state-of-the-art classification performance with a sensitivity of 100% and specificity of 99%, setting a new benchmark in the field

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision trees to enable resource-efficient classification on a neural implant. By integrating model compression with probabilistic routing and implementing cost-aware learning, our proposed model could significantly reduce the memory and hardware cost compared to state-of-the-art models, while maintaining the classification accuracy. We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements. On seizure detection task, we were able to reduce the model size by 3.4X and the feature extraction cost by 14.6X compared to the ensemble of boosted trees, using the intracranial EEG from 10 epilepsy patients. In a second experiment, we tested the ResOT-PE model on tremor detection for Parkinson's disease, using the local field potentials from 12 patients implanted with a deep-brain stimulation (DBS) device. We achieved a comparable classification performance as the state-of-the-art boosted tree ensemble, while reducing the model size and feature extraction cost by 10.6X and 6.8X, respectively. We also tested on a 6-class finger movement detection task using ECoG recordings from 9 subjects, reducing the model size by 17.6X and feature computation cost by 5.1X. The proposed model can enable a low-power and memory-efficient implementation of classifiers for real-time neurological disease detection and motor decoding

The Future of Intracranial EEG Recording in Epilepsy: a Technological Issue?

Intracranial EEG information used for epilepsy surgery has been provided from large widely spaced electrodes over a narrow bandwidth. However, over the last decades, research on animal and more recently on human, promoted by increased interest in developing high-density microelectrode arrays (MEA), has opened new windows for the comprehension of seizure origin and propagation at a submillimeter scale. From an electrophysiological perspective MEA demonstrate to be able to record local field potentials recordings and possibly single units in the mouse cortex. The limitations on the number of channels that can be recorded simultaneously may limit the number of microelectrodes that can be considered and consequently the extent of brain coverage. Thanks to improving microfabrication techniques, several prototypes of MEA are under development and investigation. They will certainly play an important role in the improvement of the understanding of the complicated and evolving concept of epileptogenesis and provide the development of new strategies regarding neurosurgical therapeutic issues

Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding and potentially treating mental diseases such as epilepsy and depression. While such devices exist, it is still necessary to address several challenges to make them more practical in terms of area and power dissipation.In this work, we apply Learning Based Compressive Subsampling (LBCS) to tackle the power and area trade-offs in neural wireless devices. To this end, we propose a lowpower and area-effcient system for neural signal acquisition which yields state-of-art compression rates up to 64x with high reconstruction quality, as demonstrated on two human iEEG datasets. This new fully digital architecture handles one neural acquisition channel, with an area of 210x210μm in 90nm CMOS technology, and a power dissipation of only 0:9μW

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants

Implantable systems are nowadays being used to interface the human brain with external devices, in order to understand and potentially treat neurological disorders. The most predominant design constraints are the system’s area and power. In this paper, we implement and combine advanced compressive sampling algorithms to reduce the power requirements of wireless telemetry. Moreover, we apply variable compression, to dynamically modify the device performance, based on the actual signal need. This paper presents an area-efficient adaptive system for wireless implantable devices, which dynamically reduces the power requirements yielding compression rates from 8× to 64×, with a high reconstruction performance, as qualitatively demonstrated on a human data set. Two different versions of the encoder have been designed and tested, one with and the second without the adaptive compression, requiring an area of 230×235 μm and 200 × 190 μm, respectively, while consuming only 0.47 μW at 0.8 V. The system is powered by a 4-coil inductive link with measured power transmission efficiency of 36%, while the distance between the external and internal coils is 10 mm. Wireless data communication is established by an OOK modulated narrowband and an IR-UWB transmitter, while consuming 124.2 pJ/bit and 45.2 pJ/pulse, respectively

Neural interface systems with on-device computing: machine learning and neuromorphic architectures

10.1016/j.copbio.2021.10.012Current Opinion in Biotechnology7295-10

Towards Adaptive Deep Brain Stimulation in Parkinson's Disease: Lfp-Based Feature Analysis and Classification

Deep Brain Stimulation (DBS) is an established therapy for advanced Parkinson's disease (PD). Recent studies have applied the closed-loop control (adaptive DBS or aDBS) using feedback from local field potential (LFP) signals. However, current aDBS practices focus on simple feedback like beta band power and thresholding, without optimized control or classification algorithms. In this work, we study the capacity of several classifiers including automatic shrinkage linear discriminant analysis (LDA) to predict motor impairment. We use 20 features extracted from both monopolar and bipolar LFPs in 12 PD patients. In our best setting, we achieve a median accuracy of 70.2%, sensitivity of 81.2% and prediction lead time of 0.1 s across patients. By including relevant features other than beta power, a 13.6% improvement in accuracy is achieved. Moreover, the Hjorth parameters and high-frequency oscillation (HFO) features perform best according to the Analysis of Variance (ANOVA) p-value and classifier weights. These results suggest a great potential to improve current aDBS system for PD, by implementing a classifier with multiple features

Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject. This subject-specific training scheme requires long labeled recordings from each patient, thus failing to detect symptoms in new patients with limited recordings. This paper introduces an unsupervised domain adaptation approach based on adversarial networks to enable few-shot, cross-subject epileptic seizure detection. Using adversarial learning, features from multiple patients were encoded into a subject-invariant space and a discriminative model was trained on subject-invariant features to make predictions. We evaluated this approach on the intracranial EEG (iEEG) recordings from 9 patients with epilepsy. Our approach enabled cross-subject seizure detection with a 9.4% improvement in 1-shot classification accuracy compared to the conventional subject-specific scheme