Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces DOI Creative Commons
Paul Hueber, Guangzhi Tang, Manolis Sifalakis

и другие.

Опубликована: Ноя. 13, 2023

<p>Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency.</p> <p>Previous benchmarks have provided limited insights into efficiency latency. This paper, however, introduces algorithmic metrics that capture potential limitations of neural decoders intra-cortical brain-computer interfaces in context hardware constraints. study common decoding methods predicting primate’s finger kinematics from motor cortex, explores suitability compute decoding. The finds ANN-based provide superior accuracy, many operations decode signals effectively. Spiking networks emerge as solution, bridging this gap by achieving competitive performance within sub-10ms while utilizing fraction computational resources.</p> <p>These distinctive advantages neuromorphic spiking networks, positions them highly suitable challenging environment modulation. Their capacity balance accuracy offers immense reshaping landscape decoders, fostering greater understanding, opening new frontiers intracortical human-machine interaction.</p>

Язык: Английский

Electrode Arrays for Detecting and Modulating Deep Brain Neural Information in Primates: a Review DOI Creative Commons
Siyu Zhang, Yilin Song,

Shiya Lv

и другие.

Cyborg and Bionic Systems, Год журнала: 2025, Номер 6

Опубликована: Янв. 1, 2025

Primates possess a more developed central nervous system and higher level of intelligence than rodents. Detecting modulating deep brain activity in primates enhances our understanding neural mechanisms, facilitates the study major diseases, enables brain–computer interactions, supports advancements artificial intelligence. Traditional imaging methods such as magnetic resonance imaging, positron emission computed tomography, scalp electroencephalogram are limited spatial resolution. They cannot accurately capture signals from individual neurons. With progress microelectromechanical systems other micromachining technologies, single-neuron detection stimulation technology rodents based on microelectrodes has made important progress. However, compared with rodents, human nonhuman have larger volume that needs deeper implantation depth, test object safety device preparation requirements. Therefore, high-resolution devices suitable for long-term brains urgently needed. This paper reviewed electrode array used electrophysiological electrochemical detections primates’ brains. The research recording technologies was introduced perspective type structures, their potential value neuroscience clinical disease treatments discussed. Finally, it is speculated future electrodes will lot room development terms flexibility, high resolution, brain, throughput. improvements forms process expand activities, bring new opportunities challenges further neuroscience.

Язык: Английский

Процитировано

0
Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces DOI Creative Commons

Yun-Ting Kuo,

Han-Lin Wang,

Bo-Wei Chen

и другие.

APL Bioengineering, Год журнала: 2025, Номер 9(2)

Опубликована: Апрель 16, 2025

Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals the implantable brain machine interface (iBMI) applications. Effective methods for imputing degraded or missing are essential to restore neural integrity, thereby improving accuracy system robustness over long-term recordings with fluctuating quality. This study introduces confidence-weighted Bayesian linear regression (CW-BLR) approach impute affected by degradation, enhancing consistency of decoding. The CW-BLR was compared traditional methods—mean imputation (Mean-imp) Gaussian-mixture-model-based expectation–maximization (GMM-EM)—using kernel-sliced inverse (kSIR) decoder evaluate outcomes. Four Wistar rats were trained perform forelimb-reaching task while (MUA LFPs) recorded 27 days. imputed during days 8–27. Decoding evaluated kSIR Mean-imp GMM-EM. demonstrated superior effectively preserving both temporal spatial dependencies within signals. CW-BLR-imputed data significantly improved methods, showing consistently higher performance, particularly quality from period. offers robust effective framework iBMI applications, addressing challenges accurate prolonged recordings. By utilizing confidence-based metrics, surpasses providing across scenarios.

Язык: Английский

Процитировано

0
A Monolithic Neuromorphic Device for In-Sensor Tactile Computing DOI
Yi Du, Yang Lu, Jiangdong Gong

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 5312 - 5320

Опубликована: Май 20, 2025

To emulate the tactile perception of human skin, integration sensors with neuromorphic devices has emerged as a promising approach to achieve near-sensor information processing. Here, we present monolithic electronic device that seamlessly integrates and computing functionalities within single architecture, synaptic plasticity directly tunable by inputs. This unique capability stems from our engineered structure employing SnO2 nanowires conductive channel coupled pressure-sensitive chitosan layer ionic gating layer. The demonstrates pressure-dependent memory retention learning behaviors, effectively mimicking enhanced cognitive functions observed in humans under stressful conditions. Furthermore, integrated design exhibits potential for implementing bioinspired systems requiring adaptive

Язык: Английский

Процитировано

0
Enhanced visibility graph for EEG classification DOI Creative Commons
Asma Belhadi, Pedro G. Lind, Youcef Djenouri

и другие.

Frontiers in Neuroscience, Год журнала: 2025, Номер 19

Опубликована: Май 27, 2025

Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions. In this paper, we propose an end-to-end framework EEG classification that integrates power spectral density (PSD) visibility graph (VG) features together deep learning (DL) techniques. Our offers a holistic approach capturing both frequency-domain characteristics temporal dynamics of signals. We evaluate four DL architectures, namely multilayer perceptron (MLP), long short-term memory (LSTM) networks, InceptionTime ChronoNet, applied to several datasets in different experimental Results demonstrate the efficacy our accurately classifying data, particular when using VG features. also shed new light on relative strengths limitations feature extraction methods architectures context classification. work contributes advancing analysis facilitating development more accurate reliable EEG-based systems neuroscience beyond. The full code research is available https://github.com/asmab89/VisibilityGraphs.git .

Язык: Английский

Процитировано

0
Flexible, ultrathin bioelectronic materials and devices for chronically stable neural interfaces DOI Creative Commons
Lianjie Zhou, Zhongyuan Wu,

Mubai Sun

и другие.

Brain‐X, Год журнала: 2023, Номер 1(4)

Опубликована: Дек. 1, 2023

Abstract Advanced technologies that can establish intimate, long‐lived functional interfaces with neural systems have attracted increasing interest due to their wide‐ranging applications in neuroscience, bioelectronic medicine, and the associated treatment of neurodegenerative diseases. A critical challenge significance remains development electronic platforms offer conformal contact soft brain tissue for sensing or stimulation activities chronically stable operation vivo, at scales range from cellular‐level resolution macroscopic areas. This review summarizes recent advances this field, an emphasis on use demonstrated concepts, constituent materials, engineered designs, system integration address current challenges. The article begins overview unique form factors, ranging filamentary probes sheets three‐dimensional frameworks alleviating mechanical mismatch between interface materials tissues. Next, active which utilize inorganic/organic semiconductor‐enabled devices are reviewed, highlighting various working principles recording mechanisms including capacitively conductively coupled enabled by high transistor matrices spatiotemporal resolution. subsequent section presents approaches biological multiplexed addressing, local amplification multimodal high‐channel‐count large‐scale a safe fashion provides multi‐decade performance both animal models human subjects. summarized will guide future direction technology provide basis next‐generation chronic high‐performance operation.

Язык: Английский

Процитировано

7
Brain–Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments DOI Open Access
О. А. Мокиенко

Sovremennye tehnologii v medicine, Год журнала: 2024, Номер 16(1), С. 78 - 78

Опубликована: Фев. 28, 2024

Brain-computer interfaces allow the exchange of data between brain and an external device, bypassing muscular system. Clinical studies invasive brain-computer interface technologies have been conducted for over 20 years. During this time, there has a continuous improvement approaches to neuronal signal processing in order improve quality control devices. Currently, with intracortical implants completely paralyzed patients robotic limbs self-service, use computer or tablet, type text, reproduce speech at optimal speed. Studies regularly provide new fundamental on functioning central nervous In recent years, breakthrough discoveries achievements annually made sphere. This review analyzes results clinical experiments implants, provides information stages technology development, its main achievements.

Язык: Английский

Процитировано

2
AI for brain-computer interfaces DOI
David Haslacher, Tugba Basaran Akmazoglu, Amanda van Beinum

и другие.

Developments in neuroethics and bioethics, Год журнала: 2024, Номер unknown, С. 3 - 28

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

2
Remote Cardiac System Monitoring Using 6G-IoT Communication and Deep Learning DOI
Abdulbasid S. Banga, Mohammed M. Alenazi, Nisreen Innab

и другие.

Wireless Personal Communications, Год журнала: 2024, Номер 136(1), С. 123 - 142

Опубликована: Май 1, 2024

Язык: Английский

Процитировано

2
Enhancing EEG artifact removal through neural architecture search with large kernels DOI
Le Wu, Aiping Liu, Chang Li

и другие.

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102831 - 102831

Опубликована: Сен. 28, 2024

Язык: Английский

Процитировано

2
Converging Technologies for Health Prediction and Intrusion Detection in Internet of Healthcare Things With Matrix- Valued Neural Coordinated Federated Intelligence DOI Creative Commons
Sarah A. Alzakari, Arindam Sarkar, Mohammad Zubair Khan

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 99469 - 99498

Опубликована: Янв. 1, 2024

This paper introduces Matrix-Valued Neural Coordinated Federated Deep Extreme Machine Learning, a novel approach for enhancing health prediction and intrusion detection on the Internet of Healthcare Things (IoHT). By leveraging Learning (ML), blockchain, Intrusion Detection Systems (IDS), this technique ensures security medical data while enabling predictive analytics. The IoHT, characterized by its vast network interconnected devices, poses significant challenges in confidentiality. However, integration proposed empowers healthcare systems to proactively address these concerns patient outcomes reducing costs. Smart healthcare, enabled ML is revolutionizing 5.0. may employ IoHTs' intelligent interactive characteristics using approach. Despite benefits, aggregation security, ownership, regulatory compliance challenges. (FL) key distributed learning that protects data. architecture has several unique benefits like 1) it provides thorough examination incorporation blockchain technology with FL 5.0; 2) takes lead creating robust monitoring system utilizes IDS identify prevent harmful actions; 3) development crucial elements means mutual neuronal synchronization Artificial Networks (ANNs) showcases pioneering progress safeguarding data; 4) framework underwent empirical assessment outperformed existing methods accurately predicting disease prediction, achieving an impressive efficiency rate 97.75% 98% respectively. represents major step forward improving abilities within IoHT ecosystem, offering potential revolutionary advancements delivery care.

Язык: Английский

Процитировано

1