Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Neuromorphic Computing and Engineering, Journal Year: 2024, Volume and Issue: 4(2), P. 024008 - 024008

Published: April 26, 2024

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

Language: Английский

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Flexible, ultrathin bioelectronic materials and devices for chronically stable neural interfaces

Brain‐X, Journal Year: 2023, Volume and Issue: 1(4)

Published: Dec. 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.

Language: Английский

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Brain–Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments

Sovremennye tehnologii v medicine, Journal Year: 2024, Volume and Issue: 16(1), P. 78 - 78

Published: Feb. 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.

Language: Английский

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Remote Cardiac System Monitoring Using 6G-IoT Communication and Deep Learning

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: 136(1), P. 123 - 142

Published: May 1, 2024

Language: Английский

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AI for brain-computer interfaces

Developments in neuroethics and bioethics, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 28

Published: Jan. 1, 2024

Language: Английский

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Advancements Beyond Limb Loss: Exploring the Intersection of AI and BCI in Prosthetic Evaluation

Current Pharmaceutical Design, Journal Year: 2024, Volume and Issue: 30(35), P. 2749 - 2752

Published: Aug. 2, 2024

Language: Английский

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Converging Technologies for Health Prediction and Intrusion Detection in Internet of Healthcare Things With Matrix- Valued Neural Coordinated Federated Intelligence

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 99469 - 99498

Published: Jan. 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.

Language: Английский

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Recent advances in neurotechnology-based biohybrid robots

Soft Matter, Journal Year: 2024, Volume and Issue: 20(40), P. 7993 - 8011

Published: Jan. 1, 2024

This review aims to show the evolution of biohybrid robots, their key technologies, applications, and challenges. We believe that multimodal monitoring stimulation technologies holds potential enhance performance robots.

Language: Английский

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Enhancing EEG artifact removal through neural architecture search with large kernels

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102831 - 102831

Published: Sept. 28, 2024

Language: Английский

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Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition

Published: Oct. 20, 2024

Language: Английский

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