ArticleIEEE journal of solid-state circuits2022
NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC.
Article in IEEE journal of solid-state circuits, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Multitarget brain implants enable generalized decoding of Parkinson's disease symptoms from chronic home recordings.Research square · 2026Article
- Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces.Communications biology · 2026Article
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- An Improved Beta Burst Extraction for Chip-Based Deep Brain Stimulation With Real-Time Model Updating.IEEE open journal of engineering in medicine and biology · 2026Article
- Transcranial Temporal Interference Stimulation: A Brief Review of Architectures, Circuits, and Application Challenges.IEEE open journal of engineering in medicine and biology · 2026Article
- Real-Time Multi-Channel Epileptic Seizure Detection Exploiting an Ultra-Low-Complexity Algorithm-Hardware Co-Design Approach.Sensors (Basel, Switzerland) · 2025Article
- Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: Systematic Review of AI and Machine Learning Innovations in Biomedical Engineering.JMIR biomedical engineering · 2025Review
- Multitarget neurostimulation of the deep brain: clinical opportunities, challenges, and emerging technologies.Journal of neural engineering · 2025Review
- Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.Journal of nanobiotechnology · 2025Review
- Comparison metrics and power trade-offs for BCI motor decoding circuit design.Frontiers in human neuroscience · 2025Review
- Neural decoding and feature selection methods for closed-loop control of avoidance behavior.Journal of neural engineering · 2024Article
- Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.bioRxiv : the preprint server for biology · 2024Article
- Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.Frontiers in neuroscience · 2024Article
- 68-channel neural signal processing system-on-chip with integrated feature extraction, compression, and hardware accelerators for neuroprosthetics in 22 nm FDSOI.Frontiers in neuroscience · 2024Article
- Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges.APL bioengineering · 2023Review
- A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC.IEEE solid-state circuits letters · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227μJ/class energy efficiency in a compact area of 0.014mm
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.