ReviewIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2017
Decoding Local Field Potentials for Neural Interfaces.
Review in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 80 citations in OpenAlex.
- Decoding instrumental lever pressing from prefrontal, accumbens, and hippocampal local field potentials: conservation across sex, task, and dopamine depletion.bioRxiv : the preprint server for biology · 2026Article
- Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.Nature biomedical engineering · 2026Article
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Parkinson's disease severity is encoded by non-linear oscillatory interactions across cortical-subcortical networks.Scientific reports · 2026Article
- Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces.Communications biology · 2026Article
- LFP-LOC: an LFP power-based method for validating the anatomical placement of high-density neural probes in rodents.Frontiers in neuroscience · 2026Article
- Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.APL bioengineering · 2025Article
- Article
- Classification of Odor-Derived Electroantennograms with Machine Learning.Integrative organismal biology (Oxford, England) · 2025Article
- Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling.Advances in neural information processing systems · 2025Article
- Single-trial detection of auditory cues from the rat brain using memristors.Science advances · 2024Article
- Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus.Journal of neural engineering · 2024Article
- Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings.Nature communications · 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
- Article
- A Hyperflexible Electrode Array for Long-Term Recording and Decoding of Intraspinal Neuronal Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Neural Decoding for Intracortical Brain-Computer Interfaces.Cyborg and bionic systems (Washington, D.C.) · 2023Review
- Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals.Frontiers in human neuroscience · 2023Article
- Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network.Frontiers in neuroscience · 2022Article
- Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.
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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.