ArticleFrontiers in neuroscience2019
Adaptive Artifact Removal From Intracortical Channels for Accurate Decoding of a Force Signal in Freely Moving Rats.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
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- Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.APL bioengineering · 2025Article
- Estimating a brain network predictive of stress and genotype with supervised autoencoders.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2022Article
- Multi-scale neural decoding and analysis.Journal of neural engineering · 2021Review
- Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method.Cell reports methods · 2021Article
- A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1).BMC bioinformatics · 2021Article
- Regularized Partial Least Square Regression for Continuous Decoding in Brain-Computer Interfaces.Neuroinformatics · 2020Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracortical data recorded with multi-electrode arrays provide rich information about kinematic and kinetic states of movement in the brain-machine interface (BMI) systems. Direct estimation of kinetic information such as the force from cortical data has the same importance as kinematic information to make a functional BMI system. Various types of the information including single unit activity (SUA), multiunit activity (MUA) and local field potential (LFP) can be used as an input information to extract motor commands for control of the external devices in BMI. Here we combine LFP and MUA information to improve decoding accuracy of the force signal from the multi-channel intracortical data of freely moving rats. We suggest a weighted common average referencing (CAR) algorithm in order to valid interpretation of the force decoding from different data types. The proposed spatial filter adaptively identifies contribution of the common noise on the channels employing Kalman filter method. We evaluated the efficacy of the proposed artifact algorithm on both simulation and real data. In the simulation study, the average
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