208 Hits in 5.4 sec

Decoding finger movements from ECoG signals using switching linear models [article]

Rémi Flamary
2011 arXiv   pre-print
The difficulty lies in the fact that there is no simple relation between ECoG signals and finger movement. We propose in this paper to decode finger flexions using switching models.  ...  This method permits to simplify the system as it is now described as an ensemble of linear models depending on an internal state.  ...  Finger flexion decoding using switching linear models This section presents the full methodology we have used for addressing the problem of estimating finger flexions from ECoG signals.  ... 
arXiv:1106.3395v1 fatcat:aw3dnz4nmzaa3jf732mk2jdd3a

Decoding Finger Movements from ECoG Signals Using Switching Linear Models

Rémi Flamary, Alain Rakotomamonjy
2012 Frontiers in Neuroscience  
We propose in this paper, to estimate and decode these finger flexions using switching models controlled by an hidden state.  ...  As a witness of the BCI community increasing interest toward such a problem, the fourth BCI Competition provides a dataset which aim is to predict individual finger movements from ECoG signals.  ...  This work was supported in part by the IST Program of the European Community, under the PASCAL2 Network of Excellence, IST-216886, by grants from the ASAP ANR-09-EMER-001 and INRIA ARC MaBI.  ... 
doi:10.3389/fnins.2012.00029 pmid:22408601 pmcid:PMC3294271 fatcat:vxkhagvtk5ggfabl7tsoa7oc2e

Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals

Z. Wang, Q. Ji, K. J. Miller, Gerwin Schalk
2011 Frontiers in Neuroscience  
In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals.  ...  Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models.  ...  DISCUSSION This paper demonstrates that prior knowledge can be successfully captured to build switched non-parametric dynamic systems to decode finger flexion from ECoG signals.  ... 
doi:10.3389/fnins.2011.00127 pmid:22144944 pmcid:PMC3226159 fatcat:gtewvgdtjrglxglzpdazrgc5am

Decoding Movement From Electrocorticographic Activity: A Review

Ksenia Volkova, Mikhail A. Lebedev, Alexander Kaplan, Alexei Ossadtchi
2019 Frontiers in Neuroinformatics  
During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of  ...  ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients.  ...  Several other decoding algorithms of different complexity have been used for reproducing finger movements from ECoG, including switching linear model (Flamary and Rakotomamonjy, 2012; Liang and Bougrain  ... 
doi:10.3389/fninf.2019.00074 pmid:31849632 pmcid:PMC6901702 fatcat:gadwyhntarddpl3sckoiuipgvy

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Marie-Caroline Schaeffer, Tetiana Aksenova
2018 Frontiers in Neuroscience  
Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered.  ...  One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy.  ...  movement decoding from EEG signals.  ... 
doi:10.3389/fnins.2018.00540 pmid:30158847 fatcat:zpm6e55gvfedvhf26xyda4peti

Logistic-weighted regression improves decoding of finger flexion from electrocorticographic signals

Weixuan Chen, Xilin Liu, Brian Litt
2014 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
In this paper, we propose an algorithm called logistic-weighted regression to make use of the property, and apply the algorithm to a BCI system decoding flexion of human fingers from ECoG signals.  ...  As the output variables are continuous in these studies, a regression model is commonly used.  ...  DISCUSSION & CONCLUSION This article proposed a new method of decoding ECoG signals for the prediction of finger flexion in human beings.  ... 
doi:10.1109/embc.2014.6944162 pmid:25570530 dblp:conf/embc/ChenLL14 fatcat:so4gq2rshfhg5hdjgdt2q5ypda

Classification of multichannel ECoG related to individual finger movements with redundant spatial projections

I. Onaran, N. F. Ince, A. E. Cetin
2011 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
These results indicate that the redundant spatial projection framework can be used successfully in decoding finger movements from ECoG for BMI.  ...  We tackle the problem of classifying multichannel electrocorticogram (ECoG) related to individual finger movements for a brain machine interface (BMI).  ...  The firing rates from multiple electrodes were used in conjunction with an artificial neural network (ANN) to decode the finger movements.  ... 
doi:10.1109/iembs.2011.6091341 pmid:22255564 dblp:conf/embc/OnaranIC11 fatcat:oy3jky2qlzc4tlwul3sjxh53gi

Decoding finger movements from ECoG signals using Empirical Mode Decomposition

Mehrnaz Kh. Hazrati, Ulrich G. Hofmann
2012 Biomedical Engineering  
In this work we applied a time-frequency analysis based on Empirical Mode decomposition (EMD) and Adaptive Filtering (AF) to decode and estimate the finger movement using 10 minutes-long, multi-channel  ...  ECoG signals.  ...  Switching linear models controlled by a hidden state was proposed in [13] to decode finger flexion on the same database.  ... 
doi:10.1515/bmt-2012-4489 fatcat:hhru7hg23vbl3ernzh3i6reshq

The representation of finger movement and force in human motor and premotor cortices

Robert D. Flint, Matthew C. Tate, Kejun Li, Jessica W. Templer, Joshua M. Rosenow, Chethan Pandarinath, Marc W. Slutzky
2020 eNeuro  
Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90±6%).  ...  We recorded electrocorticography (ECoG) from seven human subjects who performed a sequential movement-force motor task.  ...  We built one such model to decode the continuous time course of finger movement kinematics using both high and low spectral features from all (M1/PM) electrodes.  ... 
doi:10.1523/eneuro.0063-20.2020 pmid:32769159 pmcid:PMC7438059 fatcat:koaeqfvr5rfcla4ki2qpokpd2q

An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic [article]

Alexandre Moly, Thomas Costecalde, Felix Martel, Christelle Larzabal, Serpil Karakas, Alexandre Verney, Guillaume Charvet, Stephan Chabardes, Alim Louis Benabid, Tetiana Aksenova
2022 arXiv   pre-print
For this purpose, an adaptive online tensor-based decoder: the Recursive Exponentially Weighted Markov-Switching multi-Linear Model (REW-MSLM) was developed.  ...  We demonstrated over a period of 6 months the stability of the 8-dimensional alternative bimanual control of the exoskeleton and its virtual avatar using REW-MSLM without recalibration of the decoder.  ...  In particular, the problem of multi-finger movement trajectory reconstruction from ECoG recordings was studied.  ... 
arXiv:2201.10449v1 fatcat:2toxasgy7fdlrlr4c77hvhossi

Decoding natural grasp types from human ECoG

Tobias Pistohl, Andreas Schulze-Bonhage, Ad Aertsen, Carsten Mehring, Tonio Ball
2012 NeuroImage  
Comparison to other studies decoding grasp types and finger movements Apart from evaluating the usefulness of ECoG signals for grasp decoding, this study aimed to establish a new approach to the decoding  ...  Decoding Algorithm. We used a regularized version (Friedman, 1989) of linear discriminant analysis (Hastie et al., 1995) to classify the trials from the recorded ECoG activity.  ... 
doi:10.1016/j.neuroimage.2011.06.084 pmid:21763434 fatcat:l4lmzk3tm5fttg2gtjhssdvpeu

Decoding ECoG signal into 3D hand translation using deep learning [article]

Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, Tetiana Aksenova
2021 arXiv   pre-print
However, most ECoG signal decoders used to predict continuous hand movements are linear models.  ...  These models have a limited representational capacity and may fail to capture the relationship between ECoG signal and continuous hand movements.  ...  Acknowledgments The authors would like to thank Thomas Costecalde, Serpil Karakas, Felix Martel (all from CEA-Leti), and Stephan Chabardes (CHUGA) for designing and recording the dataset used in this study  ... 
arXiv:2110.03528v1 fatcat:ixrlxbx36rdfflr3ymphxxytwa

Robust tactile sensory responses in finger area of primate motor cortex relevant to prosthetic control

Karen E Schroeder, Zachary T Irwin, Autumn J Bullard, David E Thompson, J Nicole Bentley, William C Stacey, Parag G Patil, Cynthia A Chestek
2017 Journal of Neural Engineering  
Sufficient sensory information was present in M1 to correctly decode stimulus position from multiunit activity above chance levels in all monkeys, and also from ECoG gamma power in two human subjects.  ...  A four-dimensional virtual hand brain-machine interface using active dimension selection Adam G Rouse Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor  ...  The most well-studied data sets for predicting our capability to decode finger movements online have come from mixed motor and sensory signals, as monkeys flexed their fingers to activate microswitches  ... 
doi:10.1088/1741-2552/aa7329 pmid:28504971 pmcid:PMC5734857 fatcat:yoiktfvzrvg67nba3x45khxsp4

Distinct representations of finger movement and force in human motor and premotor cortices [article]

Robert D Flint, Matthew C Tate, Kejun Li, Jessica W Templer, Joshua M Rosenow, Chethan Pandarinath, Marc W Slutzky
2020 biorxiv/medrxiv   pre-print
Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90±6%). Thus, finger movement and force have distinct representations in motor/premotor cortices.  ...  We decoded finger movement (0.7±0.3 fractional variance account for; FVAF) and force (0.7±0.2 FVAF) with high accuracy, yet found different spatial representations.  ...  We used one such model to decode 378 the continuous time course of finger movement kinematics using both high and low 379 spectral features from all (M1/PM) electrodes.  ... 
doi:10.1101/2020.02.18.952945 fatcat:lvjqmlbra5h5hnd5y4gfvrb2je

Human motor decoding from neural signals: a review

Wing-kin Tam, Tong Wu, Qi Zhao, Edward Keefer, Zhi Yang
2019 BMC Biomedical Engineering  
Many people suffer from movement disability due to amputation or neurological diseases.  ...  Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges.  ...  Using the LMP in addition to frequency features, position and velocity of 2D arm movement can also be decoded from ECoG signals [30, 51, 58] .  ... 
doi:10.1186/s42490-019-0022-z pmid:32903354 pmcid:PMC7422484 fatcat:nyazco6ho5gevi4t35qx22jsmy
« Previous Showing results 1 — 15 out of 208 results