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Decoding finger movements from ECoG signals using switching linear models
[article]
2011
arXiv
pre-print
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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
2012
Frontiers in Neuroscience
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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
2011
Frontiers in Neuroscience
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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
2019
Frontiers in Neuroinformatics
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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
2018
Frontiers in Neuroscience
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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
2014
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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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
2011
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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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
2012
Biomedical Engineering
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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
2020
eNeuro
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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]
2022
arXiv
pre-print
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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
2012
NeuroImage
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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]
2021
arXiv
pre-print
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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
2017
Journal of Neural Engineering
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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]
2020
biorxiv/medrxiv
pre-print
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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
2019
BMC Biomedical Engineering
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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
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