State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

Ahmadi, Amirmasoud; Khorasani, Abed; Shalchyan, Vahid; Daliri, Mohammad Reza

doi:10.1109/access.2020.3019267

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…Bundy et al [29] designed a hierarchical partial least square (PLS) method with logistic regression as a discrete decoder for enhancing continuous estimation of reaching movement in a center-out task using electrocorticographic signals in human subjects. Ahmadi et al [45] proposed a method combining filter-bank CSP and PLS regression for improving continuous force decoding from LFP signals acquired from rats performing a key-pressing task.…”

Section: Discussionmentioning

confidence: 99%

State-Based Decoding of Continuous Hand Movements Using EEG Signals

Hosseini

Shalchyan

2023

IEEE Access

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Recently, the advent of the non-invasive brain-computer interface (BCI) for continuous decoding of upper limb motions opens a new horizon for motor-disabled people. However, the performance of discrete-decoding BCIs based on discriminating different brain states are still more robust. In this study, we aimed to cascade a discrete state decoder with a continuous decoder to enhance the prediction of hand trajectories. EEG data were recorded from nine healthy subjects performing a center-out task with four orthogonal targets on the horizontal plane. The pre-movement data of each trial has been used for training a binary discrete decoder which identifies the axis of the movement based on common spatial pattern (CSP) features. Two non-parametric continuous decoders based on Gaussian process regression (GPR) have been designed for continuous decoding of hand movements along each axis using the envelope features of EEG signals in six frequency bands. In addition to those four principal orthogonal targets, some targets at random directions on the horizontal plane were recorded to evaluate the generalizability of the proposed model. The discrete decoder attained the average binary classification of 97.1% for discriminating movement along the x-axis and y-axis. The proposed state-based method achieved the mean correlation coefficient of 0.54 between actual and predicted trajectories for principal targets over all subjects. The trajectories of random targets were also decoded with a mean correlation of 0.37. The generalizability of the proposed paradigm proved by the findings of this study could open new possibilities in developing novel types of neuroprostheses for rehabilitation purposes.INDEX TERMS brain-computer interface, continuous decoding, electroencephalography, Gaussian process regression, state-based decoding.

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Section: Discussionmentioning

confidence: 99%

State-Based Decoding of Continuous Hand Movements Using EEG Signals

Hosseini

Shalchyan

2023

IEEE Access

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“…Decoding parameters such as joint torque can be very useful in the development of hindlimb-related BCIs, leading to BCI systems that allow the user to control the machine/prosthesis better and with higher precision in different walking conditions. In forelimb studies, these parameters have been examined with acceptable decoding results [63], [114]. Concerning hand-related BCIs, some studies were focused on the torque decoding of the wrist joints [115], [116] but this group of kinetic parameters were not considered in hindlimb-related BCIs.…”

Section: Barroso Et Al (2019) [34] Ratmentioning

confidence: 99%

Intracortical Hindlimb Brain–Computer Interface Systems: A Systematic Review

et al. 2023

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Brain-computer interfaces (BCI) can help people with motor disorders to regain their ability to communicate and interact with the surrounding environment. The majority of studies in this field pursue the development of BCI systems to enhance or restore the movement functionality of people with disability. Although the studies on the development of BCIs to restore hindlimb movements have shorter backgrounds compared to forelimb, several studies have investigated hindlimb BCIs and their results were promising. In the present study, we systematically reviewed the studies investigating the decoding of hindlimb movement parameters using intracortical signals. Three scientific databases (PubMed, Scopus, and Embase) were used to extract the articles and the experiment, recording, processing methods, and results of the included studies were discussed. Although several studies on upper-limb intracortical BCIs have been conducted on human subjects, almost all studies in hindlimb intracortical BCIs field were performed on animal subjects. The most investigated task was walking on a treadmill, and the position of hindlimb joints and gait phase were the most studied continuous and discrete parameters, respectively. The included studies have mainly used spikes and linear decoders, which leaves the question of the effectiveness of using local field potentials and nonlinear decoders in this field unanswered. Although the results imply that hindlimb movement decoding using brain signals is feasible in laboratory conditions, further investigations are required to examine the hindlimb BCIs in real-life conditions.

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“…Ahmadi et al proposed a state-based decoder for estimating applied force using recorded LFP signals in rats [31]. This study asserted that the applied force time series includes resting and active phases.…”

Section: Introductionmentioning

confidence: 99%

GMMPLS: A Novel Automatic State-Based Algorithm for Continuous Decoding in BMIs

Foodeh¹,

Shalchyan²,

Daliri³

2021

IEEE Access

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In this paper, a novel fully-automated state-based decoding method has been proposed for continuous decoding problems in brain-machine interface (BMI) systems, called Gaussian mixture of model (GMM)-assisted PLS (GMMPLS). In contrast to other state-based and hierarchical decoders, the proposed method does not demand any prior information about the desired output structure. Instead, GMMPLS uses the GMM algorithm to divide the desired output into a specific number of states (clusters). Next, a logistic regression model is trained to predict the probability membership of each time sample for each state. Finally, using the concept of the partial least square (PLS) algorithm, GMMPLS constructs a model for decoding the desired output using the input data and the achieved membership probabilities. The performance of the GMMPLS has been evaluated and compared to PLS, the nonlinear quadratic PLS (QPLS), and the bayesian PLS (BPLS) methods through a simulated dataset and two different real-world BMI datasets. The achieved results demonstrated that the GMMPLS significantly outperformed PLS, QPLS, and BPLS overall datasets. INDEX TERMSBrain-machine interfaces (BMI), Partial least square (PLS), State-based decoding, Continuous decoding, Gaussian mixture of model (GMM).

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State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

Cited by 14 publications

References 41 publications

State-Based Decoding of Continuous Hand Movements Using EEG Signals

State-Based Decoding of Continuous Hand Movements Using EEG Signals

Intracortical Hindlimb Brain–Computer Interface Systems: A Systematic Review

GMMPLS: A Novel Automatic State-Based Algorithm for Continuous Decoding in BMIs

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