Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals (original) (raw)
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Anatomically constrained decoding of finger flexion from electrocorticographic signals
2011
Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these demonstrations usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target kinematic parameter. In this paper, we show that different types of anatomical constraints that govern finger flexion can be exploited in this context. Specifically, we incorporate these constraints in the construction, structure, and the probabilistic functions of a switched non-parametric dynamic system (SNDS) model. We then apply the resulting SNDS decoder to infer the flexion of individual fingers from the same ECoG dataset used in a recent study. Our results show that the application of the proposed model, which incorporates anatomical constraints, improves decoding performance compared to the results in the previous work. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation.
Decoding fingertip trajectory from electrocorticographic signals in humans
Neuroscience research, 2014
Seeking to apply brain-machine interface technology in neuroprosthetics, a number of methods for predicting trajectory of the elbow and wrist have been proposed and have shown remarkable results. Recently, the prediction of hand trajectory and classification of hand gestures or grasping types have attracted considerable attention. However, trajectory prediction for precise finger motion has remained a challenge. We proposed a method for the prediction of fingertip motions from electrocorticographic signals in human cortex. A patient performed extension/flexion tasks with three fingers. Average Pearson's correlation coefficients and normalized root-mean-square errors between decoded and actual trajectories were 0.83-0.90 and 0.24-0.48, respectively. To confirm generalizability to other users, we applied our method to the BCI Competition IV open data sets. Our method showed that the prediction accuracy of fingertip trajectory could be equivalent to that of other results in the com...
Logistic-weighted regression improves decoding of finger flexion from electrocorticographic signals
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
One of the most interesting applications of brain computer interfaces (BCIs) is movement prediction. With the development of invasive recording techniques and decoding algorithms in the past ten years, many single neuron-based and electrocorticography (ECoG)-based studies have been able to decode trajectories of limb movements. As the output variables are continuous in these studies, a regression model is commonly used. However, the decoding of limb movements is not a pure regression problem, because the trajectories can be apparently classified into a motion state and a resting state, which result in a binary property overlooked by previous studies. 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. Our results show that the application of logistic-weighted regression improves decoding performance compared to the application of linear reg...
Decoding Finger Movements from ECoG Signals Using Switching Linear Models
Frontiers in Neuroscience, 2012
One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. 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. The difficulty of the problem relies on the fact that there is no simple relation between ECoG signals and finger movements. We propose in this paper, to estimate and decode these finger flexions using switching models controlled by an hidden state. Switching models can integrate prior knowledge about the decoding problem and helps in predicting fine and precise movements. Our model is thus based on a first block which estimates which finger is moving and another block which, knowing which finger is moving, predicts the movements of all other fingers. Numerical results that have been submitted to the Competition show that the model yields high decoding performances when the hidden state is well estimated. This approach achieved the second place in the BCI competition with a correlation measure between real and predicted movements of 0.42.
Decoding flexion of individual fingers using electrocorticographic signals in humans
2009
Abstract Brain signals can provide the basis for a non-muscular communication and control system, a brain–computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)).
Decoding Finger Flexion from Electrocorticographic Signals Using a Sparse Gaussian Process
2010
Abstract A brain-computer interface (BCI) creates a direct communication pathway between the brain and an external device, and can thereby restore function in people with severe motor disabilities. A core component in a BCI system is the decoding algorithm that translates brain signals into action commands of an output device. Most of current decoding algorithms are based on linear models (eg, derived using linear regression) that may have important shortcomings.
Finger Movement Classification for an Electrocorticographic BCI
2007
We study the problem of distinguishing between individual finger movements of one hand using electrocorticographic (ECOG) signals. In previous work, we have shown that ECOG signals have high predictive accuracy and spatial resolution for classifying hand versus tongue movements. In this paper, we significantly extend this paradigm by studying the first 5-class classification problem for ECOG, and show that an average 5-class error of 23% across 6 subjects is possible using as little as 10min of training data. In addition to opening up possibilities for higher-bandwidth brain-computer interfaces, the use of finger movements for control may yield a more intuitive mapping from ECOG signals to control of a prosthetic. Although this study uses real movements, our results provide the foundation for understanding ECOG signal changes during finger movement.
Previous works in Brain-Machine Interfaces (BMI) have mostly used a single Kalman filter decoder for deriving continuous kinematics in the complete execution of behavioral tasks. A linear dynamical system may not be able to generalize the sequence whose dynamics changes over time. Examples of such data include human motion such as walking, running, and dancing each of which exhibit complex constantly evolving dynamics. Switching linear dynamical systems (S-LDSs) are powerful models capable of describing a physical process governed by state equations that switch from time to time. The present work demonstrates the motion-state-dependent adaptive decoding of hand and arm kinematics in four different behavioral tasks. Single-unit neural activities were recorded from cortical ensembles in the ventral and dorsal premotor (PMv and PMd) areas of a trained rhesus monkey during four different reach-to-grasp tasks. We constructed S-LDSs for decoding of continuous hand and arm kinematics based on different epochs of the experiments, namely, baseline, pre-movement planning, movement, and final fixation. Average decoding accuracies as high as 89.9%, 88.6%, 89.8%, 89.4%, were achieved for motion-statedependent decoding across four different behavioral tasks, respectively (p<0.05); these results are higher than previous works using a single Kalman filter (accuracy: 0.83). These results demonstrate that the adaptive decoding approach, or motion-state-dependent decoding, may have a larger descriptive capability than the decoding approach using a single decoder. This is a critical step towards the development of a BMI for adaptive neural control of a clinically viable prosthesis.
— Brain Computer Interface (BCI) is one of the clinical applications that may help to restore communication to people with motor disabilities. Electrocorticography (ECoG) is a semi invasive record to brain signals from electrode grids on the cortex surface. ECoG signal makes possible localization of the source of neural signals due to its high spatial resolution This study is a step towards exploring the usability of ECoG signal as BCI input technique and a multidimensional BCI control. The objective of this deterministic approach is to predict individual finger movement from ECoG signal by combining both classification and regression problems in machine learning of signal responses (regression via classification), on the other hand addressing the signal responses variability within a single subject. The dataset used in this work is the one presented in the fourth dataset from BCI competition IV. The difficulty is that; there is no simple and direct relation between ECoG signals and finger movements. This research work starts in two directions. The first direction is related to the decoding of the finger position signal to obtain a finger movement state signal. The second direction is related to the ECoG recorded signal, in order to obtain the corresponding brain signal of each finger movement. The work consists of five main phases (decoding finger state, pre-processing, features acquisition, classification, and regression). This approach suggests kinematic finger model which is applied on the finger muscle signal to generate the finger kinematic state signal. For feature extraction we used shift invariant wavelet decomposition and multi-taper frequency spectrum, followed by Gram-Schmidt test for selection. Linear support vector machine (SVM) is used for classification. Regression models are established by using the finger position training signal and the acquired ECoG features. To predict the finger movement signal under test; switching between these regression models is made. Finally the predicted finger movement signal is correlated with the measured one for evaluation. Results show that the average correlation measure between real and predicted movement is 0.82. This result is higher than the one obtained by the competition winner (0.46). Index Terms— BCI, ECoG, finger flexion, shift invariant wavelet decomposition, switching between linear models.
Cortical decoding of individual finger and wrist kinematics for an upper-limb neuroprosthesis
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009
Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey as they performed individuated movements of the fingers and wrist. The primates' hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit's position. Both linear and non-linear filters were designed to simultaneously predict kinematics of each digit and the wrist, and their performance compared using mean squared error and correlation coefficients. All models had high decoding accuracy, but the feedforward ANN (R = 0.76-0.86, MSE = 0.04-0.05) and Kalman filter (R = 0.68-0.86, MSE = 0.04-0.07) performed better than a simple linear ...