Papers by Vikram Aggarwal
Abstract Recent advances in brain-machine interfaces (BMIs) have allowed for high density recordi... more Abstract Recent advances in brain-machine interfaces (BMIs) have allowed for high density recordings using microelectrode arrays. However, these large datasets present a challenge in how to practically identify features of interest and discard non-task-related neurons. Thus, we apply a previously reported unsupervised clustering analysis to neural data acquired from a non-human primate as it performed a center-out reach-and-grasp task.
Building Blocks of Closed Loop Neural Prostheses 1 Mathematical model of neuron ensemble; 2 Contr... more Building Blocks of Closed Loop Neural Prostheses 1 Mathematical model of neuron ensemble; 2 Controller to generate appropriate inputs for the prostheses; 3 Decoder to extract mechanical information; 4 Mathematical model of prostheses/arm; 5 Encoder to convert mechanical outputs from the prostheses into electrical potentials.
Abstract Background: It has been suggested that Brain-Computer Interfaces (BCI) may one day be su... more Abstract Background: It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.
Spatiotemporal Variation of Multiple Neurophysiological Signals in the Primary Motor Cortex during Dexterous Reach-to-Grasp Movements
Abstract To examine the spatiotemporal distribution of discriminable information about reach-to-g... more Abstract To examine the spatiotemporal distribution of discriminable information about reach-to-grasp movements in the primary motor cortex upper extremity representation, we implanted four microelectrode arrays in the anterior bank and lip of the central sulcus in each of two monkeys. We used linear discriminant analysis to compare information, quantified as decoding accuracy, contained in various neurophysiological signals.
Neural Systems and …, Jan 1, 2008
While previous efforts in Brain-Machine Interfaces (BMI) have looked at decoding movement intent ... more While previous efforts in Brain-Machine Interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, current neural control strategies have not focused on the decoding of dexterous actions such as finger movements. The present work demonstrates the asynchronous deciphering of the neural coding associated with the movement of individual and combined fingers. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Non-linear filters were used to decode both movement intent and movement type from randomly selected neuronal ensembles. Average asynchronous decoding accuracies as high as 99.8% ± 0.1%, 96.2% ± 1.8%, and 90.5% ± 2.1%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% ± 1.1% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This is an important step towards the development of a BMI for direct neural control of a state-of-the-art, multi-fingered hand prosthesis.
Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area
Neural Systems and …, Jan 1, 2008
Individuated finger and wrist movements can be decoded using random subpopulations of neurons tha... more Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates (i) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and (ii) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were re-created by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial Neural Network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a Brain-Machine Interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.
Cortical decoding of individual finger and wrist kinematics for an upper-limb neuroprosthesis
… in Medicine and …, Jan 1, 2009
Previous research has shown that neuronal activity can be used to continuously decode the kinemat... more 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&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;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 regression filter (0.58-0.81, 0.05-0.07). These results suggest that individual finger and wrist kinematics can be decoded with high accuracy, and be used to control a multi-fingered prosthetic hand in real-time.
Biomedical …, Jan 1, 2010
Towards a brain-computer interface for dexterous control of a multi-fingered prosthetic hand
… , 2007. CNE'07. 3rd …, Jan 1, 2007
Abstract Advances in brain-computer interfaces (BCI) have enabled direct neural control of roboti... more Abstract Advances in brain-computer interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (Ml) of a trained rhesus monkey were recorded, as it performed individuated movements of the fingers and wrist of the right hand. Virtual multi-unit ensembles, or voxels, were created by ...
Journal of …, Jan 1, 2007
Background: It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable fo... more Background: It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.
Computational …, Jan 1, 2010
A robust method to help identify the population of neurons used for decoding motor tasks is devel... more A robust method to help identify the population of neurons used for decoding motor tasks is developed. We use sensitivity analysis to develop a new metric for quantifying the relative contribution of a neuron towards the decoded output, called "fractional sensitivity." Previous model-based approaches for neuron ranking have been shown to largely depend on the collection of training data. We suggest the use of an ensemble of models that are trained on random subsets of trials to rank neurons. For this work, we tested a decoding algorithm on neuronal data recorded from two male rhesus monkeys while they performed a reach to grasp a bar at three orientations (45 • , 90 • , or 135 • ). An ensemble approach led to a statistically significant increase of 5% in decoding accuracy and 25% increase in identification accuracy of simulated noisy neurons, when compared to a single model. Furthermore, ranking neurons based on the ensemble fractional sensitivities resulted in decoding accuracies 10%-20% greater than when randomly selecting neurons or ranking based on firing rates alone. By systematically reducing the size of the input space, we determine the optimal number of neurons needed for decoding the motor output. This selection approach has practical benefits for other BMI applications where limited number of electrodes and training datasets are available, but high decoding accuracies are desirable.
Spectral modulation of LFP activity in M1 during dexterous finger movements
… in Medicine and …, Jan 1, 2008
Recent studies have shown that cortical local field potentials (LFP) contain information about pl... more Recent studies have shown that cortical local field potentials (LFP) contain information about planning or executing hand movement. While earlier research has looked at gross motor movements, we investigate the spectral modulation of LFP activity and its dependence on recording location during dexterous motor actions. In this study, we recorded LFP activity from the primary motor cortex of a primate as it performed a fine finger manipulation task involving different switches. The event-related spectral perturbations (ERSP) in four different frequency bands were considered for the analysis; 4 Hz, 6-15 Hz, 17-40 Hz and 75-170 Hz. LFPs recorded from electrodes in the hand area showed the largest change in ERSP for the highest frequency band (75-170 Hz) (p 0.05), while LFPs recorded from electrodes placed more medially in the arm area showed the largest change in ERSP for the lowest frequency band (4 Hz) (p 0.05). Furthermore, the spectral information from the &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;4 Hz and 75-150 Hz frequency bands was used to successfully decode the three dexterous grasp movements with an average accuracy of up to 81%. Although previous research has shown that multi-unit neuronal activity can be used to decode fine motor movements, these results demonstrate that LFP activity can also be used to decode dexterous motor tasks. This has implications for future neuroprosthetic devices due to the robustness of LFP signals for chronic recording.
Towards closed-loop decoding of dexterous hand movements using a virtual integration environment
… in Medicine and …, Jan 1, 2008
It has been shown that Brain-Computer Interfaces (BCIs) involving closed-loop control of an exter... more It has been shown that Brain-Computer Interfaces (BCIs) involving closed-loop control of an external device, while receiving visual feedback, allows subjects to adaptively correct errors and improve the accuracy of control. Although closed-loop cortical control of gross arm movements has been demonstrated, closed-loop decoding of more dexterous movements such as individual fingers has not been shown. Neural recordings were obtained from rhesus monkeys in three different experiments involving individuated flexion/extension of each finger, wrist rotation, and dexterous grasps. Separate decoding filters were implemented in Matlab&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s Simulink environment to independently decode this suite of dexterous movements in real-time. Average real-time decoding accuracies of 80% was achieved for all dexterous tasks with as few as 15 neurons for individual finger flexion/extension, 41 neurons for wrist rotation, and 79 neurons for grasps. In lieu of the availability of advanced multi-fingered prosthetic hands, real-time visual feedback of the decoded output was provided through actuation of a virtual prosthetic hand in a Virtual Integration Environment. This work lays the foundation for future closed-loop experiments with monkeys in the loop and dexterous control of an actual prosthetic limb.
Ultrasound-guided noninvasive measurement of a patient's central venous pressure
… in Medicine and …, Jan 1, 2006
Central venous pressure (CVP) is an important physiological parameter, the correct measure of whi... more Central venous pressure (CVP) is an important physiological parameter, the correct measure of which is a clinically relevant diagnostic tool for heart failure patients. A current challenge for physicians, however, is to obtain a quick and accurate measure of a patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s CVP in a manner that poses minimum discomfort. Current approaches for measuring CVP involve invasive methods such as threading a central venous catheter along a major vein, or tedious physical exams that require physicians to grossly estimate the measurement. Our solution proposes a novel noninvasive method to estimate central venous pressure using ultrasound-guided surface pressure measurement. Specifically, our device works in conjunction with an ultrasound machine and probe that is used to visualize the interior jugular (IJ) vein below the surface of the skin on a patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s neck. Once the interior jugular vein is located, our device detects the pressure on the skin required to collapse the IJ and correlates this value to a central venous pressure reading reported to the operator. This quick and noninvasive measurement is suitable for emergency situations or primary care settings where rapid diagnosis of a patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s CVP is required, and prevents the need for further invasive and costly procedures. The measurement procedure is also simple enough to be performed by operators without extensive medical training.
Brain-Computer Interface for a Prosthetic Hand Using Local Machine Control and Haptic Feedback
… , 2007. ICORR 2007 …, Jan 1, 2007
Abstract A brain-computer interface (BCI) uses electrophysiological measures of brain function to... more Abstract A brain-computer interface (BCI) uses electrophysiological measures of brain function to enable individuals to communicate with the external world, bypassing normal neuromuscular pathways. While it has been suggested that this control can be applied for neuroprostheses, few studies have demonstrated practical BCI control of a prosthetic device. In this paper, an electroencephalogram (EEG)-based motor imagery BCI is presented to control movement of a prosthetic hand. The hand was instrumented with force and angle ...
Operation of a Brain-Computer Interface Using Vibrotactile Biofeedback
… , 2007. CNE'07. 3rd …, Jan 1, 2007
Abstract Advances in brain-computer interfaces (BCI) will require the integration of haptic feedb... more Abstract Advances in brain-computer interfaces (BCI) will require the integration of haptic feedback channels to add extra sensory dimensions for applications such as neuroprostheses. To the best of our knowledge, previous BCIs have relied on visual biofeedback to the user in the form of a computer interface or a device. This study demonstrates that it is possible to operate a BCI using only vibrotactile biofeedback. Our results show that subjects are able to use vibrotactile feedback to control the BCI with ...
Noninvasive cortical control of a prosthetic hand with local machine control and haptic feedback
Meeting Biomed. Eng. Soc.(BMES …
American Control …, Jan 1, 2010
We propose to use the Izhikevich single neuron model to represent a motor cortex neuron for study... more We propose to use the Izhikevich single neuron model to represent a motor cortex neuron for studying a control-theoretic perspective of a neuroprosthetic system. The problem of estimating model parameters is addressed when the only available data from intracortical recordings of a neuron are the Inter-Spike Intervals (ISIs). Non-linear constrained and unconstrained optimization problems are formulated to estimate model parameters as well as synaptic inputs using ISIs data. The primal-dual interior-point method is implemented to solve the constrained optimization problem. Reasonable model parameters are estimated by solving these optimization problems which may serve as a template for studying and developing a model of ensemble cortical neurons for neuroprosthesis applications.
Computational complexity versus accuracy in classification of cortical neural signals
… , 2009. NER'09. 4th …, Jan 1, 2009
Abstract This paper analyzes different computational methods for real-time decoding of neural sig... more Abstract This paper analyzes different computational methods for real-time decoding of neural signals in primary motor cortex (M1). Specifically, we compare different classifiers as well as different Principal Component Analysis (PCA)-based pre-classification strategies to identify how to proceed in terms of the necessary trade-off between computational complexity and accuracy. Our methods are applied to neural data in monkey, recorded while performing dexterous hand and finger movement tasks. We show that differences due to ...
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Papers by Vikram Aggarwal