scholarly journals Utility and lower limits of frequency detection in surface electrode stimulation for somatosensory brain-computer interface in humans

2020 ◽  
Vol 48 (2) ◽  
pp. E2
Author(s):  
Daniel R. Kramer ◽  
Krista Lamorie-Foote ◽  
Michael Barbaro ◽  
Morgan B. Lee ◽  
Terrance Peng ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Limit Of Detection ◽  
Brain Computer Interface ◽  
Stimulation Frequency ◽  
Computer Interface ◽  
Stimulation Parameter ◽  
Frequency Detection ◽  
Stimulation Parameters ◽  
Lower Limits ◽  
Hand Area

OBJECTIVEStimulation of the primary somatosensory cortex (S1) has been successful in evoking artificial somatosensation in both humans and animals, but much is unknown about the optimal stimulation parameters needed to generate robust percepts of somatosensation. In this study, the authors investigated frequency as an adjustable stimulation parameter for artificial somatosensation in a closed-loop brain-computer interface (BCI) system.METHODSThree epilepsy patients with subdural mini-electrocorticography grids over the hand area of S1 were asked to compare the percepts elicited with different stimulation frequencies. Amplitude, pulse width, and duration were held constant across all trials. In each trial, subjects experienced 2 stimuli and reported which they thought was given at a higher stimulation frequency. Two paradigms were used: first, 50 versus 100 Hz to establish the utility of comparing frequencies, and then 2, 5, 10, 20, 50, or 100 Hz were pseudorandomly compared.RESULTSAs the magnitude of the stimulation frequency was increased, subjects described percepts that were “more intense” or “faster.” Cumulatively, the participants achieved 98.0% accuracy when comparing stimulation at 50 and 100 Hz. In the second paradigm, the corresponding overall accuracy was 73.3%. If both tested frequencies were less than or equal to 10 Hz, accuracy was 41.7% and increased to 79.4% when one frequency was greater than 10 Hz (p = 0.01). When both stimulation frequencies were 20 Hz or less, accuracy was 40.7% compared with 91.7% when one frequency was greater than 20 Hz (p < 0.001). Accuracy was 85% in trials in which 50 Hz was the higher stimulation frequency. Therefore, the lower limit of detection occurred at 20 Hz, and accuracy decreased significantly when lower frequencies were tested. In trials testing 10 Hz versus 20 Hz, accuracy was 16.7% compared with 85.7% in trials testing 20 Hz versus 50 Hz (p < 0.05). Accuracy was greater than chance at frequency differences greater than or equal to 30 Hz.CONCLUSIONSFrequencies greater than 20 Hz may be used as an adjustable parameter to elicit distinguishable percepts. These findings may be useful in informing the settings and the degrees of freedom achievable in future BCI systems.

Selection of P300 signal features based on machine learning and the Morlet wavelet use for BCI applications

2021 ◽  
Author(s):  
N. Haddad ◽  
M.V. Derkach ◽  
A.N. Dmitriev ◽  
I.K. Sergeev ◽  
S.I. Shchukin
Keyword(s):  
Machine Learning ◽  
Correlation Function ◽  
Classification Accuracy ◽  
Target Stimulus ◽  
Brain Computer Interface ◽  
Stimulation Frequency ◽  
Computer Interface ◽  
Support Vector ◽  
Stimulation Parameters

Brain-computer interface is a promising technology that gives humans with a motor disease in some particular cases the ability to directly control computers or any external devices using only brain signals such as EEG. Among a variety of EEG patterns that were used to design EEG-based BCI, P300 is considered as one of the most common options for fine motor rehabilitation. Efficient P300 detection is essential due to its crucial role in evaluating the accuracy and reliability of brain-computer interfaces, in the last few years, Machine learning methods have been widely used as classifiers for P300. Where the quality of the classification using these methods significantly depends on the input features. This work aims to study the parameters of visual stimulation, as well as the characteristics of the P300, which would lead to an increase in the accuracy of the target stimulus detection for the P300-BCI system. Correlation analysis between target – non-target stimuli and wavelet Morlet has been done after splitting the data according to the stimulation frequency for comparisons, taking into account the stimulation parameters that were used in each experiment to find out which stimulation parameters are more suitable for brain-computer interface based on P300. A comparative analysis of various features of the P300 using artificial neural networks has been considered to achieve more analytical and consistent conclusions. The use of ANN in our work has been decided after considering many other methods like support vector machine, linear discriminant analysis, and Random forest. A significant improvement in the classification accuracy was achieved using the correlation function as a P300 feature. It was found that the wavelet parameters should be selected individually for each participant. However, no direct relationship was found between the wavelet parameters and the stimulation frequency. Taking into account the individual parameters of the wavelet Morlet for each patient, and using the correlation function as a P300 feature, allows achieving a significant increase in the classification accuracy of the target stimulus. In addition to the importance of choosing the most appropriate paradigm during the experiment, which would increase the quality of data and thus improve BCI performance.

EFFECT OF STIMULUS PARAMETERS IN THE TREATMENT OF SEIZURES BY ELECTRICAL STIMULATION IN THE KAINATE ANIMAL MODEL

2011 ◽  
Vol 21 (02) ◽  
pp. 151-162 ◽  
Author(s):  
POOJA RAJDEV ◽  
MATTHEW WARD ◽  
PEDRO IRAZOQUI
Keyword(s):  
Electrical Stimulation ◽  
Pulse Width ◽  
Closed Loop ◽  
Seizure Activity ◽  
Open Loop ◽  
Stimulation Frequency ◽  
Total Charge ◽  
Stimulation Parameter ◽  
Loop Control ◽  
Stimulation Parameters

Preliminary results from animal and clinical studies demonstrate that electrical stimulation of brain structures can reduce seizure frequency in patients with refractory epilepsy. Since most researchers derive stimulation parameters by trial and error, it is unclear what stimulation frequency, amplitude and duration constitutes a set of optimal stimulation parameters for aborting seizure activity in a given patient. In this investigation, we begin to quantify the independent effects of stimulation parameters on electrographic seizures, such that they could be used to develop an efficient closed-loop prosthesis that intervenes before the clinical onset of a seizure and seizure generalization. Biphasic stimulation is manually delivered to the hippocampus in response to a visually detected electrographic seizure. Such focal, responsive stimulation allows for anti-seizure treatment delivery with improved temporal and spatial specificity over conventional open-loop stimulation paradigms, with the possibility of avoiding tissue damage stemming from excessive exposure to electrical stimulation. We retrospectively examine the effects of stimulation frequency (low, medium and high), pulse-width (low and high) and amplitude (low and high) in seizures recorded from 23 kainic acid treated rats. We also consider the effects of total charge delivered and the rate of charge delivery, and identify stimulation parameter sets that induce after-discharges or more seizures. Among the stimulation parameters evaluated, we note 2 major findings. First, stimulation frequency is a key parameter for inhibiting seizure activity; the anti-seizure effect cannot be attributed to only the charge delivered per phase. Second, an after-discharge curve shows that as the frequency and pulse-width of stimulation increases, smaller pulse amplitudes are capable of eliciting an after-discharge. It is expected that stimulation parameter optimization will lead to devices with enhanced treatment efficacies and reduced side-effect profiles, especially when used in conjunction with seizure prediction or detection algorithms in a closed-loop control application.

scholarly journals Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons

2014 ◽  
Vol 112 (6) ◽  
pp. 1528-1548 ◽  
Author(s):  
Andrew J. Law ◽  
Gil Rivlis ◽  
Marc H. Schieber
Keyword(s):  
Motor Cortex ◽  
Degrees Of Freedom ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Rapid Acquisition ◽  
Interface Control ◽  
Large Ensembles ◽  
Performance Patterns ◽  
The Individual

Pioneering studies demonstrated that novel degrees of freedom could be controlled individually by directly encoding the firing rate of single motor cortex neurons, without regard to each neuron's role in controlling movement of the native limb. In contrast, recent brain-computer interface work has emphasized decoding outputs from large ensembles that include substantially more neurons than the number of degrees of freedom being controlled. To bridge the gap between direct encoding by single neurons and decoding output from large ensembles, we studied monkeys controlling one degree of freedom by comodulating up to four arbitrarily selected motor cortex neurons. Performance typically exceeded random quite early in single sessions and then continued to improve to different degrees in different sessions. We therefore examined factors that might affect performance. Performance improved with larger ensembles. In contrast, other factors that might have reflected preexisting synaptic architecture—such as the similarity of preferred directions—had little if any effect on performance. Patterns of comodulation among ensemble neurons became more consistent across trials as performance improved over single sessions. Compared with the ensemble neurons, other simultaneously recorded neurons showed less modulation. Patterns of voluntarily comodulated firing among small numbers of arbitrarily selected primary motor cortex (M1) neurons thus can be found and improved rapidly, with little constraint based on the normal relationships of the individual neurons to native limb movement. This rapid flexibility in relationships among M1 neurons may in part underlie our ability to learn new movements and improve motor skill.

Design and Implementation of Portable Visual Stimulator for BCI

2014 ◽  
Vol 912-914 ◽  
pp. 1205-1208
Author(s):  
Qiu Ling Yang ◽  
Ping Dong Wu ◽  
Xi Peng Li
Keyword(s):  
Visual Stimulation ◽  
Brain Computer Interface ◽  
Operating Mode ◽  
Computer Interface ◽  
Stimulation Parameter ◽  
Usb Interface ◽  
Design And Implementation ◽  
Visual Stimulator ◽  
Almost All ◽  
Bci System

Visual stimulator is one of the key factors that affect the steady-state visual evoked potential (SSVEP). Research and development of brain-computer interface (BCI) system based on SSVEP have priority to consider the design and implementation of visual stimulation. Compare with visual stimulators for particular applications, this paper presents a visual stimulator that is portable and easy to modify apparatus, which is suitable for expanding the application of SSVEP based BCI system. This article induces a visual stimulator based on ARM microcontroller for BCI. The stimulator uses a common USB interface, achieving multiple selective stimulus methods. The system features include visual stimulation, parameter setting and display, the experimental results feedback display. Experimental data prove that this stimulator can be used for almost all types of brain-computer interface based on visual stimulation experiments, and have a portable operating mode, flexible parameter settings , and easy operation.

Brain-Computer Interface Application in Robotic Gripper Control

2018 ◽  
Author(s):  
Briana Landavazo ◽  
Vidya K. Nandikolla
Keyword(s):  
Control System ◽  
Degrees Of Freedom ◽  
Brain Computer Interface ◽  
Research Work ◽  
Computer Interface ◽  
Data Sets ◽  
Control Input ◽  
Robotic Hand ◽  
Test Results ◽  
Eeg Data

In robotics research, the electroencephalograph (EEG) based brain-computer interface (BCI) as a control input has been used in designing prosthesis, wheelchairs and virtual navigation. The paper presents the research work on BCI development that communicates between an operator and a robotic gripping device. The control of a BCI robotic hand is broken down into two main subsystems. The first subsystem acquires a signal from the brain through the Emotiv EPOC EEG headset, extracts features and translates them into an input to the control system. The second subsystem incorporates kinematics and feedback from sensors, to control the multiple degrees of freedom used in the gripping device depending on the action specified by the higher-level BCI control. The BCI is trained to filter and extract features relating to the different hand motions from the data sets. Machine learning is used in conjunction with data filtering, feature extraction, and feature classification techniques to create a more accurate and personalized BCI hand control system. The system analyzes the EEG data, compares with the EEG data patterns from previous attempts. The test results demonstrate the movement functions of the gripper using the BCI, and the success rate for each function are presented in this paper.

scholarly journals Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson’s disease

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Kenneth H. Louie ◽  
Matthew N. Petrucci ◽  
Logan L. Grado ◽  
Chiahao Lu ◽  
Paul J. Tuite ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Stimulation Frequency ◽  
Trial And Error ◽  
Optimal Frequency ◽  
Stimulation Parameter ◽  
Stimulation Parameters ◽  
Deep Brain

Abstract Background Deep brain stimulation (DBS) is a treatment option for Parkinson’s disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptible to clinician bias. This trial-and-error process will be further prolonged with the introduction of segmented electrodes that are now commercially available. New approaches to optimizing a patient’s stimulation parameters, that can also handle the increasing complexity of new electrode and stimulator designs, is needed. Methods To improve DBS parameter programming, we explored two semi-automated optimization approaches: a Bayesian optimization (BayesOpt) algorithm to efficiently determine a patient’s optimal stimulation parameter for minimizing rigidity, and a probit Gaussian process (pGP) to assess patient’s preference. Quantified rigidity measurements were obtained using a robotic manipulandum in two participants over two visits. Rigidity was measured, in 5Hz increments, between 10–185Hz (total 30–36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. First, we compared the optimal frequency between visits with the participant’s preferred frequency. Next, we evaluated the efficiency of the BayesOpt algorithm, comparing it to random and equal interval selection of frequency. Results The BayesOpt algorithm estimated the optimal frequency to be the highest tolerable frequency, matching the optimal frequency found during the first visit. However, the participants’ pGP models indicate a preference at frequencies between 70–110 Hz. Here the stimulation frequency is lowest that achieves nearly maximal suppression of rigidity. BayesOpt was efficient, estimating the rigidity response curve to stimulation that was almost indistinguishable when compared to the longer brute force method. Conclusions These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient’s preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters.

Sign in / Sign up

Export Citation Format

Share Document