AIMC Topic: Brain-Computer Interfaces

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A cryptography-based approach for movement decoding.

Nature biomedical engineering Dec 12, 2017
Brain decoders use neural recordings to infer the activity or intent of a user. To train a decoder, one generally needs to infer the measured variables of interest (covariates) from simultaneously measured neural activity. However, there are cases fo...
Machine Learning Algorithms Animals Brain-Computer Interfaces Models, Neurological Movement Motor Cortex Data Interpretation, Statistical Macaca mulatta Neurons
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Decoding of finger trajectory from ECoG using deep learning.

Journal of neural engineering Nov 28, 2017
OBJECTIVE: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained...
Photic Stimulation Electrocorticography Sensorimotor Cortex Brain-Computer Interfaces Fingers Movement Humans Deep Learning
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Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI.

Computational and mathematical methods in medicine Nov 14, 2017
Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with...
Electroencephalography Reproducibility of Results Humans Motor Activity Brain-Computer Interfaces Signal Processing, Computer-Assisted Decision Making Wheelchairs Movement Probability Machine Learning Bayes Theorem Algorithms
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Embodiment and Estrangement: Results from a First-in-Human "Intelligent BCI" Trial.

Science and engineering ethics Nov 11, 2017
While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from...
Humans Qualitative Research Surveys and Questionnaires Stress, Psychological Self Concept Artificial Intelligence Technology Brain Knowledge Prostheses and Implants Intelligence Brain-Computer Interfaces
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Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

Scientific reports Nov 8, 2017
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase ...
Visual Perception Brain-Computer Interfaces Evoked Potentials, Visual Photic Stimulation Female Young Adult Adult Support Vector Machine Electroencephalography Male Adolescent Humans Algorithms Brain
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Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces.

Computational intelligence and neuroscience Nov 7, 2017
From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to assist people that somehow find it difficult or impossible to perform certain activities. Focusing on...
Time Perception Female Male User-Computer Interface Time Factors Humans Neuropsychological Tests Space Perception Neural Networks, Computer Auditory Perception Event-Related Potentials, P300 Electroencephalography Brain Brain-Computer Interfaces Analysis of Variance
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Brain Computer Interface Systems for Neurorobotics: Methods and Applications.

BioMed research international Oct 30, 2017
Brain-Computer Interfaces Humans Robotics
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CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

Computers in biology and medicine Oct 24, 2017
BACKGROUND: Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused ...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Humans Support Vector Machine Electroencephalography
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A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines.

Journal of neuroscience methods Oct 7, 2017
BACKGROUND: Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow.
Space Perception Language Imagination Female Ultrasonography, Doppler, Transcranial Humans Brain Linear Models Rest Brain-Computer Interfaces Neuropsychological Tests Cerebrovascular Circulation Young Adult Wavelet Analysis Feasibility Studies Cognition Male Blood Flow Velocity Rotation Functional Neuroimaging Support Vector Machine
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Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

Sensors (Basel, Switzerland) Oct 7, 2017
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configur...
Models, Biological Brain-Computer Interfaces Wavelet Analysis Neurology Normal Distribution Humans Algorithms Support Vector Machine Electroencephalography
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