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Brain-Computer Interfaces

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A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification.

Computational and mathematical methods in medicine
Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on diffe...

Emergent coordination underlying learning to reach to grasp with a brain-machine interface.

Journal of neurophysiology
The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a bra...

A cryptography-based approach for movement decoding.

Nature biomedical engineering
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...

Decoding of finger trajectory from ECoG using deep learning.

Journal of neural engineering
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...

Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI.

Computational and mathematical methods in medicine
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...

Embodiment and Estrangement: Results from a First-in-Human "Intelligent BCI" Trial.

Science and engineering ethics
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...

Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

Scientific reports
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 ...

Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces.

Computational intelligence and neuroscience
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...

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

Computers in biology and medicine
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 ...