Summary of over fifty years with brain-computer interfaces—a review
Over the last few decades, the Brain-Computer Interfaces have been gradually making their
way to the epicenter of scientific interest. Many scientists from all around the world have …
way to the epicenter of scientific interest. Many scientists from all around the world have …
Application of BCI systems in neurorehabilitation: a scoping review
M Bamdad, H Zarshenas, MA Auais - Disability and Rehabilitation …, 2015 - Taylor & Francis
Purpose: To review various types of electroencephalographic activities of the brain and
present an overview of brain–computer interface (BCI) systems' history and their …
present an overview of brain–computer interface (BCI) systems' history and their …
Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy
E Combrisson, K Jerbi - Journal of neuroscience methods, 2015 - Elsevier
Abstract Machine learning techniques are increasingly used in neuroscience to classify
brain signals. Decoding performance is reflected by how much the classification results …
brain signals. Decoding performance is reflected by how much the classification results …
Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines
T Lajnef, S Chaibi, P Ruby, PE Aguera… - Journal of neuroscience …, 2015 - Elsevier
Background Sleep staging is a critical step in a range of electrophysiological signal
processing pipelines used in clinical routine as well as in sleep research. Although the …
processing pipelines used in clinical routine as well as in sleep research. Although the …
Hybrid EEG–fNIRS-based eight-command decoding for BCI: application to quadcopter control
In this paper, a hybrid electroencephalography–functional near-infrared spectroscopy (EEG–
fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain …
fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain …
EEG-based classification of fast and slow hand movements using wavelet-CSP algorithm
A brain-computer interface (BCI) acquires brain signals, extracts informative features, and
translates these features to commands to control an external device. This paper investigates …
translates these features to commands to control an external device. This paper investigates …
[图书][B] Principles of neural coding
RQ Quiroga, S Panzeri - 2013 - books.google.com
Understanding how populations of neurons encode information is the challenge faced by
researchers in the field of neural coding. Focusing on the many mysteries and marvels of the …
researchers in the field of neural coding. Focusing on the many mysteries and marvels of the …
An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic
A Moly, T Costecalde, F Martel, M Martin… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. The article aims at addressing 2 challenges to step motor brain-computer
interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with …
interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with …
[HTML][HTML] Corticokinematic coherence mainly reflects movement-induced proprioceptive feedback
Corticokinematic coherence (CKC) reflects coupling between magnetoencephalographic
(MEG) signals and hand kinematics, mainly occurring at hand movement frequency (F0) and …
(MEG) signals and hand kinematics, mainly occurring at hand movement frequency (F0) and …
On the usage of linear regression models to reconstruct limb kinematics from low frequency EEG signals
Several works have reported on the reconstruction of 2D/3D limb kinematics from low-
frequency EEG signals using linear regression models based on positive correlation values …
frequency EEG signals using linear regression models based on positive correlation values …