AIMC Topic: Brain-Computer Interfaces

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Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.

Scientific reports Jun 8, 2021
This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning a...
Male Child Imaging, Three-Dimensional Learning Female Adult Signal Processing, Computer-Assisted Neurons Brain-Computer Interfaces Nerve Net Humans Magnetic Resonance Imaging Neural Networks, Computer Models, Neurological Electroencephalography Algorithms Brain Computer Simulation
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An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.

Computer methods and programs in biomedicine Apr 21, 2021
BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and con...
Upper Extremity Humans Deep Learning Electroencephalography Amputees Intention Brain-Computer Interfaces
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On Robot Compliance: A Cerebellar Control Approach.

IEEE transactions on cybernetics Apr 15, 2021
The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebe...
Humans Neuronal Plasticity Cerebellum Upper Extremity Brain-Computer Interfaces Models, Neurological Movement Action Potentials Robotics
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A Survey on Learning-Based Approaches for Modeling and Classification of Human-Machine Dialog Systems.

IEEE transactions on neural networks and learning systems Apr 2, 2021
With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction proble...
Artificial Intelligence Machine Learning Deep Learning Humans Neural Networks, Computer Brain-Computer Interfaces User-Computer Interface Models, Theoretical Natural Language Processing
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A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Journal of neural engineering Mar 5, 2021
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning technique...
Deep Learning Electroencephalography Brain-Computer Interfaces Humans Algorithms Brain
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Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning.

Journal of neural engineering Feb 26, 2021
. Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy th...
Neural Networks, Computer Deep Learning Biomechanical Phenomena Brain-Computer Interfaces Animals Motor Cortex
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The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

Sensors (Basel, Switzerland) Feb 25, 2021
In addition to helping develop products that aid the disabled, brain-computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance...
Bayes Theorem Humans Electroencephalography Reproducibility of Results Brain-Computer Interfaces Deep Learning
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Motor imagery recognition with automatic EEG channel selection and deep learning.

Journal of neural engineering Feb 11, 2021
Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing ...
Electroencephalography Brain-Computer Interfaces Imagination Humans Algorithms Deep Learning Imagery, Psychotherapy
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Neural Encoding and Decoding With Distributed Sentence Representations.

IEEE transactions on neural networks and learning systems Feb 4, 2021
Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, ha...
Semantics Brain-Computer Interfaces Cerebral Cortex Linguistics Reading Algorithms Temporal Lobe Brain Image Processing, Computer-Assisted Computer Simulation Humans Magnetic Resonance Imaging Language Neural Networks, Computer Occipital Lobe Natural Language Processing Reproducibility of Results Deep Learning
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Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification.

IEEE transactions on neural networks and learning systems Feb 4, 2021
In the context of motor imagery, electroencephalography (EEG) data vary from subject to subject such that the performance of a classifier trained on data of multiple subjects from a specific domain typically degrades when applied to a different subje...
Image Processing, Computer-Assisted Algorithms Brain-Computer Interfaces Humans Signal Processing, Computer-Assisted Reproducibility of Results Brain Mapping Movement Neural Networks, Computer Neuroimaging Deep Learning Electroencephalography
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