AIMC Topic: Brain-Computer Interfaces

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Thinker invariance: enabling deep neural networks for BCI across more people.

Journal of neural engineering Oct 13, 2020
OBJECTIVE: Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal ...
Machine Learning Humans Neural Networks, Computer Electroencephalography Brain-Computer Interfaces
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Improvement of P300-Based Brain-Computer Interfaces for Home Appliances Control by Data Balancing Techniques.

Sensors (Basel, Switzerland) Sep 29, 2020
The oddball paradigm used in P300-based brain-computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classificatio...
Humans Support Vector Machine Brain-Computer Interfaces Electroencephalography
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Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain-Machine Interface.

Sensors (Basel, Switzerland) Sep 27, 2020
BACKGROUND: For the nonstationarity of neural recordings in intracortical brain-machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinfor...
Machine Learning Hand Brain-Computer Interfaces Algorithms Haplorhini Animals
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Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks.

Computational and mathematical methods in medicine Aug 28, 2020
In the process of brain-computer interface (BCI), variations across sessions/subjects result in differences in the properties of potential of the brain. This issue may lead to variations in feature distribution of electroencephalogram (EEG) across su...
Computational Biology Mathematical Concepts Electroencephalography Algorithms Humans Photic Stimulation Imagination Brain-Computer Interfaces Deep Learning Neural Networks, Computer Visual Perception
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Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces.

Nature communications Aug 25, 2020
Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. As the number of recording electrodes has been exponentially rising, the signal processing capability of brain-machine interfaces is f...
Electrical Synapses Models, Neurological Computers, Analog Epilepsy Humans Transistors, Electronic Neural Networks, Computer Signal Processing, Computer-Assisted Brain-Computer Interfaces Brain
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Emotional EEG classification using connectivity features and convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society Aug 19, 2020
Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-dimensional raw data. However,...
Humans Neural Networks, Computer Brain Emotions Algorithms Electroencephalography Brain-Computer Interfaces
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Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.

Sensors (Basel, Switzerland) Aug 17, 2020
Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain-computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across s...
Deep Learning Speech Brain-Computer Interfaces Electroencephalography Neural Networks, Computer Machine Learning
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Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction.

Journal of neuroscience methods Aug 15, 2020
BACKGROUND: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intentio...
Robotics Electroencephalography Stroke Algorithms Brain Stroke Rehabilitation Intention Brain-Computer Interfaces Humans
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Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network.

Sensors (Basel, Switzerland) Aug 11, 2020
As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extracti...
Humans Neural Networks, Computer Brain-Computer Interfaces Imagination Algorithms Electroencephalography
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Research and Verification of Convolutional Neural Network Lightweight in BCI.

Computational and mathematical methods in medicine Aug 1, 2020
With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convo...
Stochastic Processes Data Compression Humans Facial Expression Neural Networks, Computer Models, Neurological Brain-Computer Interfaces Databases, Factual Algorithms Deep Learning Electroencephalography Signal Processing, Computer-Assisted
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