AIMC Topic: Brain-Computer Interfaces

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Motor decoding from the posterior parietal cortex using deep neural networks.

Journal of neural engineering May 19, 2023
Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless...
Macaca fascicularis Parietal Lobe Bayes Theorem Neurons Brain-Computer Interfaces Movement Neural Networks, Computer Animals Humans
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Sci-fi imagines how good brain-machine interfaces will amplify bad choices.

Science robotics May 17, 2023
and explore personal and societal ramifications of brain-machine interfaces.
Robotics Brain-Computer Interfaces Humans Male
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Deployment of an electrocorticography system with a soft robotic actuator.

Science robotics May 10, 2023
Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery and increasingly explored in brain-machine interface applications. Current devic...
Brain Animals Brain-Computer Interfaces Swine, Miniature Swine Robotics Electrocorticography
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Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG).

IEEE journal of biomedical and health informatics May 4, 2023
OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its applicati...
Humans Neural Networks, Computer Electroencephalography Epilepsy Brain-Computer Interfaces Algorithms Deep Learning
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Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.

Medical & biological engineering & computing Apr 25, 2023
Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the applica...
Brain-Computer Interfaces Imagination Deep Learning Electroencephalography Neural Networks, Computer Algorithms Humans
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EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface.

Computer methods in biomechanics and biomedical engineering Mar 23, 2023
Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in b...
Time Factors Electroencephalography Brain-Computer Interfaces Neural Networks, Computer Heuristics Algorithms
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Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Medical & biological engineering & computing Feb 23, 2023
Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG s...
Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted Imagination Algorithms Brain-Computer Interfaces
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Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

IEEE transactions on neural networks and learning systems Feb 3, 2023
In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on...
Neural Networks, Computer Machine Learning Algorithms Electroencephalography Humans Calibration Brain-Computer Interfaces
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Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience Jan 5, 2023
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...
Signal Processing, Computer-Assisted Humans Algorithms Brain-Computer Interfaces Neural Networks, Computer Imagination Discriminant Analysis Brain Electroencephalography
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A novel ANN adaptive Riemannian-based kernel classification for motor imagery.

Biomedical physics & engineering express Dec 30, 2022
More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify th...
Brain-Computer Interfaces Imagery, Psychotherapy Neural Networks, Computer Electroencephalography
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