AIMC Topic: Brain-Computer Interfaces

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An Interpretable Deep Learning Model for Speech Activity Detection Using Electrocorticographic Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Oct 10, 2022
Numerous state-of-the-art solutions for neural speech decoding and synthesis incorporate deep learning into the processing pipeline. These models are typically opaque and can require significant computational resources for training and execution. A d...
Brain-Computer Interfaces Deep Learning Humans Electrocorticography Brain Speech
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Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.

Sensors (Basel, Switzerland) Oct 7, 2022
An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronoun...
Artifacts Brain-Computer Interfaces Support Vector Machine Discriminant Analysis Electroencephalography Signal Processing, Computer-Assisted Algorithms
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Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

IEEE journal of biomedical and health informatics Oct 4, 2022
Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG...
Humans Neural Networks, Computer Electroencephalography Brain-Computer Interfaces Event-Related Potentials, P300 Algorithms Deep Learning
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On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 19, 2022
Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and ...
Brain-Computer Interfaces Imagination Deep Learning Electroencephalography Humans Algorithms
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Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces.

IEEE transactions on cybernetics Aug 18, 2022
Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This...
Algorithms Brain Neural Networks, Computer Brain-Computer Interfaces Deep Learning Electroencephalography
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Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

Sensors (Basel, Switzerland) Aug 15, 2022
Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the deco...
Hand Brain-Computer Interfaces Aged Support Vector Machine Movement Humans Neural Networks, Computer Algorithms
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eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 11, 2022
Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We her...
Brain Deep Learning Amyotrophic Lateral Sclerosis Brain-Computer Interfaces Humans Electrooculography
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A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 4, 2022
The attention mechanism of the Transformer has the advantage of extracting feature correlation in the long-sequence data and visualizing the model. As time-series data, the spatial and temporal dependencies of the EEG signals between the time points ...
Deep Learning Electroencephalography Humans Algorithms Imagination Brain-Computer Interfaces Movement
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Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.

PloS one Jul 22, 2022
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine...
Electroencephalography Algorithms Imagery, Psychotherapy Brain-Computer Interfaces Deep Learning Imagination Humans
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Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems Jul 6, 2022
Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space ...
Brain-Computer Interfaces Electroencephalography Neuroimaging Neural Networks, Computer Algorithms Humans
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