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Found 14 papers, 1 papers with code
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network
We believe that effective execution of motor imagery can be achieved not only for fine MI, but also for local muscle MI
Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis
Traffic prediction is one of the key elements to ensure the safety and convenience of citizens.
Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification
To check the generalization ability of the proposed method, an AFDB-trained model was tested on the MITDB, and superior performance was attained compared with that of a state-of-the-art model.
Multi-image Super-resolution via Quality Map Associated Attention Network
Multi-image super-resolution, which aims to fuse and restore a high-resolution image from multiple images at the same location, is crucial for utilizing satellite images.
Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG
We pre-trained the U-Net to classify the 5-class sleep stages using night-sleep EEG and used the sleep stages estimated by the U-Net to detect micro-sleep during driving.
Predicting the Transition from Short-term to Long-term Memory based on Deep Neural Network
So far, many studies have analyzed electroencephalogram (EEG) signals at encoding to predict later remembered items, but few studies have predicted long-term memory only with EEG signals of successful short-term memory.
Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network
Our findings showed that EEG signals are possible to differentiate when subjects are presented with visual stimulus of different semantic categories and at an exemplar-level with a high classification accuracy; this demonstrates its viability to be applied it in a real-world BMI.
Classification of Imagined Speech Using Siamese Neural Network
The proposed framework would help to increase the classification performance of imagined speech for a small amount of data and implement an intuitive communication system.
Reconstructing ERP Signals Using Generative Adversarial Networks for Mobile Brain-Machine Interface
As a result, the reconstructed signals had important components such as N200 and P300 similar to ERP during standing.
Assessment of Unconsciousness for Memory Consolidation Using EEG Signals
Seven participants performed two memory tasks (word-pairs and visuo-spatial) before and after the nap to assess the memory consolidation during unconsciousness.
Prediction of Memory Retrieval Performance Using Ear-EEG Signals
These results showed that it is possible to predict performance of a memory task using ear-EEG signals and it could be used for predicting memory retrieval in a practical brain-computer interface.
End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features
For five sleep stage classification, the classification performance 85. 6% and 91. 1% using the raw input data and the proposed input, respectively.
Effective Correlates of Motor Imagery Performance based on Default Mode Network in Resting-State
Motor imagery based brain-computer interfaces (MI-BCIs) allow the control of devices and communication by imagining different muscle movements.
Neural Oscillations for Encoding and Decoding Declarative Memory using EEG Signals
For decoding phase, only significant decreases of alpha power were observed over fronto-central area.
