Adaptive Multimodel Knowledge Transfer Matrix Machine for EEG Classification.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3220551
Abstract: The emerging matrix learning methods have achieved promising performances in electroencephalogram (EEG) classification by exploiting the structural information between the columns or rows of feature matrices. Due to the intersubject variability of EEG data, these… read more here.
Keywords: matrix machine; knowledge; eeg classification; eeg ... See more keywords
A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2022.3194600
Abstract: 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… read more here.
Keywords: eeg classification; classification; transformer based; eeg ... See more keywords
Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification
Sign Up to like & getrecommendations! Published in 2018 at "The Scientific World Journal"
DOI: 10.1155/2018/8463256
Abstract: Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram… read more here.
Keywords: classification; eeg signals; eeg classification; sample entropy ... See more keywords
SPD-CNN: A plain CNN-based model using the symmetric positive definite matrices for cross-subject EEG classification with meta-transfer-learning
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neurorobotics"
DOI: 10.3389/fnbot.2022.958052
Abstract: The electroencephalography (EEG) signals are easily contaminated by various artifacts and noise, which induces a domain shift in each subject and significant pattern variability among different subjects. Therefore, it hinders the improvement of EEG classification… read more here.
Keywords: cross subject; eeg classification; symmetric positive; classification ... See more keywords
A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking
Sign Up to like & getrecommendations! Published in 2018 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2018.00217
Abstract: Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading… read more here.
Keywords: classification; feature; framework; eeg classification ... See more keywords