Papers EEG Signal Classification
“EEG Signal Classification” 태그가 달린 논문 33편 · 필터 해제
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for solving Epileptic EEG signal classification problems. The aim is to achieve a lightweight deep learning model without losing model cl…
ClassificationEEGEEG Signal ClassificationElectroencephalogram (EEG)Analyzing EEG Data with Machine and Deep Learning: A Benchmark
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology. In general, these techniques can be used in two ways: trying to adapt well-known models and architectu…
Deep LearningEEGEEG Signal ClassificationElectroencephalogram (EEG)An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction
Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific f…
ClassificationEEGEEG Signal ClassificationElectroencephalogram (EEG)Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface
Motor imagery brain-computer interface (MI-BCI) has many promising applications but there are problems such as poor classification accuracy and robustness which need to be addressed. We propose a novel approach called ti…
Brain Computer InterfaceClassificationEEGEEG Signal Classification+2A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG
Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers …
EEGEEG Signal ClassificationElectroencephalogram (EEG)Federated Transfer Learning for EEG Signal Classification
The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns as…
ClassificationDomain AdaptationEEGEEG Signal Classification+6MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification
Recurrent Neural Networks (RNN) are widely used for learning sequences in applications such as EEG classification. Complex RNNs could be hardly deployed on wearable devices due to their computation and memory-intensive p…
BinarizationClassificationEEGEEG Signal Classification+2EEG Signal Classification using Variational Mode Decomposition
Epilepsy affects about 1% of the population every year, and is characterized by abnormal and sudden hyper-synchronous excitation of the neurons in the brain. The electroencephalogram(EEG) is the most widely used method t…
ClassificationEEGEEG Signal ClassificationElectroencephalogram (EEG)Applying Transfer Learning To Deep Learned Models For EEG Analysis
The introduction of deep learning and transfer learning techniques in fields such as computer vision allowed a leap forward in the accuracy of image classification tasks. Currently there is only limited use of such techn…
Deep LearningEEGEEG Signal ClassificationElectroencephalogram (EEG)+3Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy
Epilepsy is the fourth most common neurological disorder, affecting about 1% of the population at all ages. As many as 60% of people with epilepsy experience focal seizures which originate in a certain brain area and are…
EEGEEG Signal ClassificationElectroencephalogram (EEG)General ClassificationClassification of EEG Signal based on non-Gaussian Neutral Vector
In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task. Recently, as the marginalized discrete wavelet transform (mDWT) repres…
Brain Computer InterfaceClassificationEEGEEG Signal Classification+3A Data Driven Approach for Resting-state EEG signal Classification of Schizophrenia with Control Participants using Random Matrix Theory
Resting state electroencephalogram (EEG) abnormalities in clinically high-risk individuals (CHR), clinically stable first-episode patients with schizophrenia (FES), healthy controls (HC) suggest alterations in neural osc…
ClassificationEEGEEG Signal ClassificationElectroencephalogram (EEG)Imagined speech classification using EEG
The objective of this work is to assess the possibility of using (Electroencephalogram) EEG for communication between different subjects. Here EEG signals are recorded from 13 subjects by inducing the subjects to imagine…
ClassificationEEGEEG Signal ClassificationElectroencephalogram (EEG)+1