Deep neural network for eeg signal-based subject-independent imaginary mental task classification
BACKGROUND. Mental task identification using electroencephalography (EEG) signals is
required for patients with limited or no motor movements. A subject-independent mental task …
required for patients with limited or no motor movements. A subject-independent mental task …
Light-weight 1-D convolutional neural network architecture for mental task identification and classification based on single-channel EEG
Mental task identification and classification using single/limited channel (s)
electroencephalogram (EEG) signals in real-time play an important role in the design of …
electroencephalogram (EEG) signals in real-time play an important role in the design of …
TD-LSTM: a time distributed and deep-learning-based architecture for classification of motor imagery and execution in EEG signals
M Karimian-Kelishadrokhi, F Safi-Esfahani - Neural Computing and …, 2024 - Springer
One of the critical challenges in brain-computer interfaces is the classification of brain
activities through the analysis of EEG signals. This paper seeks to improve the efficacy of …
activities through the analysis of EEG signals. This paper seeks to improve the efficacy of …
Variational mode decomposition based mental task classification from electroencephalogram
Selective feature extraction from single-channel electroencephalogram signal provides
appropriate classification of mental tasks, which is crucial for designing mobile brain …
appropriate classification of mental tasks, which is crucial for designing mobile brain …
Wearable EEG-based activity recognition in PHM-related service environment via deep learning
It is of paramount importance to track the cognitive activity or cognitve attenion of the service
personnel in a Prognostics and Health Monitoring (PHM) service related training or …
personnel in a Prognostics and Health Monitoring (PHM) service related training or …
State-of-the-art mental tasks classification based on electroencephalograms: a review
Electroencephalograms (EEGs) play an important role in analyzing different mental tasks
and neurological disorders. Hence, they are a critical component for designing various …
and neurological disorders. Hence, they are a critical component for designing various …
A novel convolutional neural network classification approach of motor-imagery EEG recording based on deep learning
Recently, Electroencephalography (EEG) motor imagery (MI) signals have received
increasing attention because it became possible to use these signals to encode a person's …
increasing attention because it became possible to use these signals to encode a person's …
A three phase approach for mental task classification using EEG
The Brain Computer Interface provides a channel of communication to physically challenged
individuals who have fully or partially lost the power to interact with their surroundings. The …
individuals who have fully or partially lost the power to interact with their surroundings. The …
Mental task classification using electroencephalogram signal
This paper studies the classification problem on electroencephalogram (EEG) data of mental
tasks, using standard architecture of three-layer CNN, stacked LSTM, stacked GRU. We …
tasks, using standard architecture of three-layer CNN, stacked LSTM, stacked GRU. We …
Interpretable convolutional neural networks for subject-independent motor imagery classification
JS Bang, SW Lee - 2022 10th International Winter Conference …, 2022 - ieeexplore.ieee.org
Deep learning frameworks have become increasingly popular in brain-computer interface
(BCI) study thanks to their outstanding performance. However, in terms of the classification …
(BCI) study thanks to their outstanding performance. However, in terms of the classification …