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Recognizing drivers' sleep onset by detecting slow eye movement using a parallel multimodal one-dimensional convolutional neural network.
Comput Methods Biomech Biomed Engin
January 2025
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, P.R. China.
Slow eye movements (SEMs) are a reliable physiological marker of drivers' sleep onset, often accompanied by EEG alpha wave attenuation. A parallel multimodal 1D convolutional neural network (PM-1D-CNN) model is proposed to classify SEMs. The model uses two parallel 1D-CNN blocks to extract features from EOG and EEG signals, which are then fused and fed into fully connected layers for classification.
View Article and Find Full Text PDFSTIT-Net- A Wavelet based Convolutional Transformer Model for Motor Imagery EEG Signal Classification in the Sensorimotor Bands.
Clin EEG Neurosci
January 2025
Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work.
View Article and Find Full Text PDFEarly-phase EEG power spectrum analysis may differentiate acute encephalopathy with biphasic seizures and late reduced diffusion from prolonged febrile seizures.
Epilepsy Res
January 2025
Department of Pediatric Neurology, Tokyo Metropolitan Children's Medical Center, Tokyo, Japan. Electronic address:
Background: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is clinically characterized by biphasic seizures associated with mild to severe neurological sequelae and is the most common subtype of acute encephalopathy in Japan, accounting for around 30 % of cases. The present study retrospectively analyzed the utility of electroencephalography (EEG) in determining the optimal method of diagnosing AESD at the early stage.
Methods: This study explores early power value differences to differentiate acute encephalopathy from prolonged febrile seizure (FS).
ECA-FusionNet: A hybrid EEG-fNIRS signals network for MI classification.
J Neural Eng
January 2025
Shanghai Dianji University, shnaghai, Shanghai, Shanghai, 201306, CHINA.
Objective: Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain.
View Article and Find Full Text PDFonEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation.
PLoS One
January 2025
Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland.
Electroencephalographic signals are obtained by amplifying and recording the brain's spontaneous biological potential using electrodes positioned on the scalp. While proven to help find changes in brain activity with a high temporal resolution, such signals are contaminated by non-stationary and frequent artefacts. A plethora of noise reduction techniques have been developed, achieving remarkable performance.
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