To address this challenge, this study proposes an adaptive wavelet transform Kolmogorov.Arnold network (KAN) approach named AWKNet, which uses wavelet loss to construct personalized discrete wavelet ...

This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected ...

[1] Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet ...

This documentation describes the application of wavelet transforms for EEG (Electroencephalogram) signal analysis, including preprocessing, feature extraction, and event detection. Think of EEG ...

accurate means toward probabilistically assessing the presence of sleep spindles in EEG signals. We use the intuitively appealing continuous wavelet transform (CWT) with a Morlet basis function, ...

Classification of schizophrenic patients by applying the wavelet transform to evoked potentials.

This paper proposes a unified framework, Wavelet Movement Primitives (WMPs), which are built on Probabilistic Movement Primitives (ProMPs) integrated with Discrete Wavelet Transform (DWT), to model ...

Electroencephalography (EEG) is a method for monitoring electrical activity in the brain. It uses electrodes placed on or below the scalp to record activity with coarse spatial but high temporal ...

The writer and director Coralie Fargeat narrates a sequence from her film, which is nominated for best picture. transcript The writer and director Coralie Fargeat narrates a sequence from her film ...

Brain implant transforms teenage girl's life "GMA" shares the inspiring story of a teen who found hope after struggling with seizures for years, thanks to a groundbreaking new treatment. Up Next in ...