This study introduces a customized ConvNeXt architecture, a powerful convolutional neural network, specifically adapted for EEG analysis. ConvNeXt addresses traditional EEG challenges like high dimensionality, noise, and variability, enhancing the precision of mental workload classification. Using the STEW ...
Electroencephalography (EEG) is a non-invasive method for recording electrical activity in the brain, first performed on humans by Hans Berger in 1924(Berger, 1929). Here are 1,289 public repositories matching this topic... Language:All ...
Temporal Factors of EEG and Artificial Neural Network Classifiers of Mental Workload The use of machine learning algorithms to classify Mental Workload (MW) from various neurophysiological measures is a growing trend in Human Factors resear... BN Penaranda,CL Baldwin - 《International Journal of Comp...
To address this issue we build and validate a neural decoding model to both reconstruct and identify the music an individual is listening to from a combination of EEG and fMRI recordings of their brain activity. Music is a form of emotional communication and is also a complex acoustic signal t...
Seizure sensitivity measures the fraction of the correctly predicted seizures, as described by equation (2). $$\begin{aligned} SS = \frac{Predicted \ seizures}{All \ seizures} \end{aligned}$$ (2) FPR/h measures the occurrence of false alarms during an hour and is defined as the proport...
Klassen et al (2011) evaluated qEEG measures as predictive biomarkers for the development of dementia in Parkinson disease (PD). Preliminary work shows that qEEG measures correlate with current PD cognitive state. A reliable predictive qEEG biomarker for PD dementia (PD-D) incidence would be valuab...
Historically, no clear agreement has been reached regarding the functional meaning of EEG alpha wave activity or even which measures best characterize it. The physiological basis of alpha signals (including anatomical and topographical factors) means that measurement of alpha activity can involve tracking...
Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the ...
activity with high-temporal resolution9,10,11. Quantitative EEG (QEEG) measures, including power spectral density10,11and spatiotemporal microstates9, have been extracted as distinctive features to distinguish PD patients from healthy individuals. More recently, an increasing number of studies on PD ...
Targeted treatments for fragile X syndrome (FXS) have frequently failed to show efficacy in clinical testing, despite success at the preclinical stages. This has highlighted the need for more effective translational outcome measures. EEG differences obse