This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow deep-learning tensorflow keras eeg convolutional-neural-networks brain-computer-interface event-related-potentials time-series-...
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 ...
To this end, we use different error measures showing that the IRA estimation model can be more accurate and robust than the other compared methods.doi:10.1007/978-3-319-18914-7_41E. Giraldo-SuárezUniversidad Tecnológica de PereiraG. Castellanos-Dominguez...
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 resolution. EEG can be used in cognitive research or to diagnose conditions such as epilepsy and sleep...
Relationships between music played to the participants and music reconstructed via our fMRI-informed EEG source analysis approach. Correlation coefficients are shown in the time domain (left) and frequency domain (centre). The right figure shows structural similarity measures between the time-frequency ...
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 ...
processes believed to be involved, (ii) describe these components in terms of key neuroanatomical regions of interest, and (iii) critically appraise current findings regarding EEG measures as they relate to different aspects of meditation, functional activity and connectivity across regions of interest....
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...