In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity …
We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to …
We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able …
A Schlögl, F Lee, H Bischof… - Journal of neural …, 2005 - iopscience.iop.org
To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from …
Several feature types have been used with EEG-based Brain–Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific …
M Simon, EA Schmidt, WE Kincses, M Fritzsche… - Clinical …, 2011 - Elsevier
OBJECTIVE: The purpose of this study is to show the effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for …
C Vidaurre, TH Sander, A Schlögl - Computational intelligence …, 2011 - Wiley Online Library
BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and …
Abstract In Internet of Medical Things (IoMT) environment, feature selection is an efficient way of identifying the most discriminant health-related features from the original feature-set …
Purpose Fifteen percent to 25% of patients with refractory epilepsy require invasive video– electroencephalography (EEG) monitoring (IVEM) to precisely delineate the ictal‐onset …