EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges

N Padfield, J Zabalza, H Zhao, V Masero, J Ren - Sensors, 2019 - mdpi.com
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges

JR Millán, R Rupp, GR Müller-Putz… - Frontiers in …, 2010 - frontiersin.org
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 …

EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing

A Delorme, T Mullen, C Kothe… - Computational …, 2011 - Wiley Online Library
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 …

Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks

G Pfurtscheller, C Brunner, A Schlögl, FHL Da Silva - NeuroImage, 2006 - Elsevier
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 …

Characterization of four-class motor imagery EEG data for the BCI-competition 2005

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 …

Time domain parameters as a feature for EEG-based brain–computer interfaces

C Vidaurre, N Krämer, B Blankertz, A Schlögl - Neural Networks, 2009 - Elsevier
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 …

EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions

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 …

BioSig: the free and open source software library for biomedical signal processing

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 …

A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment

R Chatterjee, T Maitra, SKH Islam, MM Hassan… - Future Generation …, 2019 - Elsevier
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 …

Ictal‐onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy

P Van Mierlo, E Carrette, H Hallez, R Raedt… - …, 2013 - Wiley Online Library
Purpose Fifteen percent to 25% of patients with refractory epilepsy require invasive video–
electroencephalography (EEG) monitoring (IVEM) to precisely delineate the ictal‐onset …