Methods for simultaneous EEG-fMRI: an introductory review

RJ Huster, S Debener, T Eichele… - Journal of …, 2012 - Soc Neuroscience
The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data
in human systems, cognitive and clinical neurosciences is rapidly evolving and has received …

EEG-based brain-computer interfaces: a thorough literature survey

HJ Hwang, S Kim, S Choi, CH Im - International Journal of Human …, 2013 - Taylor & Francis
Brain–computer interface (BCI) technology has been studied with the fundamental goal of
helping disabled people communicate with the outside world using brain signals. In …

Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy

E Combrisson, K Jerbi - Journal of neuroscience methods, 2015 - Elsevier
Abstract Machine learning techniques are increasingly used in neuroscience to classify
brain signals. Decoding performance is reflected by how much the classification results …

Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines

T Lajnef, S Chaibi, P Ruby, PE Aguera… - Journal of neuroscience …, 2015 - Elsevier
Background Sleep staging is a critical step in a range of electrophysiological signal
processing pipelines used in clinical routine as well as in sleep research. Although the …

Role of EEG as biomarker in the early detection and classification of dementia

NK Al-Qazzaz, SHBMD Ali, SA Ahmad… - The Scientific World …, 2014 - Wiley Online Library
The early detection and classification of dementia are important clinical support tasks for
medical practitioners in customizing patient treatment programs to better manage the …

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

D Sabbagh, P Ablin, G Varoquaux, A Gramfort… - NeuroImage, 2020 - Elsevier
Predicting biomedical outcomes from Magnetoencephalography and
Electroencephalography (M/EEG) is central to applications like decoding, brain-computer …

Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain …

P Gerjets, C Walter, W Rosenstiel, M Bogdan… - Frontiers in …, 2014 - frontiersin.org
According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning
is the type and amount of working-memory load (WML) learners experience while studying …

Early seizure detection algorithm based on intracranial EEG and random forest classification

C Donos, M Dümpelmann… - International journal of …, 2015 - World Scientific
The goal of this study is to provide a seizure detection algorithm that is relatively simple to
implement on a microcontroller, so it can be used for an implantable closed loop stimulation …

Brain-computer interface based on generation of visual images

P Bobrov, A Frolov, C Cantor, I Fedulova, M Bakhnyan… - PloS one, 2011 - journals.plos.org
This paper examines the task of recognizing EEG patterns that correspond to performing
three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The …

Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG)

M Jobert, FJ Wilson, GSF Ruigt, M Brunovsky… - …, 2012 - karger.com
Abstract The International Pharmaco-EEG Society (IPEG) presents updated guidelines
summarising the requirements for the recording and computerised evaluation of pharmaco …