Brain–computer interfaces for communication and control

JR Wolpaw, N Birbaumer, DJ McFarland… - Clinical …, 2002 - Elsevier
For many years people have speculated that electroencephalographic activity or other
electrophysiological measures of brain function might provide a new non-muscular channel …

[PDF][PDF] Brain-computer interface technology: a review of the first international meeting

JR Wolpaw, N Birbaumer, WJ Heetderks… - IEEE transactions on …, 2000 - academia.edu
Over the past decade, many laboratories have begun to explore brain–computer interface
(BCI) technology as a radically new communication option for those with neuromuscular …

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 …

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 …

[책][B] Machine learning in non-stationary environments: Introduction to covariate shift adaptation

M Sugiyama, M Kawanabe - 2012 - books.google.com
Theory, algorithms, and applications of machine learning techniques to overcome" covariate
shift" non-stationarity. As the power of computing has grown over the past few decades, the …

[PDF][PDF] Covariate shift adaptation by importance weighted cross validation.

M Sugiyama, M Krauledat, KR Müller - Journal of Machine Learning …, 2007 - jmlr.org
A common assumption in supervised learning is that the input points in the training set follow
the same probability distribution as the input points that will be given in the future test phase …

A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals

A Bashashati, M Fatourechi, RK Ward… - Journal of Neural …, 2007 - iopscience.iop.org
Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending
commands to the external world using the electroencephalographic activity or other …

EMG and EOG artifacts in brain computer interface systems: A survey

M Fatourechi, A Bashashati, RK Ward, GE Birch - Clinical neurophysiology, 2007 - Elsevier
It is widely accepted in the brain computer interface (BCI) research community that
neurological phenomena are the only source of control in any BCI system. Artifacts are …

Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation

S Marcel, JR Millán - IEEE transactions on pattern analysis and …, 2007 - ieeexplore.ieee.org
In this paper, we investigate the use of brain activity for person authentication. It has been
shown in previous studies that the brain-wave pattern of every individual is unique and that …

Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems

EA Curran, MJ Stokes - Brain and cognition, 2003 - Elsevier
Brain–computer interface (BCI) technology relies on the ability of individuals to voluntarily
and reliably produce changes in their electroencephalographic (EEG) activity. The present …