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Vibrotactile Feedback for Brain-Computer Interface Operation

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Brain-Machine Interfaces through Control of Electroencephalographic Signals and Vibrotactile Feedback

José del Rocio Millán Ruiz

A Brain-Computer Interface (BCI) allow direct expression of its userï¿½s will by interpreting signals which directly reflect the brainï¿½s activity, thus bypassing the natural efferent channels (nerves and muscles). To be correctly mastered, it is needed t ...

2007

Error-related EEG potentials in brain-computer interfaces

José del Rocio Millán Ruiz, Pierre Ferrez

People with severe motor disabilities (spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), etc.) but with intact brain functions are somehow prisoners of their own body. They need alternative ways of communication and control to interact with th ...

EPFL2007

Error-related EEG potentials in brain-computer interfaces

Pierre Ferrez

École Polytechnique Fédérale de Lausanne2007

Haptic Feedback Compared with Visual Feedback for BCI

José del Rocio Millán Ruiz

Feedback plays an important role when learning to use a Brain-Computer Interface (BCI). Here we compare visual and haptic feedback in a short experiment. By imagining left and right hand movements, six subjects tried to control a BCI with the help of eithe ...

2006

Human-Brain Interface: Signal Processing and Machine Learning

Touradj Ebrahimi, Ulrich Hoffmann

The Electroencephalogram (EEG) is a recording of the electrical potentials generated by brain activity on the scalp. It has been used for decades as a non-invasive tool both in fundamental brain research and in clinical diagnosis. But it is now widely used ...

John Wiley and Sons Ltd2006

You Are Wrong!---Automatic Detection of Interaction Errors from Brain Waves

José del Rocio Millán Ruiz, Pierre Ferrez

Brain-computer interfaces, as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. In this paper we exploit a unique feat ...

2005

HMM and IOHMM Modeling of EEG Rhythms for Asynchronous BCI Systems

Samy Bengio, Silvia Chiappa

We compare the use of two Markovian models, HMMs and IOHMMs, to discriminate between three mental tasks for brain computer interface systems using an asynchronous protocol. We show that IOHMMs outperform HMMs but that, probably due to the lack of any prior ...

2004

Brain-Computer Interface in Multimedia Communication

Touradj Ebrahimi

This article raises various issues in the design of an efficient BCI system in multimedia applications. The main focus is on one specific modality, namely an electroencephalography (EEG)-based BCI. In doing so, we provide an overview of the most recent pro ...

2003

HMM and IOHMM Modeling of EEG Rhythms for Asynchronous BCI Systems

Samy Bengio, Silvia Chiappa

IDIAP2003