Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes
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Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users� training and control of EEG-based BCI ap ...
Brain-computer interfaces (BCIs) aim to provide a new channel of communication by enabling the subject to control an external systems by using purely mental commands. One method of doing this without invasive surgical procedures is by measuring the electri ...
Objective: To assess the feasibility of recognizing visual spatial attention frames for Brain-computer interfaces (BCI) applications. Methods: EEG data was recorded with 64 electrodes from 2 subjects executing a visual spatial attention task indicating 2 t ...
This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...
By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs d ...
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 ...
In recent years a number of non-invasive Brain-Computer Interfaces have been developed that determine the intent of a subject by analysing the Electroencephalograph(EEG) signals up to frequencies of 40Hz. The use of high frequency EEG features have recentl ...
A direct connection between ElectroEncephaloGram (EEG) and the genetic information of individuals has been investigated by neurophysiologists and psychiatrists since 1960’s; and it opens a new research area in the science. This paper focuses on the person ...
This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...
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 ...