Add to ORKGAbstract. 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 the dis-criminant properties of IOHMMs give superior classification performance but that, probably due to the lack of prior knowledge in the design of an appropriate topology, none of these models are able to use temporal information adequately.
International audienceThis paper describes a method to improve uncued Brain-Computer Interfaces base...
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuros...
This paper examines the performance of four classifiers for Brain Computer Interface (BCI) systems b...
We compare the use of two Markovian models, HMMs and IOHMMs, to discriminate between three mental ta...
Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden S...
Brain Computer interfaces are systems that allow the control of external devices using the informati...
In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Mod...
Changes in EEG power spectra related to the imagination of movements may be used to build up a direc...
ObjectiveSupport vector machines (SVM) have developed into a gold standard for accurate classificati...
This thesis explores latent-variable probabilistic models for the analysis and classification of ele...
This thesis explores latent-variable probabilistic models for the analysis and classification of ele...
In this paper we investigate the use of a temporal extension of Independent Component Analysis (ICA)...
A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing t...
Modern electrophysiological studies in animals show that the spectrum of neural oscillations encodin...
Brain-computer interface (BCI) research combines neuroscience, motor learning and computer science a...
International audienceThis paper describes a method to improve uncued Brain-Computer Interfaces base...
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuros...
This paper examines the performance of four classifiers for Brain Computer Interface (BCI) systems b...
We compare the use of two Markovian models, HMMs and IOHMMs, to discriminate between three mental ta...
Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden S...
Brain Computer interfaces are systems that allow the control of external devices using the informati...
In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Mod...
Changes in EEG power spectra related to the imagination of movements may be used to build up a direc...
ObjectiveSupport vector machines (SVM) have developed into a gold standard for accurate classificati...
This thesis explores latent-variable probabilistic models for the analysis and classification of ele...
This thesis explores latent-variable probabilistic models for the analysis and classification of ele...
In this paper we investigate the use of a temporal extension of Independent Component Analysis (ICA)...
A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing t...
Modern electrophysiological studies in animals show that the spectrum of neural oscillations encodin...
Brain-computer interface (BCI) research combines neuroscience, motor learning and computer science a...
International audienceThis paper describes a method to improve uncued Brain-Computer Interfaces base...
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuros...
This paper examines the performance of four classifiers for Brain Computer Interface (BCI) systems b...