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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 ...
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently develope ...
There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rel ...
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 ...
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently develope ...
Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) tri ...
This paper presents a general method for incorporating prior knowledge into kernel methods. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assuming that all points in the given ...
Rat spinal networks generate a spontaneous rhythmic output directed to motoneurons under conditions of increased excitation or of disinhibition. It is not known whether these differently induced rhythms are produced by a common rhythm generator. To investi ...
Brain activity recorded non-invasively is sufficient to control a moblie robot if advanced robotics is used in combination with asynchronous EEG analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted elect ...
Institute of Electrical and Electronics Engineers2004
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 ...
