Humans need communication. The desire to communicate remains one of the
primary issues for people with locked-in syndrome (LIS). While many assistive
and augmentative communication systems that use various physiological signals
are available commercially, the need is not satisfactorily met. Brain
interfaces, in particular, those that utilize event related potentials (ERP) in
electroencephalography (EEG) to detect the intent of a person noninvasively, are
emerging as a promising communication interface to meet this need where existing
options are insufficient. Existing brain interfaces for typing use many
repetitions of the visual stimuli in order to increase accuracy at the cost of
speed. However, speed is also crucial and is an integral portion of peer-to-peer
communication; a message that is not delivered timely often looses its
importance. Consequently, we utilize rapid serial visual presentation (RSVP) in
conjunction with language models in order to assist letter selection during the
brain-typing process with the final goal of developing a system that achieves
high accuracy and speed simultaneously. This paper presents initial results from
the RSVP Keyboard system that is under development. These initial results on
healthy and locked-in subjects show that single-trial or few-trial accurate
letter selection may be possible with the RSVP Keyboard paradigm.
A classification system typically consists of both a feature extractor (preprocessor) and a classifier. These two components can be trained either independently or simultaneously. The former option has an implementation advantage since the extractor need only be trained once for use with any classifier, whereas the latter has an advantage since it can be used to minimize classification error directly. Certain criteria, such as Minimum Classification Error, are better suited for simultaneous training, whereas other criteria, such as Mutual Information, are amenable for training the feature extractor either independently or simultaneously. Herein, an information-theoretic criterion is introduced and is evaluated for training the extractor independently of the classifier. The proposed method uses nonparametric estimation of Renyi's entropy to train the extractor by maximizing an approximation of the mutual information between the class labels and the output of the feature extractor. The evaluations show that the proposed method, even though it uses independent training, performs at least as well as three feature extraction methods that train the extractor and classifier simultaneously.
This paper proposes a novel prewhitening eigenspace beamformer suitable for magnetoencephalogram (MEG) source reconstruction when large background brain activities exist. The prerequisite for the method is that control-state measurements, which contain only the contributions from the background interference, be available, and that the covariance matrix of the background interference can be obtained from such control-state measurements. The proposed method then uses this interference covariance matrix to remove the influence of the interference in the reconstruction obtained from the target measurements. A numerical example, as well as applications to two types of MEG data, demonstrates the effectiveness of the proposed method.
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