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Canonical correlation analysis (CCA) has been successfully used for extracting frequency components of steady-state visual evoked potential (SSVEP) in electroencephalography (EEG). Recently, a few efforts on CCA-based SSVEP methods have been made to demonstrate the benefits for brain computer interface (BCI). Most of these methods are limited to linear CCA. In this paper consider a deep extension...
Brain computer interface (BCI) is a system for communication between people and computers via brain activity. Steady-state visual evoked potentials (SSVEPs), a brain response observed in EEG, are evoked by flickering stimuli. SSVEP is one of the promising paradigms for BCI. Canonical correlation analysis (CCA) is widely used for EEG signal processing in SSVEP-based BCIs. However, the classification...
Detection of frequency for steady-state visual evoked potentials (SSVEP) is addressed. We propose to use the combination of CCA and training data-based template matching between two level of data adaptive reference signals that can deal with the dominant frequency. On the basis of magnitude of stimulus frequency components, the dominant channels are selected. The recognition accuracy as well as the...
Brain machine interfaces (BMIs) transform modulation of electroencephalogram (EEG) elicited by cognitive and mental events users voluntarily perform into words and commands in accordance with their intents to communicate with somebody else or machines the users want to control. One of the leading paradigms in BMIs includes a method which utilizes the modulation of a steady state visual evoked potentials...
Brain computer interfaces (BCIs) provide an assistive tool for communication between disabled with severe motor disability and care personnel. Although state-of-the-art BCIs have achieved the outstanding improvement in its information transfer rate and classification accuracy, most of the BCIs are difficult to use for patients with impaired oculomotor control due to requirement of visual modality...
A potential for detecting high gamma band (HGB) activity from scalp EEG is explored by employing a high input-impedance electroencephalograph for the measurement. An anti-saccade task was designed to elicit motor-related HGB activity. As a result, we confirmed increased power of HGB (100–105 Hz) preceding the anti-saccade initiation in all three subjects. A common timing of the power increase was...
Visually evoked potentials have attracted great attention in the last two decades for the purpose of brain computer interface design. Visually evoked P300 response is a major signal of interest that has been widely studied. Steady state visual evoked potentials that occur in response to periodically flickering visual stimuli have been primarily investigated as an alternative. There also exists some...
A set of new mental tasks that can be used with Brain Computer Interface systems are introduced. New mental tasks are natural to perform for controlling a cursor on a computer screen to select icons. With the help of three healthy subjects, performance of new mental tasks was evaluated and compared with that of popular motor imagery mental tasks. Three subjects who participated in this study showed...
Autonomous decision making modules in computer vision application allow recognition and classification of different objects, persons, and events in images and video sequences and also make it possible to classify different sensor readings (e.g. images) according to their scientific saliencies. In this paper, we propose a new approach to create the training set for these algorithms by retrieving salient...
A novel brain computer interface (BCI) is implemented in this study, which only depends on auditory modality. The subjects voluntary recognition of the property (e.g. voice laterality) of a target human voice makes the discriminability between brain responses to target and non-target voices in a random sequence. EEG data from eight subjects showed that the amplitude of N2 and late positive component...
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