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It is impossible for severely disabled people to browse or learn through the Internet due to the mere lack of independent control of the mouse. This paper proposes a brain computer interface (BCI) to aid severely disabled individuals, such as people disabled by amyotrophic lateral sclerosis (ALS), in browsing or learning on the Internet. By analyzing specific components of event-related potentials...
The P300 speller is one of the brain-computer interfaces that uses the P300 event-related potential. The performance of the P300 speller is influenced by various parameters related to the matrix displayed. In addition, it is reported that letters in the corner or at the edge of the matrix can be concentrated on more easily, giving a higher accuracy rate for the P300 speller. In order to improve the...
The detection of Steady-State Visual Evoked Potential (SSVEP) responses in the Electroencephalogram (EEG) is a current challenge in signal processing applied on Brain-Computer Interfaces (BCI). BCI based on SSVEP requires visual stimuli. When these stimuli are displayed on an LCD screen, the number of frequencies for flickering object on the screen is limited. We propose to extend the number of frequencies...
The aim of this study performed on 20 able-bodied right-handed volunteers was to test whether motor imagery questionnaires can be used as a method to detect Brain-Computer Interface (BCI) illiteracy. Participants performed a cue-based kinesthetic imagination of their left and their right hand. Features chosen for classification were power spectral densities in two frequency bands recorded bipolarly...
Brain-Computer Interface (BCI) systems allow user to operate devices without muscular activation and experimental results indicate that they can induce activity-dependent plasticity. A BCI system has two key features: exploitation of brain signal changes induced by the cognitive task assigned to the user and mutual learning. With respect to the first feature, BCI users can learn to control and hence...
Considering of high transmission rate and short training time, Steady State Visual Evoked Potential (SSVEP) rapidly becomes a practical signal in Brain-Computer Interface(BCI) system. This paper study the extraction method of SSVEP based on the Hilbert-Huang Transformation. The SSVEP was processed by a time-frequency processing system. after empirical mode decomposition and Hilbert-Huang Transform(HHT),...
In this paper, feature selection was carried out for multi-intelligence classification, and finds key regions. We designed different multi-intelligence tasks with BCI. SVM was used to classify and select features. The experiment reveals that a band has a greater effect on imagery intelligent tasks. And the introduced feature selection algorithm succeeded to detect key regions for multi-intelligence...
It is known that Electroencephalograph (EEG) signal shows specific responses according to the event (e.g. visual stimulus, cognition and motor imagery). Especially, by classifying short time EEG signal, features are used to control an electronic device (such system is called brain computer interface: BCI). In this study, we tried to construct SSVEP type EEG spelling system which uses response with...
A method of displaying robust visual stimulator for P300-BCI is proposed. This visual stimulator plays vital role in BCI system since the evoked potential with duration of 300 ms is mainly affected by the precise timing of each trial. It is preferable that the number of trials must be set as small as possible and it means that the stimulator which provides the random stimuli must operate in the very...
Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) are systems in which virtual or physical objects are tagged with flicker of different frequencies. When a user focuses on one of the objects its flicker frequency becomes visible in the electroencephalogram (EEG) and so the object on which the user focuses can be determined from brain activity alone. A significant...
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