The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Using brain-computer interfaces (BCIs) to improve human performance has become a state-of-the-art research topic. The concept of collaborative BCIs, which aimed to use multi-brain computing to enhance human performance, was proposed recently. To further study the feasibility of collaborative BCIs, here we propose to develop an online collaborative BCI to accelerate human response to visual target...
Steady-state visual evoked potential (SSVEP) has been widely applied in brain computer interface (BCI) systems. The amplitude and phase features of SSVEP were commonly extracted by Fourier analysis method from single-channel EEG data. In the multichannel case, canonical correlation analysis (CCA) has been utilized for the analysis of frequency coding SSVEP. This paper presents the analysis of phase...
Frequency coding has been the traditional method implemented in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). However, it is limited in terms of possible target numbers and, consequently, inappropriate for certain applications involving liquid crystal display (LCD) with multiple stimuli. This paper proposes an innovative coding method for SSVEP that, through a...
We present a novel method for idle and work states classification in brain computer interface (BCI) based on steady-state visual evoked potentials (SSVEP). Canonical correlation analysis (CCA) and maximum contrast combination (MCC) are used to extract features of electroencephalogram (EEG) signals. The correlation coefficients from CCA and SNR from MCC were classified by a linear classifier. Then...
A brain-actuated human computer interface for Google search is proposed in this study. Aiming at increasing system simplicity and flexibility, a steady-state visual evoked potential based system was developed by integrating simplified EEG cap, compact wireless EEG amplifier, flexible visual stimulator and user-friendly software design. A practical application for Google search which involved character...
Modulation of steady-state visual evoked potential (SSVEP) by directing gaze to targets flickering at different frequencies has been utilized in many brain-computer interface (BCI) studies. However, this paradigm may not work with patients suffering from complete locked-in syndrome or other severe motor disabilities that do not allow conscious control of gaze direction. In this paper, we present a...
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...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.