Rajesh Chandrasekhara Panicker

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In this paper, an asynchronous brain-computer interface (BCI) system combining the P300 and steady-state visually evoked potentials (SSVEPs) paradigms is proposed. The information transfer is accomplished using P300 event-related potential paradigm and the control state (CS) detection is achieved using SSVEP, overlaid on the P300 base system. Offline and(More)
A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based brain-computer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fisher's linear discriminant analysis and Bayesian linear discriminant analysis progressively teaching each other to build a final classifier, which is(More)
—With embedded systems becoming ubiquitous, there is a growing need to teach and train engineers to be well-versed in their design and development. The multidisciplinary nature of such systems makes it challenging to give students exposure to and experience in all their facets. This paper proposes a generic architecture, containing multiple processors, that(More)
A Brain Computer Interface (BCI) is a system that allows direct communication between a computer and the human brain. Though the main application for BCIs is in rehabilitation of disabled patients, they are increasingly being used in other application scenarios as well. Most of the current BCI systems are based on personal computers. However, there is an(More)
An asynchronous hybrid brain-computer interface (BCI) system combining the P300 and steady-state visually evoked potentials (SSVEP) paradigms is introduced. A P300 base system is used for information transfer, and is augmented to include SSVEP for control state detection. The proposed system has been validated through off-line and online experiments. It is(More)
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