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• W2747677555 abstract "Nowadays, many people are affected by the neuromuscular problems like stroke, Parkinson's disease etc. around the world. Brain Computer Interface plays a major role for the communication between the patient and the real time environment. In this proposed paper noninvasive BCI method is used to detect the consciousness of the patient who is in the unconscious state for a longer period using the P300 potential. With the application of visual stimulus of bright light in patients eyes, measurement of EEG waves are done by placing the electrodes in the occipital lobe. Median filter is used for preprocessing algorithm to remove the artifacts due to noise. Finite Impulse Response filter is used for extracting the features of EEG waves to detect the P300 event when the patient is in the awakening state. Fisher Linear Discriminator (FLDA) is the classifier used in which the local maximum and local minimum of the signal at each rate of interests were found. To achieve the higher accuracy for processing the brain waves the above algorithms are used. This proposed algorithm is implemented in various devices and compared in terms of area, power and delay. From the result it is concluded that Virtex-5 is best since it consumes 0.67 W and it is able to detect the P300 potential which occurs when the patient becomes conscious within a minimum period of 10ns. The proposed model is implemented in the Virtex-5 FPGA." @default.
• W2747677555 created "2017-08-31" @default.
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• W2747677555 date "2017-01-01" @default.
• W2747677555 modified "2023-09-27" @default.
• W2747677555 title "Feature extraction for the patient consciousness detection using P300 event" @default.
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• W2747677555 doi "https://doi.org/10.1109/icaccs.2017.8014566" @default.
• W2747677555 hasPublicationYear "2017" @default.
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