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Kapgate, Deepak
- P300 Based Brain Computer Interfaces:An Overview
Authors
1 Department of Computer Science and Engineering, Nagpur University, Nagpur, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 6, No 9 (2014), Pagination: 307-310Abstract
Brain computer interface is a direct communication way between the brain and a computer or external devices. BCI can translate user brain activity into corresponding commands for communication with or without using conventional communication. The P300 is an event related potentials which evoked the process of decision making. Here the P300 signals are elicited from the EEG [2] and then further procedure are going to be processed. In this paper, we review the different techniques which are used in the different applications of P300 based BCI system and compare how the P300 is efficient than the other conventional BCIs.Keywords
P300, Brain Computer Interface, Event Related Potentials, Electroencephalography.
- An Overview on SSVEP Based Brain Computer Interfaces
Authors
1 G. H. Raisoni Academy of Engineering and Technology, Department of Computer Science, Nagpur, IN
2 Pune University, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 6, No 9 (2014), Pagination: 311-316Abstract
BCI is a system for communication between brain and computer, in this process the person or subject is need not to be do actual muscular activities for interaction as sending or receiving messages or commands to the computer. Electroencephalography (EEG) along the scalp is the recording of electrical activity. The system is using EEG signals for the interface with brain to the computer. One out of all visual responses Steady-state visually evoked potentials are visually evoked potentials by an external stimulus flickering at fixed frequency. Research focused on Steady State Visual Evoked Potentials (SSVEP) base BCI widely used because of the excellent signal-to-noise ratio (SNR) and relative immunity to artifacts we can use SSVEP. According to various papers the accuracy of the SSVEP signals is very high i.e. 90 ± 8. The goal of this paper is to overview of SSVEP generated frameworks and their processing. The system needs a headset which can capture EEG signals. Usually for generation of SSVEP signals we need at least 10 channels of the headset. There is need of very little training for use of higher ITR and high accuracy for living environment.