Corpus ID: 212528162

Multi-Channel Electroencephalogram (EEG) Signal Acquisition and its Effective Channel selection with De-noising Using AWICA for Biometric System

@inproceedings{Sabarigiri2014MultiChannelE,
  title={Multi-Channel Electroencephalogram (EEG) Signal Acquisition and its Effective Channel selection with De-noising Using AWICA for Biometric System},
  author={B. Sabarigiri and D. SuganyaDevi},
  year={2014}
}
the embedding of low cost electroencephalogram (EEG) sensors in wireless headsets gives improved authentication based on their brain wave signals has become a practical opportunity. In this paper signal acquisition along with effective multi-channel selection from a specific area of the brain and denoising using AWICA methods are proposed for EEG based personal identification. At this point, to develop identification system the steps are as follows. (i) the high-quality device with the least… Expand

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References

SHOWING 1-10 OF 13 REFERENCES
A Hybrid Pre-Processing Techniques for Artifacts Removal to Improve the Performance of Electroencephalogram (EEG) Features Extraction
Electroencephalogram (EEG) blend reflects the summation of the synchronous activity of thousands or millions of neurons that have parallel spatial direction. EEG Signals are extracted from HumanExpand
Analysis of effective channel placement for an EEG-based biometric system
TLDR
Results show that data from eyes open and eyes closed using 4 channels gave good classification rates of 96% and 97% respectively and that data recorded from 2 channels gave classification rates from 90% to 95%. Expand
Selecting Relevant EEG Signal Locations for Personal Identification Problem Using ICA and Neural Network
TLDR
EEG signals are used to identify a person as different persons have different EEG patterns and a practical technique combining independent component analysis (ICA) for signal cleaning and a supervised neural network for classifying signals is proposed. Expand
Cost-Effective EEG Signal Acquisition and Recording System
TLDR
In this paper, EEG signal acquisition principles are discussed, applied, and some suggestions for improving signal acquisition are presented. Expand
Leave-one-out Authentication of Persons Using 40 Hz EEG Oscillations
  • K. Ravi, R. Palaniappan
  • Computer Science
  • EUROCON 2005 - The International Conference on "Computer as a Tool"
  • 2005
TLDR
Two modifications have been proposed to improve the classification performance of 40 Hz EEG oscillations: principal component analysis (PCA) to reduce the noise and background EEG effects from the VEP signals and normalization. Expand
Multitask learning for EEG-based biometrics
  • Shiliang Sun
  • Computer Science
  • 2008 19th International Conference on Pattern Recognition
  • 2008
TLDR
Experimental results on EEG-based personal identification show the effectiveness of the proposed multitask learning approach to biometrics based on electroencephalogram signals. Expand
An Efficient Multimodal Biometric Authentication based on IRIS and Electroencephalogram (EEG)
In current years, IRIS recognition is flattering a very dynamic topic in both research and sensible applications. In this part, fake IRIS is a possible hazard for IRIS based biometric systems. InExpand
Enhancement of Multi-Modal Biometric Authentication Based on IRIS and Brain Neuro Image Coding
Abstract The proposed method describes the current forensics and biometrics in a modern approach and implements the concept of IRIS along with brain and resolves the issues and increases the strengthExpand
Countermeasures against IRIS spoofing and liveness detection using Electroencephalogram (EEG)
The proposed multi-modality human identification system, which fuses the IRIS and Electroencephalogram (EEG). These approaches are the most popular human identification system when they areExpand
Parametric person identification from the EEG using computational geometry
TLDR
Person identification based on features extracted parametrically from the EEG spectrum is investigated, and the correct classification scores obtained are promising in that they corroborate existing evidence that EEG carries genetically specific information and is therefore appropriate as a basis for person identification methods. Expand
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