Personal Identification by EEG Using ICA and Neural Network
نویسندگان
چکیده
The problem of identifying a person using biometric data is interesting. In this paper, the uniqueness of EEG signals of individuals is used to determine personal identity. EEG signals can be measured from different locations, but too many signals can degrade the recognition speed and accuracy. A practical technique combining Independent Component Analysis (ICA) for signal cleaning and a supervised neural network for classifying signals is proposed. From 16 EEG different signal locations, four truly relevant locations F7, C3, P3, and O1 were selected. This selection can identify a group of 20 persons with high accuracy.
منابع مشابه
Evaluation of the Hidden Markov Model for Detection of P300 in EEG Signals
Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool between humans and machines. Most brain-computer interface (BCI) systems use the P300 component, which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for detection of P300. Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...
متن کاملIdentifying Flow Units Using an Artificial Neural Network Approach Optimized by the Imperialist Competitive Algorithm
The spatial distribution of petrophysical properties within the reservoirs is one of the most important factors in reservoir characterization. Flow units are the continuous body over a specific reservoir volume within which the geological and petrophysical properties are the same. Accordingly, an accurate prediction of flow units is a major task to achieve a reliable petrophysical description o...
متن کاملDiscriminating Mental States Using EEG Represented by Power Spectral Density
Artificial neural networks were trained to classify segments of 12 channel EEG data into one of five classes corresponding to five cognitive tasks performed by one subject. Three-layer feedforward neural networks were trained using a validation set to control over-fitting. Independent Component Analysis (ICA) was used to segregate obvious artifactual EEG components from other sources, and a fre...
متن کاملPerformance Analysis of Epileptic Seizure Detection Using DWT & ICA with Neural Networks
The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The aim of this work is compare the automatic detection of EEG patterns using Discrete wavelet...
متن کاملPredicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm
Background: The effects of trace elements on human health and the environment gives importance to the analysis of heavy metals contamination in environmental samples and, more particularly, human food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn) contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network...
متن کامل