Human Identification from Brain EEG signals Using Advanced Machine Learning Algorithms


Category: Image Processing projects

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EEG-based human recognition is increasingly
becoming a popular modality for biometric authentication. Two
important features of EEG signals are liveliness and the
robustness against falsification. However, a comprehensive study
on human authentication using EEG signal is still remains. On
the other hand, low-cost wireless EEG recording devices are now
growing in the market places. Although these devices have the
potential to many applications, researches have yet to be done to
find the feasibility of these devices. In this study, we propose a
method for human identification using EEG signals obtained
from such low-cost devices. EEG signal is first preprocessed to
remove noise and artifacts using Bandpass FIR filter. These
signals are then divided into disjoint segments. Three feature
extraction methods, namely multiscale shape description (MSD),
multiscale wavelet packet statistics (WPS) and multiscale wavelet
packet energy statistics (WPES) are then applied. These features
are finally used to train a supervised error-correcting output
code multiclass model (ECOC) using support vector machine
(SVM) classifier, which ultimately can recognize humans from
test EEG signals. A preliminary experiment with 9 EEG records
from 9 subjects shows the true positive rate of 94.44% of the
proposed method.

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