State‐of‐the‐art methods and future perspectives for personal recognition based on electroencephalogram signals

Abstract

In the past decade, biomedical instrumentations have witnessed major developments and now it is very easy to measure human biomedical electrical signals. One of these signals is the brain waves, known as electroencephalogram (EEG) signals, which became very easy to be measured using portable devices and dry electrodes. This opens the way for the use of brain waves in different applications rather than the biomedical diagnosis. One of the most recent nonmedical applications for brain waves is the biometric authentication. Brain waves have some advantages which are not present in the commonly used identifiers, such as face and fingerprints, making them robust to spoof attacks. However, brain waves still face many challenges with reference to permanence and uniqueness. In this study, the authors discuss the employment of brain signals for human recognition tasks and focus on the challenges facing these signals towards the deployment of a practical biometric system. This study, also, provides a comprehensive review of the proposed approaches developed in EEG-based biometric authentication systems.

User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features

“…Extraction of EEG features is conducted in different domains such as the time domain or the frequency domain. The most used feature extraction methods for EEG biometric systems are AR modeling, Power Spectral Density (PSD), the energy of EEG channels and wavelet packet decomposition (WPD) [3].…”

“…Equation (3) is used to calculate the energy distribution to the sliced power spectra (i.e. AC components) [16], [3], and [17].…”

Brainwaves for User Verification using Two Separate Sets of Features based on DCT and Wavelet

“…The taken EEG signals duration is 10 sec. with sampling rate of (250 sample/sec) [11]. This dataset holds an error that occurred in one of subjects (i.e., 4 th ) in letter composing trails [12], [10].…”

“…DFT's power spectra consist of the sine and cosine components, while DCT use only the cosine functions, it is a Fourier related but just using real numbers [15], [11]. The DCT's general mapping equation is given by (1), while DFT's general mapping equation is given by (2):…”

“…The wavelet transform computes the inner products of a signal with a family of wavelets to decompose the EEG signal (to scale-shift domain) with keeping location in time information; unlike DFT and DCT which maps the input signal to frequencies that making it up regardless of time information. DWT uses two filters (i.e., high pass filter and low pass filter) [17], [11]. Three types of wavelet transform were proposed in the previous work [4]; the first one is Haar Wavelet transform which is the simplest wavelet type, it computes the sums and differences of input signal, the low and high filters of HWT is given by (4) and (5) [17]:…”