Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

Al–Fahoum, Amjed S.; Alfraihat, Ausilah Ahmad Sulieman

doi:10.1155/2014/730218

Cited by 383 publications

(226 citation statements)

References 29 publications

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“…This application is widely used as it is known to be more efficient than other methods (Autoregressive analyses, Fourier transforms, Frequency distributions, etc. ), especially when signals are unstable (vary in time [32]). This property was used in almost 90% of papers presented, but this was almost always done in combination with other wavelet applications.…”

Section: Discussionmentioning

confidence: 99%

A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

Alonso

López

Suárez-García³

et al. 2017

IJBAS

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Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent psychological disorders in pediatric patients. The actual golden standard of ADHD diagnosis is based on conclusions derived from clinical questionnaires. Nowadays, there is no quantitative measurement performed with any imaging system (MRI, PET, EEG, etc.) that can be considered as a golden standard for this diagnosis. This issue, is highlighted by the existence of international competitions focused on the production of a technological (quantitative) solution capable of complementing ADHD diagnosis (ADHD-200 Global Competition). Wavelet analysis, on the other hand, is a flexible mathematical tool that can be used for information and data processing. Its advantage over other types of mathematical transformations is its ability to decompose a signal into two parameters (frequency and time). Based on the prevalence of ADHD and the extra functionality of wavelet tools, this review will try to answer the following question: How have wavelet analyses been used to complement diagnosis and characterization of ADHD? It will be shown that applications were not casual and limited to time-frequency decomposition, noise removal or down sampling of signals, but were pivotal for construction of learning networks, specific parameterization of signals or calculations of connectivity between brain nodes.

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Section: Discussionmentioning

confidence: 99%

A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

Alonso

López

Suárez-García³

et al. 2017

IJBAS

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show abstract

“…autoregressive models). In general, linear methods can be successfully applied in the study of several problems [3,5,14]. However, despite good results have been obtained with linear techniques, they only provide a limited amount of information about the electrical activity of the brain because they ignore the underlying non-linear EEG dynamics.…”

Section: Eeg Analysis Of Non-linear and Chaotic Characteristicsmentioning

confidence: 99%

Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

Nguyen

Tran

et al. 2018

JCSM

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Current electroencephalogram (EEG)-based methods in security have been mainly used for person authentication and identification purposes only. The non-linear and chaotic characteristics of EEG signal have not been taken into account. In this paper, we propose a new method that explores the use of these EEG characteristics in generating random numbers. EEG signal and its wavebands are transformed into bit sequences that are used as random number sequences or as seeds for pseudo-random number generators. EEG signal has the following advantages: 1) it is noisy, complex, chaotic and non-linear in nature, 2) it is very difficult to mimic because similar mental tasks are person dependent, and 3) it is almost impossible to steal because the brain activity is sensitive to the stress and the mood of the person and an aggressor cannot force the person to reproduce his/her mental pass-phrase. Our experiments were conducted on the four EEG datasets: AEEG, Alcoholism, DEAP and GrazA 2008. The randomness of the generated bit sequences was tested at a high level of significance by comprehensive battery of tests recommended by the National Institute of Standard and Technology (NIST) to verify the quality of random number generators, especially in cryptography application. Our experimental results showed high average success rates for all wavebands and the highest rate is 99.17% for the gamma band.

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“…Some of these are: band powers [14], power spectral density [15], autoregressive and adaptive autoregressive parameters [16], time-frequency features [17][18][19][20] and inverse model-based features [21][22][23]. In 1998, Norden E. Huang from NASA proposed a new signal analysis method named Hilbert-Huang transform (HHT) and it is applied to analyze nonlinear and non-stationary signals and was regarded as an important progress since the fast Fourier transform (FFT) [24].…”

Section: Data Analyzementioning

confidence: 99%

Effects on the Design of Transport Systems of Pedestrian Dynamics

Erkan¹

2017

Highway Engineering

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In the study, transportation-architecture-people-focused triple, urban transportation, design of transportation systems, pedestrian-oriented design and pedestrian walkable spaces will be emphasized. However, by analyzing the effects of pedestrian dynamics, transport systems aim to present a mechanism with an improved model that will define dynamics for the first time to explain the processes underlying design decisions. Four hundred and twelve healthy volunteers were selected from 18-65 years of age. First, threedimensional (3D) virtual city is designed to understand the experiences of the pedestrians. Later, it was provided to navigate the three-dimensional virtual glasses in the city where the broadcasts were designed. During this navigation, pedestrian dynamics were observed, and spaces where pedestrians cautioned were identified. Following this determination, the "attractive" locations will be shown on the macroscale, with the eye-tracking method, it has been in both virtual city navigation and analysis with eye-tracking technology, the cognitive activities of the broadcasts were tested with electroencephalogram (EEG). This approach will in general bridge an empirical and theoretical link between transport-architectural literature in understanding pedestrian movements/behaviors and combining architectural-pedestrian interactions with transport research. However, by analyzing the way-finding behavior, the study interprets its effects on the spatial area.

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Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

Cited by 383 publications

References 29 publications

A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

Effects on the Design of Transport Systems of Pedestrian Dynamics

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