Application and Contrast in Brain-Computer Interface between Hilbert-Huang Transform and Wavelet Transform

Huang, Manling; Wu, Pingdong; Liu, Ying; Bi, Luzheng; Chen, Hongwei

doi:10.1109/icycs.2008.537

Cited by 41 publications

(32 citation statements)

References 3 publications

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“…The HHT is an effective method on non-linear and non-stationary EEG signals. Huang et al [25] proposed a communication between human and computer. They used the HHT and wavelet transform for extracting the features from the steady-state visual evoked potential and indicated the HHT is more accurately expressing the time and frequency characteristics ability than the wavelet transform.…”

Section: Resultsmentioning

confidence: 99%

“…The HT performs to obtain instantaneous frequency and amplitude values of each IMFs in the time-frequency domain [11,24]. The HHT can perform more precise, distinctive and clear results than other methods in the presentation of time-frequency-energy for nonstationary and nonlinear signals [25].…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

“…Each different oscillation in the signal is indicated by an IMF. The IMFs are extracted from the signal by following two basic conditions [24,25]:…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

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Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

Altan

Kutlu

Allahverdi

2016

International Journal of Applied Mathematics, Electronics and Computers

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An electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded from slow cortical potentials (SCP) training in stroke patients using Deep belief network (DBN) which has a greedy layer wise training using Restricted Boltzmann Machines based unsupervised weight and bias evaluation and neural network based supervised training. EEGs are recorded during eight SCP neurofeedback sessions from two stroke patients with a sampling rate of 256 Hz. All EEGs are filtered with a low pass filter. Hilbert-Huang Transform is applied to the trails and various numbers of Instinct Mode Functions (IMFs) are obtained. High order statistics and standard statistics are extracted from IMFs to create the dataset. The proposed DBN-based brain activity classification has discriminated positivity and negativity tasks in stroke patients and has achieved high rates of 90.30%, 96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Hilbert-huang Transformmentioning

confidence: 99%

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Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

Altan

Kutlu

Allahverdi

2016

International Journal of Applied Mathematics, Electronics and Computers

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“…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]. Huang et al [24] stated that the HHT method includes two steps. In the first step, the original data will be transformed into an intrinsic mode function (IMF), which satisfying the requirements of the Hilbert transform, by the method of empirical mode decomposition (EMD).…”

Section: Data Analyzementioning

confidence: 99%

“…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]. Huang et al [24] stated that the HHT method includes two steps.…”

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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