Hilbert transform assisted complex wavelet transform for neuroelectric signal analysis

Olkkonen, H.; Pesola, P.; Olkkonen, Juuso; Zhou, Hui

doi:10.1016/j.jneumeth.2005.06.028

Cited by 27 publications

(21 citation statements)

References 13 publications

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“…5). The energy of the signal in different scales can be estimated with the aid of the Hilbert transform (Olkkonen et al 2006). Applying the result of this work the energy of the wavelet sequence [] hc wn (14) approaches closely to the energy (envelope) of the signal.…”

Section: Discussionmentioning

confidence: 99%

“…Applying the result of this work the energy of the wavelet sequence [] hc wn (14) approaches closely to the energy (envelope) of the signal. However, the delayed wavelet sequence is produced only by the polyphase filter () N Pz (N=1,2,…,M-1)(12), while the Hilbert transform requires the FFT based signal processing (Olkkonen et al 2006). In the EEG signal recorded from the frontal cortex, the spindle waves have concentrated energy, which is clearly revealed both by the FD BDWT and the Hilbert transform analysis (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…For comparison the EEG signals were analysed using the well established Hilbert transform assisted complex wavelet transform (Olkkonen et al 2006). The EEG recording method is described in detail in our previous work (Olkkonen et al 2006). The EEG signals were treated using the BDWT bank given in (Olkkonen et al 2005).…”

Section: Methodsmentioning

confidence: 99%

“…5B shows the energy (absolute value) of the wavelet coefficients and Fig. 5C the energy of the wavelet coefficients computed via the Hilbert transform method (Olkkonen et al 2006). …”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Shift Invariant Biorthogonal Discrete Wavelet Transform for EEG Signal Analysis

Olkkonen¹,

Olkkonen²

2011

Discrete Wavelet Transforms - Theory and Applications

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…5B shows the energy (absolute value) of the wavelet coefficients and Fig. 5C the energy of the wavelet coefficients computed via the Hilbert transform method (Olkkonen et al 2006). …”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Shift Invariant Biorthogonal Discrete Wavelet Transform for EEG Signal Analysis

Olkkonen¹,

Olkkonen²

2011

Discrete Wavelet Transforms - Theory and Applications

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“…In this article, we use a wavelet-based algorithm for the classification of EEG signals for different artifacts versus groundtruth sequences and analyze the overall emotional response to the video. Our main contribution is a method for processing the EEG data using wavelets as proposed by Olkkonen et al [2006] and then an SVM to classify a single trial based on the type of distortion. We show that for typical IBR artifacts in an image sequence it is possible to differentiate a single trial based on the type of the artifact viewed and determine the perceived quality of the video.…”

Section: Introductionmentioning

confidence: 99%

Single-trial EEG classification of artifacts in videos

Mustafa

Guthe

Magnor

2012

ACM Trans. Appl. Percept.

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In this article we use an ElectroEncephaloGraph (EEG) to explore the perception of artifacts that typically appear during rendering and determine the perceptual quality of a sequence of images. Although there is an emerging interest in using an EEG for image quality assessment, one of the main impediments to the use of an EEG is the very low Signal-to-Noise Ratio (SNR) which makes it exceedingly difficult to distinguish neural responses from noise. Traditionally, event-related potentials have been used for analysis of EEG data. However, they rely on averaging and so require a large number of participants and trials to get meaningful data. Also, due the the low SNR ERP's are not suited for single-trial classification.We propose a novel wavelet-based approach for evaluating EEG signals which allows us to predict the perceived image quality from only a single trial. Our wavelet-based algorithm is able to filter the EEG data and remove noise, eliminating the need for many participants or many trials. With this approach it is possible to use data from only 10 electrode channels for single-trial classification and predict the presence of an artifact with an accuracy of 85%. We also show that it is possible to differentiate and classify a trial based on the exact type of artifact viewed. Our work is particularly useful for understanding how the human visual system responds to different types of degradations in images and videos. An understanding of the perception of typical image-based rendering artifacts forms the basis for the optimization of rendering and masking algorithms.

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Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms

et al. 2009

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As a kind of complicated mechanical component, rolling element bearing plays a significant role in rotating machines, and bearing fault detection benefits decision-making of maintenance and avoids undesired downtime cost. However, extraction of fault signatures from a collected signal in a practical working environment is always a great challenge. This paper proposes an improved combination of the Hilbert and wavelet transforms to identify early bearing fault signatures. Real rail vehicle bearing and motor bearing data were used to validate the proposed method. A traditional combination of Hilbert and wavelet transforms was employed for comparison purpose. An indicator to evaluate fault detection capability of methods was developed in this research. Analysis results showed that the extraction capability of bearing fault signatures is greatly enhanced by the proposed method.

show abstract

Hilbert transform assisted complex wavelet transform for neuroelectric signal analysis

Cited by 27 publications

References 13 publications

Shift Invariant Biorthogonal Discrete Wavelet Transform for EEG Signal Analysis

Shift Invariant Biorthogonal Discrete Wavelet Transform for EEG Signal Analysis

Single-trial EEG classification of artifacts in videos

Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms

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