Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity

Aqil, Muhammad; Hong, Keum–Shik; Jeong, Myung-Yung; Ge, Shuzhi Sam

doi:10.1016/j.neuroimage.2012.07.006

Cited by 62 publications

(55 citation statements)

References 34 publications

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“…Among them, EEG is one of the strongest biomedical signals that has significance and a high practical value for applications in clinical neurology [57]. EEG is a noninvasive method for Various methods have been developed to measure the signal activity generated in the human brain, including EEG, Magnetoencephalography (MEG), functional Magnetic Mesonance Mmaging (f-MRI), function Near-Infrared Spectroscopy (fNIRS) and Positron Emission Tomography (PET) [61,[66][67][68][69][70][71][72]. Among them, EEG is one of the strongest biomedical signals that has significance and a high practical value for applications in clinical neurology [57].…”

Section: Electroencephalogrammentioning

confidence: 99%

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

293

124

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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

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Section: Electroencephalogrammentioning

confidence: 99%

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

293

124

View full text Add to dashboard Cite

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“…A continuous-wave fNIRS instrument (DYNOT; NIRx Medical Technologies, USA) of dual wavelengths (i.e., 760 nm and 830 nm) was used, which was configured to have 1 emitter and 32 detectors at 70 Hz sampling rate [20]. In obtaining the optical density variations, the modified Beer-Lambert law (MBLL) was used [11,16,18,[21][22][23][24][25][26].…”

Section: Equipment and Data Conversionmentioning

confidence: 99%

“…However, in most works related to GLM [4,10,17,18], the canonical HR function was borrowed from the fMRI literature [19]. Recently, a state space model has been utilized to reconstruct the HR to an impulse stimulus from the motor cortex prompted by right index-finger tapping, the model parameters having been estimated by the recursive least-square (RLS) method [20]. However, the coefficient matrices in the model, which determine the characteristics of the canonical HR function, were not well described.…”

Section: Introductionmentioning

confidence: 99%

State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices

Hong¹,

Nguyen²

2014

Biomed. Opt. Express

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Abstract:The paper presents state space models of the hemodynamic response (HR) of fNIRS to an impulse stimulus in three brain regions: motor cortex (MC), somatosensory cortex (SC), and visual cortex (VC). Nineteen healthy subjects were examined. For each cortex, three impulse HRs experimentally obtained were averaged. The averaged signal was converted to a state space equation by using the subspace method. The activation peak and the undershoot peak of the oxy-hemoglobin (HbO) in MC are noticeably higher than those in SC and VC. The time-to-peaks of the HbO in three brain regions are almost the same (about 6.76 76 ± 0.2 s). The time to undershoot peak in VC is the largest among three. The HbO decreases in the early stage (~0.46 s) in MC and VC, but it is not so in SC. These findings were well described with the developed state space equations. Another advantage of the proposed method is its easy applicability in generating the expected HR to arbitrary stimuli in an online (or real-time) imaging. Experimental results are demonstrated.

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“…It is noteworthy that representational connectivity is very different from the rationales of conventional connectivity analyses, such as Kalman filter [37], structural equation model [38], Pearson correlation [39], mutual information [40], or granger causality analysis [41] where the strength in the relation between selected time courses is the main focus. Frequently, the surrogate time courses can be derived either from the averaged temporal series of the voxels in ROIs or from the temporal series of the voxels with peak statistics in ROIs (indicating the voxels with most confident/reliable neural responses).…”

Section: Representational Connectivity and Similarity Searchlight Anamentioning

confidence: 99%

Similarity representation of pattern-information fMRI

Xue

Weng

et al. 2013

Chin. Sci. Bull.

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Representational similarity analysis (RSA) is a rapidly developing multivariate platform to investigate the structure of neural activities. Similarity/dissimilarity is the core concept of RSA, realized by the construction of a representational dissimilarity matrix, that addresses the closeness/distance for each pair of research elements (e.g., one minus the correlation between the brain responses to 2 different stimuli) and in turn, constitutes a multivariate pattern as its analytic foundation. This approach is also welcome for its sensitivity in detecting subtle differences of distributed experimental effects in the brain. Importantly, RSA is not only an experimental tool but a promising data-analytical framework that can integrate cross-modal imaging signals, explore brain-behavior link, and verify computational models according to measured neural activities. RSA substantiates its integrative power by relating similarity structure in one domain (e.g., stimulus features) to that in another domain (e.g., neural activities). This review summarizes dissimilarity/similarity definition of RSA, introduces how to derive the dissimilarity structure in neural response pattern, and carry out connectivity analysis based on RSA platform. Several recent advances are highlighted, such as the extraction of across-subjects regularity, cross-validation of brain reactivity in human beings and monkeys, the incorporation of computational models and behavioral profiles into RSA. Voxel receptor field modeling, another promising multivariate tool of pattern elucidation, is presented and compared. The application of RSA is expected to surge and extend in many fields of neuroscience, computation, psychology and medicine. We also discuss the limitations of RSA and some critical questions that need to be addressed in future research.representational similarity analysis, pattern information, condition-rich experiments, decode Citation:Xue S W, Weng X C, He S, et al. Similarity representation of pattern-information fMRI.

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Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity

Cited by 62 publications

References 34 publications

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices

Similarity representation of pattern-information fMRI

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