Increased information transmission during scientific hypothesis generation: Mutual information analysis of multichannel EEG

Jin, Songwan; Kwon, Yong‐Ju; Jeong, Jin-Su; Kwon, Suk Won; Shin, Dong-Hoon

doi:10.1016/j.ijpsycho.2006.06.003

Cited by 21 publications

(14 citation statements)

References 31 publications

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“…However, we can obtain asymmetric MI, called time-delayed mutual information (TDMI) by adding a time delay in one of the variables using the following equations (Kwapien et al, 1998; Jeong et al, 2001; Jin et al, 2006a, b; Min et al, 2003; Na et al, 2002; Nichols et al, 2006)…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have examined the function or information transmission between brain areas during object and emotional recognition tasks (Ioannides, 2001, Ioannides et al, 2000), odor stimulation in subjects classified by occupation (Min et al, 2003), and the scientific hypothesis generation process in gifted or normal children (Jin et al, 2006a, b). Hinrichs et al (2006, 2008) used directed information flow (DIF) as a model free approach of information flow to measure causal interactions in event related fMRI, EEG, and MEG experiments.…”

Section: Introductionmentioning

confidence: 99%

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Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction

Jin

Lin

Hallett

2010

Clinical Neurophysiology

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Objective-To propose a model-free method to show linear and nonlinear information flow based on time delayed mutual information (TDMI) by employing uni-and bi-variate surrogate tests and to investigate whether there are contributions of the nonlinear information flow in corticomuscular (CM) interaction.Methods-Using simulated data, we tested whether our method would successfully detect the direction of information flow and identify a relationship between two simulated time series. As an experimental data application, we applied this method to investigate CM interaction during a right wrist extension task.Results-Results of simulation tests show that we can correctly detect the direction of information flow and the relationship between two time series without a prior knowledge of the dynamics of their generating systems. As experimental results, we found both linear and nonlinear information flow from contralateral sensorimotor cortex to muscle.Conclusions-Our method is a viable model-free measure of temporally varying causal interactions that is capable of distinguishing linear and nonlinear information flow. With respect to experimental application, there are both linear and nonlinear information flows in CM interaction from contralateral sensorimotor cortex to muscle, which may reflect the motor command from brain to muscle.Significance-This is the first study to show separate linear and nonlinear information flow in CM interaction. KeywordsLinear and nonlinear information flow; Time delayed mutual information; Surrogate tests; Corticomuscular interaction; Model-free connectivity measure

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction

Jin

Lin

Hallett

2010

Clinical Neurophysiology

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“…1,4,6,15,19,45 Nevertheless, many recent studies have also studied nonlinear functional connectivity. 3,9,24,25,30,[41][42][43] An alternative to quantify nonlinear coupling is the mutual information function (MIF), which assesses temporal dependencies in terms of information transfer as a function of a time horizon (s). In other words, MIF estimates the predictability over this time horizon, either in a single time series (auto mutual information function) or between two time series (cross mutual information function, CMIF).…”

Section: Introductionmentioning

confidence: 99%

MEG Connectivity Analysis in Patients with Alzheimer’s Disease Using Cross Mutual Information and Spectral Coherence

et al. 2010

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Alzheimer's disease (AD) is an irreversible brain disorder which represents the most common form of dementia in western countries. An early and accurate diagnosis of AD would enable to develop new strategies for managing the disease; however, nowadays there is no single test that can accurately predict the development of AD. In this sense, only a few studies have focused on the magnetoencephalographic (MEG) AD connectivity patterns. This study compares brain connectivity in terms of linear and nonlinear couplings by means of spectral coherence and cross mutual information function (CMIF), respectively. The variables defined from these functions provide statistically significant differences (p < 0.05) between AD patients and control subjects, especially the variables obtained from CMIF. The results suggest that AD is characterized by both decreases and increases of functional couplings in different frequency bands as well as by an increase in regularity, that is, more evident statistical deterministic relationships in AD patients' MEG connectivity. The significant differences obtained indicate that AD could disturb brain interactions causing abnormal brain connectivity and operation. Furthermore, the combination of coherence and CMIF features to perform a diagnostic test based on logistic regression improved the tests based on individual variables for its robustness.

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“…In addition, as estimation of current source being induced from deep cerebral structures such as hippocampal and subgenual cingulate foci is enabled, a study on both cognitive and sensory area is enabled as well. Contrary to existing study of frequency analysis through potential being represented in scalp (Kwon et al, 2007;Jin et al, 2006a;2006b) …”

Section: ) Sloreta (Standardized Low Resolution Brain Electromagnetimentioning

confidence: 87%

Increased information transmission during scientific hypothesis generation: Mutual information analysis of multichannel EEG

Cited by 21 publications

References 31 publications

Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction

Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction

MEG Connectivity Analysis in Patients with Alzheimer’s Disease Using Cross Mutual Information and Spectral Coherence

Differences in Neural Current Sources of Science Gifted and Normal Children in Creative Reasoning

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