Brain order disorder 2<sup>nd</sup>group report of f-EEG

Lalonde, François; Gogtay, Nitin; Giedd, Jay N.; Vydelingum, N.; Brown, David G.; Tran, Binh Q.; Hsu, Charles C.; Hsu, Ming-Kai; Cha, Jae Sang; Jenkins, Jeffrey L.; Ma, Lien; Willey, Jeff; Wu, Jerry; Oh, Kenneth; Landa, Joseph S.; Lin, Chia-Wei; Jung, Tzyy-Ping; Makeig, Scott; Morabito, Francesco Carlo; Moon, Qyu; Yamakawa, Takeshi; Lee, Sang Yup; Lee, Jong‐Hwan; Szu, Harold; Kaur, Bhavneet; Byrd, Kenneth A.; Dang, Karen; Krzywicki, Alan T.; Familoni, Babajide O.; Larson, Louis; Harkrider, Susan; Krapels, Keith A.; Dai, Liyi

doi:10.1117/12.2051706

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the use of entropy-based metrics in the described context is completely justified. Indeed, these kinds of indices have already shown an interesting ability to reveal useful clinical information in mental disorders, such as Alzheimer's [26,28,29], epilepsy [30] or depression [31].…”

Section: Introductionmentioning

confidence: 99%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

View full text Add to dashboard Cite

Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain's behavior under this negative emotion.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

View full text Add to dashboard Cite

show abstract

“…However, even with externally-placed sensors, increased sensor density is encouraging a greater range of applications, ranging from entertainment to the detection of order–disorder transitions in the brain [31,32]. Maren and Szu describe how a CVM-based approach to modeling EEG signals can be used in a dry-mounted commercially-available EEG recording system, suitable for daily use [33].…”

Section: Introductionmentioning

confidence: 99%

The Cluster Variation Method: A Primer for Neuroscientists

Maren

2016

Brain Sciences

View full text Add to dashboard Cite

Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.

show abstract

Nonlinear Symbolic Assessment of Electroencephalographic Recordings for Negative Stress Recognition

García-Martínez

Martínez-Rodrigo

Fernández-Caballero

et al. 2017

Natural and Artificial Computation for Biomedicine and Neuroscience

View full text Add to dashboard Cite

Brain order disorder 2^ndgroup report of f-EEG

Cited by 4 publications

References 7 publications

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

The Cluster Variation Method: A Primer for Neuroscientists

Nonlinear Symbolic Assessment of Electroencephalographic Recordings for Negative Stress Recognition

Contact Info

Product

Resources

About

Brain order disorder 2ndgroup report of f-EEG

Cited by 4 publications

References 7 publications

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

The Cluster Variation Method: A Primer for Neuroscientists

Nonlinear Symbolic Assessment of Electroencephalographic Recordings for Negative Stress Recognition

Contact Info

Product

Resources

About

Brain order disorder 2^ndgroup report of f-EEG