Supervised piecewise network connectivity analysis for enhanced confidence of auditory oddball tasks

Padilla-Buriticá, Jorge Ivan; Hurtado, Juana Valeria; Castellanos-Domínguez, G.

doi:10.1016/j.bspc.2019.04.020

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…To cope with this issue, we consider two strategies for improving the DRN robustness: Firstly, the thresholding method is incorporated, usually performed in functional connectivity analysis at the preprocessing stage, to remove false connections and noise. Following the procedure in [ 53 ], we fix the proportional thresholding rule to

, preserving a sufficient amount of links under a value of p ≤ 0.1. Secondly, the leave-one-out cross-validation is further refined by incorporating the Monte Carlo dropout layers, containing neurons with a probability of being ignored during training and validation.…”

Section: Resultsmentioning

confidence: 99%

Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators

Caicedo-Acosta

Castaño

Acosta-Medina

et al. 2021

Sensors

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Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.

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Section: Resultsmentioning

confidence: 99%

Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators

Caicedo-Acosta

Castaño

Acosta-Medina

et al. 2021

Sensors

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“…Nevertheless, the influential non-stationarity nature of EGG data rules a high variability between trial sets, fluctuating on multiple timescales that range from milliseconds to seconds (Lang et al, 2012). To meet this condition, the estimator in Equation 4is performed by adjusting the short-time window to a small length, τ ζ = 0.1 s as presented in Padilla-Buritica et al (2019). Of note, all connectivity assessments are computed using the FielTrip toolbox (Oostenveld et al, 2011).…”

Section: Single-subject Dynamics Extracted By Functional Connectivitymentioning

confidence: 99%

Dynamic Modeling of Common Brain Neural Activity in Motor Imagery Tasks

Velásquez-Martínez

Zapata-Castaño

Castellanos-Domínguez

2020

Front. Neurosci.

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Evaluation of brain dynamics elicited by motor imagery (MI) tasks can contribute to clinical and learning applications. The multi-subject analysis is to make inferences on the group/population level about the properties of MI brain activity. However, intrinsic neurophysiological variability of neural dynamics poses a challenge for devising efficient MI systems. Here, we develop a time-frequency model for estimating the spatial relevance of common neural activity across subjects employing an introduced statistical thresholding rule. In deriving multi-subject spatial maps, we present a comparative analysis of three feature extraction methods: Common Spatial Patterns, Functional Connectivity, and Event-Related De/Synchronization. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: (i) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; (ii) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand vs. right-hand). The obtained validation results indicate that the estimated collective dynamics differently reflect the flow of sensorimotor cortex activation, providing new insights into the evolution of MI responses.

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