Nonlinear System Identification of Neural Systems from Neurophysiological Signals

He, Fei; Yang, Yuan

doi:10.1016/j.neuroscience.2020.12.001

Cited by 41 publications

(22 citation statements)

References 162 publications

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“…The linear EEG analysis can evaluate the communication between neural networks in the same oscillating frequency band or similar neuron firing patterns. However, it is not clear how much information is missing since the behavior of neural network can be highly nonlinear and nonstationary (Yang et al, 2018;He and Yang, 2021). Thus, nonlinear analysis methods like entropy and complexity are more suitable for EEG feature extraction than the power spectral based linear analysis.…”

Section: Discussionmentioning

confidence: 99%

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Wan

Cui

Gao

et al. 2021

Front. Hum. Neurosci.

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Measuring and identifying the specific level of sustained attention during continuous tasks is essential in many applications, especially for avoiding the terrible consequences caused by reduced attention of people with special tasks. To this end, we recorded EEG signals from 42 subjects during the performance of a sustained attention task and obtained resting state and three levels of attentional states using the calibrated response time. EEG-based dynamical complexity features and Extreme Gradient Boosting (XGBoost) classifier were proposed as the classification model, Complexity-XGBoost, to distinguish multi-level attention states with improved accuracy. The maximum average accuracy of Complexity-XGBoost were 81.39 ± 1.47% for four attention levels, 80.42 ± 0.84% for three attention levels, and 95.36 ± 2.31% for two attention levels in 5-fold cross-validation. The proposed method is compared with other models of traditional EEG features and different classification algorithms, the results confirmed the effectiveness of the proposed method. We also found that the frontal EEG dynamical complexity measures were related to the changing process of response during sustained attention task. The proposed dynamical complexity approach could be helpful to recognize attention status during important tasks to improve safety and efficiency, and be useful for further brain-computer interaction research in clinical research or daily practice, such as the cognitive assessment or neural feedback treatment of individuals with attention deficit hyperactivity disorders, Alzheimer’s disease, and other diseases which affect the sustained attention function.

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Section: Discussionmentioning

confidence: 99%

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Wan

Cui

Gao

et al. 2021

Front. Hum. Neurosci.

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“…Information theoretic analysis is then “epistemically modest,” as it does not require presupposing particular generating functions or relationships. This makes it ideal for complex, nonlinear systems, such as networks of neurons of brains, where the underlying generative dynamic is unknown and nonlinearities can play a key role ( 31 ).…”

Section: Basic Theory Of Information Dynamicsmentioning

confidence: 99%

Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

Varley

Sporns

Schaffelhofer

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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One of the essential functions of biological neural networks is the processing of information. This includes everything from processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological limitations, it has been historically unclear how information processing changes during different cognitive or behavioral states and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the frontoparietal areas AIP, F5, and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, interareal processing varied widely: During visuomotor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with predominance of processing in the feedback direction. Furthermore, the fine-scale network structure reconfigured at the neuron level in response to different grasping conditions, despite no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand and that the information-processing network is hierarchically organized at the neuron level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.

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“…But, modern studies reported that these linear methods could only deal with limited neural activities and their functional relationships, and therefore, they cannot accurately identify neural behaviors. Therefore, nonlinear methods are essential to investigate neuronal processing and signal transfer more accurately and realistically [73]. Nonlinear approaches can promisingly provide deep insights into neurophysiological mechanisms.…”

Section: A Discussion On Performance Of Different Methodsmentioning

confidence: 99%

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

et al. 2023

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Classifying distinct human emotions, the fundamental purpose of brain-computer interface research, is essential for providing instant personalized services and assistance to individuals. With such emerging applications for individuals, several techniques have been proposed recently to explore interactions between brain regions, such as correlation, synchronization, and dependence. Notably, functional and effective connectivity methods are applied to assess the relationships between different brain areas. The primary objective of this study is to compare the frequently used functional and effective connectivity methods to recognize emotion using Electroencephalogram (EEG) signals. This paper uses a benchmark emotional EEG dataset consisting of 32 channels of EEG signals collected from 32 subjects while they were watching 40 inspirational music videos. Specifically, correlation, phase synchronization, and mutual information are used to measure functional brain connectivity, and transfer entropy is used to acquire effective brain connectivity. After extracting the features, they are represented in a two-dimensional connectivity feature map (CFM). The CFMs are then used to classify emotions by a convolutional neural network model. The results of classified emotions are analyzed regarding compatible EEG bands, accuracy, and time. Notably, the Gamma band is found as the most compatible band. The comparative study has demonstrated that though the connectivity method named Pearson correlation coefficient requires less time, the normalized mutual information is the most accurate method with advantageous detecting capability of nonlinear dependencies.

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Nonlinear System Identification of Neural Systems from Neurophysiological Signals

Cited by 41 publications

References 162 publications

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

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