1
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Ranieri CM, Moioli RC, Vargas PA, Romero RAF. A neurorobotics approach to behaviour selection based on human activity recognition. Cogn Neurodyn 2023; 17:1009-1028. [PMID: 37522044 PMCID: PMC10374508 DOI: 10.1007/s11571-022-09886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 08/04/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.
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Affiliation(s)
- Caetano M. Ranieri
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
| | - Renan C. Moioli
- Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute, Federal University of Rio Grande do Norte, Avenida Senador Salgado Filho, 3000, Natal, RN 59078-970 Brazil
| | - Patricia A. Vargas
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS Scotland, UK
| | - Roseli A. F. Romero
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
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2
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Kozma R, Baars BJ, Geld N. Evolutionary Advantages of Stimulus-Driven EEG Phase Transitions in the Upper Cortical Layers. Front Syst Neurosci 2021; 15:784404. [PMID: 34955771 PMCID: PMC8692947 DOI: 10.3389/fnsys.2021.784404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Spatio-temporal brain activity monitored by EEG recordings in humans and other mammals has identified beta/gamma oscillations (20-80 Hz), which are self-organized into spatio-temporal structures recurring at theta/alpha rates (4-12 Hz). These structures have statistically significant correlations with sensory stimuli and reinforcement contingencies perceived by the subject. The repeated collapse of self-organized structures at theta/alpha rates generates laterally propagating phase gradients (phase cones), ignited at some specific location of the cortical sheet. Phase cones have been interpreted as neural signatures of transient perceptual experiences according to the cinematic theory of brain dynamics. The rapid expansion of essentially isotropic phase cones is consistent with the propagation of perceptual broadcasts postulated by Global Workspace Theory (GWT). What is the evolutionary advantage of brains operating with repeatedly collapsing dynamics? This question is answered using thermodynamic concepts. According to neuropercolation theory, waking brains are described as non-equilibrium thermodynamic systems operating at the edge of criticality, undergoing repeated phase transitions. This work analyzes the role of long-range axonal connections and metabolic processes in the regulation of critical brain dynamics. Historically, the near 10 Hz domain has been associated with conscious sensory integration, cortical "ignitions" linked to conscious visual perception, and conscious experiences. We can therefore combine a very large body of experimental evidence and theory, including graph theory, neuropercolation, and GWT. This cortical operating style may optimize a tradeoff between rapid adaptation to novelty vs. stable and widespread self-organization, therefore resulting in significant Darwinian benefits.
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Affiliation(s)
- Robert Kozma
- Center for Large-Scale Intelligent Optimization and Networks, Department of Mathematics, University of Memphis, Memphis, TN, United States
| | - Bernard J. Baars
- Center for the Future Mind, Florida Atlantic University, Boca Raton, FL, United States
- Society for MindBrain Sciences, San Diego, CA, United States
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3
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Iliopoulos AC, Papasotiriou I. Functional Complex Networks Based on Operational Architectonics: Application on Electroencephalography-Brain-computer Interface for Imagined Speech. Neuroscience 2021; 484:98-118. [PMID: 34871742 DOI: 10.1016/j.neuroscience.2021.11.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: (a) Operational architectonics (OA) concept of brain and mind functioning. (b) Network neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEGs, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). Subsequently, the time coordinates of RTPs are used for the generation of undirected weighted complex networks fulfilling a scale-free topology criterion, from which various network metrics of brain connectivity are estimated. These metrics form feature vectors, which can be used in machine learning algorithms for classification and/or prediction. The method is tested in classification problems on an EEG-based BCI data set, acquired from individuals during imagery pronunciation tasks of various words/vowels. The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.
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Affiliation(s)
- A C Iliopoulos
- Research Genetic Cancer Centre S.A. Industrial Area of Florina, 53100 Florina, Greece
| | - I Papasotiriou
- Research Genetic Cancer Centre International GmbH, Zug 6300, Switzerland.
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4
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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5
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Diffuse neural coupling mediates complex network dynamics through the formation of quasi-critical brain states. Nat Commun 2020; 11:6337. [PMID: 33303766 PMCID: PMC7729877 DOI: 10.1038/s41467-020-19716-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 10/28/2020] [Indexed: 01/17/2023] Open
Abstract
The biological mechanisms that allow the brain to balance flexibility and integration remain poorly understood. A potential solution may lie in a unique aspect of neurobiology, which is that numerous brain systems contain diffuse synaptic connectivity. Here, we demonstrate that increasing diffuse cortical coupling within a validated biophysical corticothalamic model traverses the system through a quasi-critical regime in which spatial heterogeneities in input noise support transient critical dynamics in distributed subregions. The presence of quasi-critical states coincides with known signatures of complex, adaptive brain network dynamics. Finally, we demonstrate the presence of similar dynamic signatures in empirical whole-brain human neuroimaging data. Together, our results establish that modulating the balance between local and diffuse synaptic coupling in a thalamocortical model subtends the emergence of quasi-critical brain states that act to flexibly transition the brain between unique modes of information processing.
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6
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A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test. Cogn Neurodyn 2020; 15:621-636. [PMID: 34367364 DOI: 10.1007/s11571-020-09646-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 09/07/2020] [Accepted: 10/21/2020] [Indexed: 10/23/2022] Open
Abstract
Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test-retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.
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7
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Idowu OP, Huang J, Zhao Y, Samuel OW, Yu M, Fang P, Li G. A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG. Cogn Neurodyn 2020; 14:591-607. [PMID: 33014175 DOI: 10.1007/s11571-020-09603-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 04/22/2020] [Accepted: 05/22/2020] [Indexed: 01/22/2023] Open
Abstract
Current prostheses are limited in their ability to provide direct sensory feedback to users with missing limb. Several efforts have been made to restore tactile sensation to amputees but the somatotopic tactile feedback often results in unnatural sensations, and it is yet unclear how and what information the somatosensory system receives during voluntary movement. The present study proposes an efficient model of stacked sparse autoencoder and back propagation neural network for detecting sensory events from a highly flexible electrocorticography (ECoG) electrode. During the mechanical stimulation with Von Frey (VF) filament on the plantar surface of rats' foot, simultaneous recordings of tactile afferent signals were obtained from primary somatosensory cortex (S1) in the brain. In order to achieve a model with optimal performance, Particle Swarm Optimization and Adaptive Moment Estimation (Adam) were adopted to select the appropriate number of neurons, hidden layers and learning rate of each sparse auto-encoder. We evaluated the stimulus-evoked sensation by using an automated up-down (UD) method otherwise called UDReader. The assessment of tactile thresholds with VF shows that the right side of the hind-paw was significantly more sensitive at the tibia-(p = 6.50 × 10-4), followed by the saphenous-(p = 7.84 × 10-4), and sural-(p = 8.24 × 10-4). We then validated our proposed model by comparing with the state-of-the-art methods, and recorded accuracy of 98.8%, sensitivity of 96.8%, and specificity of 99.1%. Hence, we demonstrated the effectiveness of our algorithms in detecting sensory events through flexible ECoG recordings which could be a viable option in restoring somatosensory feedback.
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Affiliation(s)
- Oluwagbenga Paul Idowu
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055 China
| | - Jianping Huang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055 China
| | - Yang Zhao
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055 China
| | - Oluwarotimi William Samuel
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055 China
| | - Mei Yu
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055 China
| | - Peng Fang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055 China
| | - Guanglin Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055 China.,Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055 China
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8
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Mora-Sánchez A, Pulini AA, Gaume A, Dreyfus G, Vialatte FB. A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments. Cogn Neurodyn 2020; 14:301-321. [PMID: 32399073 PMCID: PMC7203264 DOI: 10.1007/s11571-020-09573-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 01/31/2020] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
We developed a brain-computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.
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Affiliation(s)
- Aldo Mora-Sánchez
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Alfredo-Aram Pulini
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Antoine Gaume
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Gérard Dreyfus
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - François-Benoît Vialatte
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
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9
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Wang Y, Xu X, Wang R. Energy features in spontaneous up and down oscillations. Cogn Neurodyn 2020; 15:65-75. [PMID: 33786080 DOI: 10.1007/s11571-020-09597-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/25/2020] [Accepted: 05/04/2020] [Indexed: 12/22/2022] Open
Abstract
Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities.
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Affiliation(s)
- Yihong Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Xuying Xu
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.,School of Computer Science, Hangzhou Dianzi University, Hangzhou, China
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10
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Riley SN, Davies J. A spiking neural network model of spatial and visual mental imagery. Cogn Neurodyn 2020; 14:239-251. [PMID: 32226565 PMCID: PMC7090122 DOI: 10.1007/s11571-019-09566-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 09/30/2019] [Accepted: 11/26/2019] [Indexed: 12/18/2022] Open
Abstract
Mental imagery has long been of interest to the cognitive and neurosciences, but how it manifests itself in the mind and brain still remains unresolved. In pursuit of this, we built a spiking neural model that can perform mental rotation and mental map scanning using strategies informed by the psychology and neuroscience literature. Results: When performing mental map scanning, reaction times (RTs) for our model closely match behavioural studies (approx. 50 ms/cm), and replicate the cognitive penetrability of the task. When performing mental rotation, our model's RTs once again closely match behavioural studies (model: 55-65°/s; studies: 60°/s), and performed the task using the same task strategy (whole unit rotation of simple and familiar objects through intermediary points). Overall, our model suggests: (1) vector-based approaches to neuro-cognitive modelling are well equipped to re-produce behavioural findings, and (2) the cognitive (in)penetrability of imagery tasks may depend on whether or not the task makes use of (non)symbolic processing.
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Affiliation(s)
- Sean N. Riley
- Institute of Cognitive Science, Carleton University, 2201 Dunton Tower 1125 Colonel BY Drive, Ottawa, ON K1S 5B6 Canada
| | - Jim Davies
- Institute of Cognitive Science, Carleton University, 2201 Dunton Tower 1125 Colonel BY Drive, Ottawa, ON K1S 5B6 Canada
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11
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Čukić M, Stokić M, Simić S, Pokrajac D. The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. Cogn Neurodyn 2020; 14:443-455. [PMID: 32655709 DOI: 10.1007/s11571-020-09581-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 02/18/2020] [Accepted: 03/06/2020] [Indexed: 01/05/2023] Open
Abstract
Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naïve Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.
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Affiliation(s)
- Milena Čukić
- Department for General Physiology and Biophysics, Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, 11 000 Serbia
- Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, Madrid, Spain
| | - Miodrag Stokić
- Life Activities Advancement Center, Gospodar Jovanova 35, Belgrade, 11 000 Serbia
- Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Slobodan Simić
- Institute for Mental Health, Palmotićeva 37, Belgrade, Serbia
| | - Dragoljub Pokrajac
- Delaware Biotechnology Institute, Delaware State University, 305D Science Center North, 1200 N Dupont Hwy, Dover, DE 19901 USA
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12
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Research on cascading high-dimensional isomorphic chaotic maps. Cogn Neurodyn 2020; 15:157-167. [PMID: 33786086 DOI: 10.1007/s11571-020-09583-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 03/02/2020] [Accepted: 03/10/2020] [Indexed: 10/24/2022] Open
Abstract
In order to overcome the security weakness of the discrete chaotic sequence caused by small Lyapunov exponent and keyspace, a general chaotic construction method by cascading multiple high-dimensional isomorphic maps is presented in this paper. Compared with the original map, the parameter space of the resulting chaotic map is enlarged many times. Moreover, the cascaded system has larger chaotic domain and bigger Lyapunov exponents with proper parameters. In order to evaluate the effectiveness of the presented method, the generalized 3-D Hénon map is utilized as an example to analyze the dynamical behaviors under various cascade modes. Diverse maps are obtained by cascading 3-D Hénon maps with different parameters or different permutations. It is worth noting that some new dynamical behaviors, such as coexisting attractors and hyperchaotic attractors are also discovered in cascaded systems. Finally, an application of image encryption is delivered to demonstrate the excellent performance of the obtained chaotic sequences.
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13
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Tafreshi TF, Daliri MR, Ghodousi M. Functional and effective connectivity based features of EEG signals for object recognition. Cogn Neurodyn 2019; 13:555-566. [PMID: 31741692 DOI: 10.1007/s11571-019-09556-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 09/17/2019] [Accepted: 09/24/2019] [Indexed: 01/06/2023] Open
Abstract
Classifying different object categories is one of the most important aims of brain-computer interface researches. Recently, interactions between brain regions were studied using different methods, such as functional and effective connectivity techniques. Functional and effective connectivity techniques are applied to estimate human brain areas connectivity. The main purpose of this study is to compare classification accuracy of the most advanced functional and effective methods in order to classify 12 basic object categories using Electroencephalography (EEG) signals. In this paper, 19 channels EEG signals were collected from 10 healthy subjects; when they were visiting color images and instructed to select the target images among others. Correlation, magnitude square coherence, wavelet coherence (WC), phase synchronization and mutual information were applied to estimate functional cortical connectivity. On the other hand, directed transfer function, partial directed coherence, generalized partial directed coherence (GPDC) were used to obtain effective cortical connectivity. After feature extraction, the scalar feature selection methods including T-test and one-sided-anova were applied to rank and select the most informative features. The selected features were classified by a one-against-one support vector machine classifier. The results indicated that the use of different techniques led to different classifying accuracy and brain lobes analysis. WC and GPDC are the most accurate methods with performances of 80.15% and 64.43%, respectively.
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Affiliation(s)
| | - Mohammad Reza Daliri
- 2Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mahrad Ghodousi
- 3Department of Neuroscience, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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14
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Çotuk HB, Duru AD, Aktaş Ş. Monitoring Autonomic and Central Nervous System Activity by Permutation Entropy during Short Sojourn in Antarctica. ENTROPY 2019. [PMCID: PMC7515415 DOI: 10.3390/e21090893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The aim of this study was to monitor acute response patterns of autonomic and central nervous system activity during an encounter with Antarctica by synchronously recording heart rate variability (HRV) and electroencephalography (EEG). On three different time-points during the two-week sea journey, the EEG and HRV were recorded from nine male scientists who participated in “The First Turkish Antarctic Research Expedition”. The recordings were performed in a relaxed state with the eyes open, eyes closed, and during a space quantity perception test. For the EEG recordings, the wireless 14 channel EPOC-Emotiv device was used, and for the HRV recordings, a Polar heart rate monitor S810i was used. The HRV data were analyzed by time/frequency domain parameters and ordinal pattern statistics. For the EEG data, spectral band power in the conventional frequency bands, as well as permutation entropy values were calculated. Regarding HRV, neither conventional nor permutation entropy calculations produced significant differences for the different journey time-points, but only permutation entropy was able to differentiate between the testing conditions. During the cognitive test, permutation entropy values increased significantly, whereas the conventional HRV parameters did not show any significant differences. In the EEG analysis, the ordinal pattern statistics revealed significant transitions in the course of the sea voyage as permutation entropy values decreased, whereas spectral band power analysis could not detect any significant difference. Permutation entropy analysis was further able to differentiate between the three testing conditions as well between the brain regions. In the conventional spectral band power analysis, alpha band power could separate the three testing conditions and brain regions, and beta band power could only do so for the brain regions. This superiority of permutation entropy in discerning subtle differences in the autonomic and central nervous system’s responses to an overwhelming subjective experience renders it suitable as an analysis tool for biomonitoring in extreme environments.
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Affiliation(s)
- H. Birol Çotuk
- Department of Sport Health Sciences, Marmara University, 34810 İstanbul, Turkey;
- Correspondence:
| | - Adil Deniz Duru
- Department of Sport Health Sciences, Marmara University, 34810 İstanbul, Turkey;
| | - Şamil Aktaş
- Department of Underwater and Hyperbaric Medicine, İstanbul University, 34093 İstanbul, Turkey;
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