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Abe T, Asai Y, Lintas A, Villa AEP. Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions. Sci Rep 2024; 14:8521. [PMID: 38609457 PMCID: PMC11372163 DOI: 10.1038/s41598-024-59004-8] [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: 09/04/2023] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.
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Affiliation(s)
- Takeshi Abe
- AI Systems Medicine Research and Training Center, Graduate School of Medicine and University Hospital, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Division of Systems Medicine and Informatics, Research Institute of Cell Design Medical Science, Yamaguchi University, Yamaguchi, 755-8505, Japan
| | - Yoshiyuki Asai
- AI Systems Medicine Research and Training Center, Graduate School of Medicine and University Hospital, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Division of Systems Medicine and Informatics, Research Institute of Cell Design Medical Science, Yamaguchi University, Yamaguchi, 755-8505, Japan
| | - Alessandra Lintas
- HEC-LABEX, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland
- Neuroheuristic Research Group & Complexity Sciences Research Group, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland
| | - Alessandro E P Villa
- Neuroheuristic Research Group & Complexity Sciences Research Group, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland.
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Averna A, Coelli S, Ferrara R, Cerutti S, Priori A, Bianchi AM. Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease. J Neural Eng 2023; 20:051001. [PMID: 37746822 DOI: 10.1088/1741-2552/acf8fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 09/12/2023] [Indexed: 09/26/2023]
Abstract
Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.
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Affiliation(s)
- Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Rosanna Ferrara
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Sergio Cerutti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Alberto Priori
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Temporiti F, Calcagno A, Coelli S, Marino G, Gatti R, Bianchi AM, Galli M. Early sleep after action observation and motor imagery training boosts improvements in manual dexterity. Sci Rep 2023; 13:2609. [PMID: 36788349 PMCID: PMC9929332 DOI: 10.1038/s41598-023-29820-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 02/16/2023] Open
Abstract
The systematic observation and imagination of actions promotes acquisition of motor skills. Furthermore, studies demonstrated that early sleep after practice enhances motor learning through an offline stabilization process. Here, we investigated behavioral effects and neurodynamical correlates of early sleep after action observation and motor imagery training (AO + MI-training) on motor learning in terms of manual dexterity. Forty-five healthy participants were randomized into three groups receiving a 3 week intervention consisting of AO + MI-training immediately before sleeping or AO + MI-training at least 12 h before sleeping or a control stimulation. AO + MI-training implied the observation and motor imagery of transitive manual dexterity tasks, whereas the control stimulation consisted of landscape video-clips observation. Manual dexterity was assessed using functional tests, kinematic and neurophysiological outcomes before and after the training and at 1-month follow-up. AO + MI-training improved manual dexterity, but subjects performing AO + MI-training followed by early sleep had significantly larger improvements than those undergoing the same training at least 12 h before sleeping. Behavioral findings were supported by neurodynamical correlates during motor performance and additional sleep-dependent benefits were also detected at 1 month follow-up. These findings introduce a new approach to enhance the acquisition of new motor skills or facilitate recovery in patients with motor impairments.
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Affiliation(s)
- Federico Temporiti
- Physiotherapy Unit, Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, Milan, Italy.
- Department of Electronic, Information and Bioengineering, Politecnico Di Milano, Via Ponzio 34, Milan, Italy.
| | - Alessandra Calcagno
- Department of Electronic, Information and Bioengineering, Politecnico Di Milano, Via Ponzio 34, Milan, Italy
| | - Stefania Coelli
- Department of Electronic, Information and Bioengineering, Politecnico Di Milano, Via Ponzio 34, Milan, Italy
| | - Giorgia Marino
- Physiotherapy Unit, Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, Milan, Italy
| | - Roberto Gatti
- Physiotherapy Unit, Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan, Italy
| | - Anna Maria Bianchi
- Department of Electronic, Information and Bioengineering, Politecnico Di Milano, Via Ponzio 34, Milan, Italy
| | - Manuela Galli
- Department of Electronic, Information and Bioengineering, Politecnico Di Milano, Via Ponzio 34, Milan, Italy
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Awais MA, Yusoff MZ, Khan DM, Yahya N, Kamel N, Ebrahim M. Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network. SENSORS (BASEL, SWITZERLAND) 2021; 21:6570. [PMID: 34640888 PMCID: PMC8512774 DOI: 10.3390/s21196570] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/24/2022]
Abstract
Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
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Affiliation(s)
- Muhammad Ahsan Awais
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Mohd Zuki Yusoff
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Danish M. Khan
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
- Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan
| | - Norashikin Yahya
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Nidal Kamel
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Mansoor Ebrahim
- Faculty of Engineering, Sciences, and Technology, Iqra University, Karachi 75500, Pakistan;
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Accurate assessment of low-function autistic children based on EEG feature fusion. J Clin Neurosci 2021; 90:351-358. [PMID: 34275574 DOI: 10.1016/j.jocn.2021.06.022] [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: 11/13/2020] [Revised: 05/10/2021] [Accepted: 06/14/2021] [Indexed: 11/22/2022]
Abstract
Autism spectrum disorder (ASD) is a very serious neurodevelopmental disorder and diagnosis mainly depends on the clinical scale, which has a certain degree of subjectivity. It is necessary to make accurate evaluation by objective indicators. In this study, we enrolled 96 children aged from 3 to 6 years: 48 low-function autistic children (38 males and 10 females; mean±SD age: 4.9±1.1 years) and 48 typically developing (TD) children (38 males and 10 females; mean±SD age: 4.9 ± 1.2 years) to participate in our experiment. We investigated to fuse multi-features (entropy, relative power, coherence and bicoherence) to distinguish low-function autistic children and TD children accurately. Minimum redundancy maximum correlation algorithm was used to choose the features and support vector machine was used for classification. Ten-fold cross validation was used to test the accuracy of the model. Better classification result was obtained. We tried to provide a reliable basis for clinical evaluation and diagnosis for ASD.
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Fathima S, Kore SK. Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems-A Review. Front Neurosci 2021; 14:546656. [PMID: 33551716 PMCID: PMC7859253 DOI: 10.3389/fnins.2020.546656] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 12/18/2020] [Indexed: 11/13/2022] Open
Abstract
Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.
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Affiliation(s)
- Shireen Fathima
- Department of Electronics and Communication Engineering, HKBK College of Engineering, Bengaluru, India
| | - Sheela Kiran Kore
- Department of Electronics and Communication Engineering, KLE Dr. M. S. Sheshagiri College of Engineering and Technology, Belgaum, India
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