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Lin Y, Huang S, Mao J, Li M, Haihambo N, Wang F, Liang Y, Chen W, Han C. The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device. Neuroimage 2024; 294:120637. [PMID: 38714216 DOI: 10.1016/j.neuroimage.2024.120637] [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: 01/29/2024] [Revised: 03/31/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024] Open
Abstract
In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.
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Affiliation(s)
- Yuchen Lin
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shaojia Huang
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Jidong Mao
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Meijia Li
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Brussels, Belgium
| | - Naem Haihambo
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Brussels, Belgium
| | - Fang Wang
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Yuping Liang
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Wufang Chen
- Shenzhen Shuimu AI Technology Co., Ltd, Shenzhen, China
| | - Chuanliang Han
- School of Biomedical Sciences and Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China.
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Svantesson M, Olausson H, Eklund A, Thordstein M. Get a New Perspective on EEG: Convolutional Neural Network Encoders for Parametric t-SNE. Brain Sci 2023; 13:brainsci13030453. [PMID: 36979263 PMCID: PMC10046040 DOI: 10.3390/brainsci13030453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/03/2023] [Accepted: 03/04/2023] [Indexed: 03/09/2023] Open
Abstract
t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data.
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Affiliation(s)
- Mats Svantesson
- Department of Clinical Neurophysiology, University Hospital of Linköping, 58185 Linköping, Sweden
- Center for Social and Affective Neuroscience, Linköping University, 58183 Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, 58183 Linköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, 58183 Linköping, Sweden
- Correspondence:
| | - Håkan Olausson
- Department of Clinical Neurophysiology, University Hospital of Linköping, 58185 Linköping, Sweden
- Center for Social and Affective Neuroscience, Linköping University, 58183 Linköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, 58183 Linköping, Sweden
| | - Anders Eklund
- Center for Medical Image Science and Visualization, Linköping University, 58183 Linköping, Sweden
- Department of Biomedical Engineering, Linköping University, 58183 Linköping, Sweden
- Division of Statistics & Machine Learning, Department of Computer and Information Science, Linköping University, 58183 Linköping, Sweden
| | - Magnus Thordstein
- Department of Clinical Neurophysiology, University Hospital of Linköping, 58185 Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, 58183 Linköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, 58183 Linköping, Sweden
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Avian C, Prakosa SW, Faisal M, Leu JS. Estimating finger joint angles on surface EMG using Manifold Learning and Long Short-Term Memory with Attention mechanism. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103099] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Onojima T, Kitajo K. A state-informed stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter. J Neural Eng 2021; 18. [PMID: 34644689 DOI: 10.1088/1741-2552/ac2f7b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 10/13/2021] [Indexed: 11/11/2022]
Abstract
Objective.We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for state-informed sensory stimulation in electroencephalography (EEG) experiments.Approach.The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation.Main results.Our method showed higher accuracy in predicting the EEG phase than the conventional autoregressive (AR) model-based method.Significance.A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated AR model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.
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Affiliation(s)
- Takayuki Onojima
- CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako 351-0198, Japan
| | - Keiichi Kitajo
- CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako 351-0198, Japan.,Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8585, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki 444-8585, Japan
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Sase T, Kitajo K. The metastable brain associated with autistic-like traits of typically developing individuals. PLoS Comput Biol 2021; 17:e1008929. [PMID: 33861737 PMCID: PMC8081345 DOI: 10.1371/journal.pcbi.1008929] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/28/2021] [Accepted: 03/31/2021] [Indexed: 12/03/2022] Open
Abstract
Metastability in the brain is thought to be a mechanism involved in the dynamic organization of cognitive and behavioral functions across multiple spatiotemporal scales. However, it is not clear how such organization is realized in underlying neural oscillations in a high-dimensional state space. It was shown that macroscopic oscillations often form phase-phase coupling (PPC) and phase-amplitude coupling (PAC), which result in synchronization and amplitude modulation, respectively, even without external stimuli. These oscillations can also make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalographic signals and the autism-spectrum quotient scores acquired from healthy humans, we show experimental evidence that the PAC combined with PPC allows amplitude modulation to be transient, and that the metastable dynamics with this transient modulation is associated with autistic-like traits. In individuals with a longer attention span, such dynamics tended to show fewer transitions between states by forming delta-alpha PAC. We identified these states as two-dimensional metastable states that could share consistent patterns across individuals. Our findings suggest that the human brain dynamically organizes inter-individual differences in a hierarchy of macroscopic oscillations with multiple timescales by utilizing metastability. The human brain organizes cognitive and behavioral functions dynamically. For decades, the dynamic organization of underlying neural oscillations has been a fundamental topic in neuroscience research. Even without external stimuli, macroscopic oscillations often form phase-phase coupling and phase-amplitude coupling (PAC) that result in synchronization and amplitude modulation, respectively, and can make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalography signals acquired from healthy humans, we show evidence that these two neural couplings enable amplitude modulation to be transient, and that this transient modulation can be viewed as the transition among oscillatory states with different PAC strengths. We also demonstrate that such transition dynamics are associated with the ability to maintain attention to detail and to switch attention, as measured by autism-spectrum quotient scores. These individual dynamics were visualized as a trajectory among states with attracting tendencies, and involved consistent brain states across individuals. Our findings have significant implications for unraveling the variability in the individual brains showing typical and atypical development.
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Affiliation(s)
- Takumi Sase
- Rhythm-based Brain Information Processing Unit, CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
- * E-mail: (TS); (KK)
| | - Keiichi Kitajo
- Rhythm-based Brain Information Processing Unit, CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, Japan
- Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, Japan
- * E-mail: (TS); (KK)
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Kottlarz I, Berg S, Toscano-Tejeida D, Steinmann I, Bähr M, Luther S, Wilke M, Parlitz U, Schlemmer A. Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities. Front Physiol 2021; 11:614565. [PMID: 33597891 PMCID: PMC7882607 DOI: 10.3389/fphys.2020.614565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/16/2020] [Indexed: 11/30/2022] Open
Abstract
In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation.
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Affiliation(s)
- Inga Kottlarz
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Sebastian Berg
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Diana Toscano-Tejeida
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Iris Steinmann
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Mathias Bähr
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Stefan Luther
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Melanie Wilke
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany.,German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Ulrich Parlitz
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Alexander Schlemmer
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
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Affiliation(s)
- Atsushi Nambu
- Division of System Neurophysiology, National Institute for Physiological Sciences, Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Aichi 444-8585, Japan.
| | - Ichiro Tsuda
- Chubu University Academy of Emerging Sciences, Kasugai, Aichi 487-8501, Japan
| | - Tatsuya Mima
- The Graduate School of Core Ethics and Frontier Sciences, Ritsumeikan University, Kyoto, 603-8577, Japan
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Time-Series Prediction of the Oscillatory Phase of EEG Signals Using the Least Mean Square Algorithm-Based AR Model. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10103616] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces.
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