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Mesin L. Nonlinear spatio-temporal filter to reduce crosstalk in bipolar electromyogram. J Neural Eng 2024; 21:016021. [PMID: 38277703 DOI: 10.1088/1741-2552/ad2334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/26/2024] [Indexed: 01/28/2024]
Abstract
Objective.The wide detection volume of surface electromyogram (EMG) makes it prone to crosstalk, i.e. the signal from other muscles than the target one. Removing this perturbation from bipolar recordings is an important open problem for many applications.Approach.An innovative nonlinear spatio-temporal filter is developed to estimate the EMG generated by the target muscle by processing noisy signals from two bipolar channels, placed over the target and the crosstalk muscle, respectively. The filter is trained on some calibration data and then can be applied on new signals. Tests are provided in simulations (considering different thicknesses of the subcutaneous tissue, inter-electrode distances, locations of the EMG channels, force levels) and experiments (from pronator teres and flexor carpi radialis of 8 healthy subjects).Main results.The proposed filter allows to reduce the effect of crosstalk in all investigated conditions, with a statistically significant reduction of its root mean squared of about 20%, both in simulated and experimental data. Its performances are also superior to those of a blind source separation method applied to the same data.Significance.The proposed filter is simple to be applied and feasible in applications in which single bipolar channels are placed over the muscles of interest. It can be useful in many fields, such as in gait analysis, tests of myoelectric fatigue, rehabilitation with EMG biofeedback, clinical studies, prosthesis control.
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Affiliation(s)
- Luca Mesin
- Mathematical Biology and Physiology, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, Italy
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2
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Komeylian S, Paolini C. Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform. Sensors (Basel) 2023; 23:1742. [PMID: 36772781 PMCID: PMC9919919 DOI: 10.3390/s23031742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/23/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than -10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%.
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3
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Yan W, Wu Y, Du C, Xu G. An improved cross-subject spatial filter transfer method for SSVEP-based BCI. J Neural Eng 2022; 19. [PMID: 35850094 DOI: 10.1088/1741-2552/ac81ee] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/18/2022] [Indexed: 11/11/2022]
Abstract
Steady-state visual evoked potential (SSVEP) training feature recognition algorithms utilize user training data to reduce the interference of spontaneous electroencephalogram (EEG) activities on SSVEP response for improved recognition accuracy. The data collection process can be tedious, increasing the mental fatigue of users and also seriously affecting the practicality of SSVEP-based brain-computer interface (BCI) systems. As an alternative, a cross-subject spatial filter transfer (CSSFT) method to transfer an existing user data model with good SSVEP response to new user test data has been proposed. The CSSFT method uses superposition averages of data for multiple blocks of data as transfer data. However, the amplitude and pattern of brain signals are often significantly different across trials. The goal of this study was to improve superposition averaging for the CSSFT method and propose an Ensemble scheme based on ensemble learning, and an Expansion scheme based on matrix expansion. The feature recognition performance was compared for CSSFT and the proposed improved CSSFT method using two public datasets. The results demonstrated that the improved CSSFT method can significantly improve the recognition accuracy and information transmission rate of existing methods. This strategy avoids a tedious data collection process, and promotes the potential practical application of BCI systems.
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Affiliation(s)
- Wenqiang Yan
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, XIANNING WEST ROAD, XI'AN, SHAANXI, 710049, CHINA
| | - Yongcheng Wu
- Xi'an Jiaotong University, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Chenghang Du
- Xi'an Jiaotong University, XIANNING WEST ROAD, XI'AN, Shaanxi, 710049, CHINA
| | - Guanghua Xu
- Xi'an Jiaotong University, XIANNING WEST ROAD, Xi'an, 710049, CHINA
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4
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Yan W, Wu Y, Du C, Xu G. Cross-subject spatial filter transfer method for SSVEP-EEG feature recognition. J Neural Eng 2022; 19. [PMID: 35483331 DOI: 10.1088/1741-2552/ac6b57] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 04/27/2022] [Indexed: 11/12/2022]
Abstract
Objective.Steady-state visual evoked potential (SSVEP) is an important control method of the brain-computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system.Approach.A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user model with good SSVEP response to the new user test data without collecting any training data from the new user is proposed.Main results.Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank CCA. Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities.Significance.The proposed method requires no tedious data calibration process for new users, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.
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Affiliation(s)
- Wenqiang Yan
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Yongcheng Wu
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Chenghang Du
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Guanghua Xu
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China.,State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
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5
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Trumpis M, Chiang CH, Orsborn AL, Bent B, Li J, Rogers JA, Pesaran B, Cogan G, Viventi J. Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG. J Neural Eng 2021; 18. [PMID: 33326943 DOI: 10.1088/1741-2552/abd460] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 12/16/2020] [Indexed: 12/22/2022]
Abstract
Objective. Large channel count surface-based electrophysiology arrays (e.g. µECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode spacing based on the expected accuracy of predicting unsampled field potential from sampled sites.Approach. We applied spatial covariance modeling and field prediction techniques based on geospatial kriging to quantify sufficient sampling for thousands of 500 ms µECoG snapshots in human, monkey, and rat. We calculated a probably approximately correct (PAC) spacing based on kriging that would be required to predict µECoG fields at≤10% error for most cases (95% of observations).Main results. Kriging theory accurately explained the competing effects of electrode density and noise on predicting field potential. Across five frequency bands from 4-7 to 75-300 Hz, PAC spacing was sub-millimeter for auditory cortex in anesthetized and awake rats, and posterior superior temporal gyrus in anesthetized human. At 75-300 Hz, sub-millimeter PAC spacing was required in all species and cortical areas.Significance. PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that µECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.
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Affiliation(s)
- Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Amy L Orsborn
- Center for Neural Science, New York University, New York, NY 10003, United States of America.,Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, United States of America.,Department of Bioengineering, University of Washington, Seattle, Washington 98105, United States of America.,Washington National Primate Research Center, Seattle, Washington 98195, United States of America
| | - Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Jinghua Li
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, United States of America.,Chronic Brain Injury Program, The Ohio State University, Columbus, OH 43210, United States of America
| | - John A Rogers
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Simpson Querrey Institute, Northwestern University, Chicago, IL 60611, United States of America.,Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States of America
| | - Bijan Pesaran
- Center for Neural Science, New York University, New York, NY 10003, United States of America
| | - Gregory Cogan
- Department of Neurosurgery, Duke School of Medicine, Durham, NC 27710, United States of America.,Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States of America.,Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States of America.,Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC 27710, United States of America
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America.,Department of Neurosurgery, Duke School of Medicine, Durham, NC 27710, United States of America.,Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC 27710, United States of America.,Department of Neurobiology, Duke School of Medicine, Durham, NC 27710, United States of America
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6
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Miyakoshi M, Schmitt LM, Erickson CA, Sweeney JA, Pedapati EV. Can We Push the "Quasi-Perfect Artifact Rejection" Even Closer to Perfection? Front Neuroinform 2021; 14:597079. [PMID: 33584237 PMCID: PMC7873913 DOI: 10.3389/fninf.2020.597079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 12/16/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, United States
| | - Lauren M Schmitt
- Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Craig A Erickson
- Divisions of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Ernest V Pedapati
- Divisions of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Divisions of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
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7
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Noah JA, Zhang X, Dravida S, DiCocco C, Suzuki T, Aslin RN, Tachtsidis I, Hirsch J. Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals. Neurophotonics 2021; 8:015004. [PMID: 33598505 PMCID: PMC7881368 DOI: 10.1117/1.nph.8.1.015004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 01/19/2021] [Indexed: 05/03/2023]
Abstract
Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.
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Affiliation(s)
- J. Adam Noah
- Yale School of Medicine, Department of Psychiatry, Brain Function Laboratory, New Haven, Connecticut, United States
| | - Xian Zhang
- Yale School of Medicine, Department of Psychiatry, Brain Function Laboratory, New Haven, Connecticut, United States
| | - Swethasri Dravida
- Yale School of Medicine, Interdepartmental Neuroscience Program New Haven, Connecticut, United States
| | - Courtney DiCocco
- Yale School of Medicine, Brain Function Laboratory, New Haven, Connecticut, United States
| | - Tatsuya Suzuki
- Meiji University, Graduate School of Science and Technology, Electrical Engineering Program, Kawasaki, Japan
- Meiji University, School of Science and Technology, Department of Electronics and Bioinformatics, Kawasaki, Japan
| | - Richard N. Aslin
- Haskins Laboratories, New Haven, Connecticut, United States
- Yale University, Department of Psychology, New Haven, Connecticut, United States
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Brain Function Laboratory, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- Yale School of Medicine, Department of Neuroscience, New Haven, Connecticut, United States
- Yale School of Medicine, Department of Comparative Medicine, New Haven, Connecticut, United States
- Address all correspondence to Joy Hirsch,
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8
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Yoon I, Oh S, Oh J. Affordable Thin Lens Using Single Polarized Disparate Filter Arrays for Beyond 5G toward 6G. Sensors (Basel) 2019; 19:s19183982. [PMID: 31540121 PMCID: PMC6766796 DOI: 10.3390/s19183982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 11/16/2022]
Abstract
This paper proposes a novel design approach for a thin lens with the aim of overcoming fineness limits in the commercial millimeter wave printed circuit board (PCB) manufacturing process. The PCB manufacturing process typically does not allow the fabrication of metallic patterns with a gap and width of less than 100 μm. This hampers expanding thin lens technology to 5G commercial applications, especially when such technology is considered for 60 GHz or higher frequency, which requires a finer gap and width of metallic traces. This paper proposes that problematic process conditions can be mitigated when a lens is designed by establishing single-polarized lumped element models where larger capacitance and inductance values can be obtained for the same patch and grid unit cells. While the proposed design technique is more advantageous at higher target frequencies, a 60 GHz application and a wireless backhaul system is selected because of a limited range of frequencies that can be measured by an available vector network analyzer. The required gap or width of metallic traces can be widened significantly by using the proposed single-polarized unit cells to acquire the same in-plane capacitance or inductance. This enables the lens operating at higher-frequency under the process limits in fabricable fine traces. Finally, the effectiveness of the simulated design procedure is demonstrated by fabricating a 60 GHz thin lens that can achieve a gain enhancement of 16 dB for a 4 × 4 patch antenna array with a gain of 16.5 dBi.
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Affiliation(s)
- Inseop Yoon
- Department of Electronic Engineering, Inha University, Incheon 22212, Korea.
| | - Seongwoog Oh
- Institute of New Media and Communications and School of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.
| | - Jungsuek Oh
- Institute of New Media and Communications and School of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.
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Cao R, Yang K, Yang Q, Chen P, Sun Q, Xue R. Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean. Sensors (Basel) 2019; 19:s19173810. [PMID: 31484441 PMCID: PMC6749268 DOI: 10.3390/s19173810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 08/29/2019] [Accepted: 08/31/2019] [Indexed: 12/01/2022]
Abstract
Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.
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Affiliation(s)
- Ran Cao
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Kunde Yang
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China.
| | - Qiulong Yang
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Peng Chen
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
| | - Quan Sun
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Runze Xue
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China
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Delisle-Rodriguez D, Villa-Parra AC, Bastos-Filho T, López-Delis A, Frizera-Neto A, Krishnan S, Rocon E. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing. Sensors (Basel) 2017; 17:s17122725. [PMID: 29186848 PMCID: PMC5751387 DOI: 10.3390/s17122725] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 11/13/2017] [Accepted: 11/19/2017] [Indexed: 12/20/2022]
Abstract
This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p<0.01) improved for most of the subjects (ACC≥74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.
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Affiliation(s)
- Denis Delisle-Rodriguez
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil.
- Center of Medical Biophysics, University of Oriente, 90500 Santiago de Cuba, Cuba.
| | - Ana Cecilia Villa-Parra
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil.
- Biomedical Engineering Research Group GIIB, Universidad Politécnica Salesiana, 010105 Cuenca, Ecuador.
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil.
| | - Alberto López-Delis
- Center of Medical Biophysics, University of Oriente, 90500 Santiago de Cuba, Cuba.
| | - Anselmo Frizera-Neto
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil.
| | - Sridhar Krishnan
- Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada.
| | - Eduardo Rocon
- Centre for Automation and Robotics, CSIC-UPM, 28500 Madrid, Spain.
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11
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Reichert C, Dürschmid S, Heinze HJ, Hinrichs H. A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI. Front Neurosci 2017; 11:575. [PMID: 29085279 PMCID: PMC5650628 DOI: 10.3389/fnins.2017.00575] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 10/02/2017] [Indexed: 11/25/2022] Open
Abstract
In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.
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Affiliation(s)
- Christoph Reichert
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Stefan Dürschmid
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Hermann Hinrichs
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Center for Behavioral Brain Sciences, Magdeburg, Germany
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12
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Zhang X, Noah JA, Dravida S, Hirsch J. Signal processing of functional NIRS data acquired during overt speaking. Neurophotonics 2017; 4:041409. [PMID: 28924564 PMCID: PMC5592780 DOI: 10.1117/1.nph.4.4.041409] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 07/24/2017] [Indexed: 05/15/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) offers an advantage over traditional functional imaging methods [such as functional magnetic resonance imaging (fMRI)] by allowing participants to move and speak relatively freely. However, neuroimaging while actively speaking has proven to be particularly challenging due to the systemic artifacts that tend to be located in the critical brain areas. To overcome these limitations and enhance the utility of fNIRS, we describe methods for investigating cortical activity during spoken language tasks through refinement of deoxyhemoglobin (deoxyHb) signals with principal component analysis (PCA) spatial filtering to remove global components. We studied overt picture naming and compared oxyhemoglobin (oxyHb) and deoxyHb signals with and without global component removal using general linear model approaches. Activity in Broca's region and supplementary motor cortex was observed only when the filter was applied to the deoxyHb signal and was shown to be spatially comparable to fMRI data acquired using a similar task and to meta-analysis data. oxyHb signals did not yield expected activity in Broca's region with or without global component removal. This study demonstrates the utility of a PCA spatial filter on the deoxyHb signal in revealing neural activity related to a spoken language task and extends applications of fNIRS to natural and ecologically valid conditions.
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Affiliation(s)
- Xian Zhang
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut, United States
| | - Jack Adam Noah
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut, United States
| | - Swethasri Dravida
- Yale School of Medicine, Interdepartmental Neuroscience Program, New Haven, Connecticut, United States
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut, United States
- Yale School of Medicine, Department of Neuroscience, New Haven, Connecticut, United States
- Yale School of Medicine, Department of Comparative Medicine, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- Address all Correspondence to Joy Hirsch, E-mail:
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Cox R, Schapiro AC, Manoach DS, Stickgold R. Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles. Front Hum Neurosci 2017; 11:433. [PMID: 28928647 PMCID: PMC5591792 DOI: 10.3389/fnhum.2017.00433] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 08/15/2017] [Indexed: 11/25/2022] Open
Abstract
Sleep spindles are transient oscillatory waveforms that occur during non-rapid eye movement (NREM) sleep across widespread cortical areas. In humans, spindles can be classified as either slow or fast, but large individual differences in spindle frequency as well as methodological difficulties have hindered progress towards understanding their function. Using two nights of high-density electroencephalography recordings from 28 healthy individuals, we first characterize the individual variability of NREM spectra and demonstrate the difficulty of determining subject-specific spindle frequencies. We then introduce a novel spatial filtering approach that can reliably separate subject-specific spindle activity into slow and fast components that are stable across nights and across N2 and N3 sleep. We then proceed to provide detailed analyses of the topographical expression of individualized slow and fast spindle activity. Group-level analyses conform to known spatial properties of spindles, but also uncover novel differences between sleep stages and spindle classes. Moreover, subject-specific examinations reveal that individual topographies show considerable variability that is stable across nights. Finally, we demonstrate that topographical maps depend nontrivially on the spindle metric employed. In sum, our findings indicate that group-level approaches mask substantial individual variability of spindle dynamics, in both the spectral and spatial domains. We suggest that leveraging, rather than ignoring, such differences may prove useful to further our understanding of the physiology and functional role of sleep spindles.
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Affiliation(s)
- Roy Cox
- Department of Psychiatry, Beth Israel Deaconess Medical CenterBoston, MA, United States.,Department of Psychiatry, Harvard Medical SchoolBoston, MA, United States
| | - Anna C Schapiro
- Department of Psychiatry, Beth Israel Deaconess Medical CenterBoston, MA, United States.,Department of Psychiatry, Harvard Medical SchoolBoston, MA, United States
| | - Dara S Manoach
- Department of Psychiatry, Harvard Medical SchoolBoston, MA, United States.,Department of Psychiatry, Massachusetts General HospitalCharlestown, MA, United States.,Athinoula A. Martinos Center for Biomedical ImagingCharlestown, MA, United States
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical CenterBoston, MA, United States.,Department of Psychiatry, Harvard Medical SchoolBoston, MA, United States
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Eo T, Kim T, Jun Y, Lee H, Ahn SS, Kim DH, Hwang D. High-SNR multiple T 2 (*)-contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics. J Magn Reson Imaging 2016; 45:1835-1845. [PMID: 27635526 DOI: 10.1002/jmri.25477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Accepted: 08/30/2016] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To develop an effective method that can suppress noise in successive multiecho T2 (*)-weighted magnetic resonance (MR) brain images while preventing filtering artifacts. MATERIALS AND METHODS For the simulation experiments, we used multiple T2 -weighted images of an anatomical brain phantom. For in vivo experiments, successive multiecho MR brain images were acquired from five healthy subjects using a multiecho gradient-recalled-echo (MGRE) sequence with a 3T MRI system. Our denoising method is a nonlinear filter whose filtering weights are determined by tissue characteristics among pixels. The similarity of the tissue characteristics is measured based on the l2 -difference between two temporal decay signals. Both numerical and subjective evaluations were performed in order to compare the effectiveness of our denoising method with those of conventional filters, including Gaussian low-pass filter (LPF), anisotropic diffusion filter (ADF), and bilateral filter. Root-mean-square error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were used in the numerical evaluation. Five observers, including one radiologist, assessed the image quality and rated subjective scores in the subjective evaluation. RESULTS Our denoising method significantly improves RMSE, SNR, and CNR of numerical phantom images, and CNR of in vivo brain images in comparison with conventional filters (P < 0.005). It also receives the highest scores for structure conspicuity (8.2 to 9.4 out of 10) and naturalness (9.2 to 9.8 out of 10) among the conventional filters in the subjective evaluation. CONCLUSION This study demonstrates that high-SNR multiple T2 (*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2 J. MAGN. RESON. IMAGING 2017;45:1835-1845.
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Affiliation(s)
- Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Taeseong Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Yohan Jun
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Hongpyo Lee
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea
| | - Dong-Hyun Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
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15
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Lo CC, Chien TY, Chen YC, Tsai SH, Fang WC, Lin BS. A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection. Sensors (Basel) 2016; 16:213. [PMID: 26861347 PMCID: PMC4801589 DOI: 10.3390/s16020213] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 02/02/2016] [Indexed: 11/16/2022]
Abstract
Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.
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Affiliation(s)
- Chi-Chun Lo
- Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan.
- Department of Engineering and Maintenance, Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan.
| | - Tsung-Yi Chien
- Department of Engineering and Maintenance, Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan.
| | - Yu-Chun Chen
- Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Tainan 711, Taiwan.
| | - Shang-Ho Tsai
- Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan.
| | - Wai-Chi Fang
- Department of Electronics Egineering, National Chiao Tung University, Hsinchu 300, Taiwan.
| | - Bor-Shyh Lin
- Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Tainan 711, Taiwan.
- Department of Medical Research, Chi-Mei Medical Center, Tainan 710, Taiwan.
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Zhang X, Noah JA, Hirsch J. Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering. Neurophotonics 2016; 3:015004. [PMID: 26866047 PMCID: PMC4742567 DOI: 10.1117/1.nph.3.1.015004] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 01/12/2016] [Indexed: 05/05/2023]
Abstract
Global systemic effects not specific to a task can be prominent in functional near-infrared spectroscopy (fNIRS) signals and the separation of task-specific fNIRS signals and global nonspecific effects is challenging due to waveform correlations. We describe a principal component spatial filter algorithm for separation of the global and local effects. The effectiveness of the approach is demonstrated using fNIRS signals acquired during a right finger-thumb tapping task where the response patterns are well established. Both the temporal waveforms and the spatial pattern consistencies between oxyhemoglobin and deoxyhemoglobin signals are significantly improved, consistent with the basic physiological basis of fNIRS signals and the expected pattern of activity associated with the task.
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Affiliation(s)
- Xian Zhang
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut 06511, United States
- Address all correspondence to: Xian Zhang, E-mail:
| | - Jack Adam Noah
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut 06511, United States
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut 06511, United States
- Yale School of Medicine, Department of Neuroscience, New Haven, Connecticut 06511, United States
- Yale School of Medicine, Department of Comparative Medicine, New Haven, Connecticut 06511, United States
- University College London, Department of Medical Physics and Biomedical Engineering, Gower Street, London WC1E 6BT, United Kingdom
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Piitulainen H, Botter A, Bourguignon M, Jousmäki V, Hari R. Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography. J Neurophysiol 2015; 114:2843-53. [PMID: 26354317 DOI: 10.1152/jn.00574.2015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 09/04/2015] [Indexed: 11/22/2022] Open
Abstract
Cortex-muscle coherence (CMC) reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric contraction but absent, for unknown reasons, in some individuals. We used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode grid (36 mm × 12 mm; 60 electrodes separated by 3 mm) to study effects of sEMG recording site, electrode derivation, and rectification on the strength of CMC. Monopolar sEMG from right thenar and 306-channel whole-scalp MEG were recorded from 14 subjects during 4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two derivations were computed to mimic "macroscopic" monopolar and bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21 mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91-95% of the HD-sEMG channels, with maximum coherence at ∼25 Hz. CMC was about a fifth stronger for monopolar than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in tightest cortical source clusters in the left rolandic hand area. CMC was 19-27% stronger for HD-sEMG than for "macroscopic" monopolar or bipolar derivations. EMG rectification reduced the CMC peak by a quarter, resulted in a more uniformly distributed CMC across the thenar, and provided more tightly clustered cortical sources than unrectifed sEMGs. Moreover, it revealed CMC at ∼12 Hz. We conclude that HD-sEMG, especially with monopolar derivation, can facilitate detection of CMC and that individual muscle anatomy cannot explain the high interindividual CMC variability.
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Affiliation(s)
- Harri Piitulainen
- Brain Research Unit, Department of Neuroscience and Biomedical Engineering, and MEG Core and Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, Aalto University School of Science, Aalto, Espoo, Finland; and
| | - Alberto Botter
- Laboratory of Engineering of Neuromuscular System and Motor Rehabilitation, Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy
| | - Mathieu Bourguignon
- Brain Research Unit, Department of Neuroscience and Biomedical Engineering, and MEG Core and Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, Aalto University School of Science, Aalto, Espoo, Finland; and
| | - Veikko Jousmäki
- Brain Research Unit, Department of Neuroscience and Biomedical Engineering, and MEG Core and Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, Aalto University School of Science, Aalto, Espoo, Finland; and
| | - Riitta Hari
- Brain Research Unit, Department of Neuroscience and Biomedical Engineering, and MEG Core and Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, Aalto University School of Science, Aalto, Espoo, Finland; and
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Hauk O, Stenroos M. A framework for the design of flexible cross-talk functions for spatial filtering of EEG/MEG data: DeFleCT. Hum Brain Mapp 2013; 35:1642-53. [PMID: 23616402 DOI: 10.1002/hbm.22279] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 01/23/2013] [Accepted: 01/30/2013] [Indexed: 11/06/2022] Open
Abstract
Brain activation estimated from EEG and MEG data is the basis for a number of time-series analyses. In these applications, it is essential to minimize "leakage" or "cross-talk" of the estimates among brain areas. Here, we present a novel framework that allows the design of flexible cross-talk functions (DeFleCT), combining three types of constraints: (1) full separation of multiple discrete brain sources, (2) minimization of contributions from other (distributed) brain sources, and (3) minimization of the contribution from measurement noise. Our framework allows the design of novel estimators by combining knowledge about discrete sources with constraints on distributed source activity and knowledge about noise covariance. These estimators will be useful in situations where assumptions about sources of interest need to be combined with uncertain information about additional sources that may contaminate the signal (e.g. distributed sources), and for which existing methods may not yield optimal solutions. We also show how existing estimators, such as maximum-likelihood dipole estimation, L2 minimum-norm estimation, and linearly-constrained minimum variance as well as null-beamformers, can be derived as special cases from this general formalism. The performance of the resulting estimators is demonstrated for the estimation of discrete sources and regions-of-interest in simulations of combined EEG/MEG data. Our framework will be useful for EEG/MEG studies applying time-series analysis in source space as well as for the evaluation and comparison of linear estimators.
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Affiliation(s)
- Olaf Hauk
- MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom
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Abstract
We propose a method of correction for multiple comparisons in MEG beamformer based Statistical Parametric Maps (SPMs). We introduce a modification to the minimum-variance beamformer, in which beamformer weights and SPMs of source-power change are computed in distinct steps. This approach allows the calculation of image smoothness based on the computed weights alone. In the first instance we estimate image smoothness by looking at local spatial correlations in residual images generated using random data; we then go on to show how the smoothness of the SPM can be obtained analytically by measuring the correlations between the adjacent weight vectors. In simulations we show that the smoothness of the SPM is highly inhomogeneous and depends on the source strength. We show that, for the minimum variance beamformer, knowledge of image smoothness is sufficient to allow for correction of the multiple comparison problem. Per-voxel threshold estimates, based on the voxels extent (or cluster size) in flattened space, provide accurate corrected false positive error rates for these highly inhomogeneously smooth images.
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Affiliation(s)
- Gareth R Barnes
- The Wellcome Trust Laboratory for MEG Studies, Neurosciences Research Institute, Aston University, Birmingham B4 7ET, United Kingdom.
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