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Holmes MD, Feng R, Wise MV, Ma C, Ramon C, Wu J, Luu P, Hou J, Pan L, Tucker DM. Safety of slow-pulsed transcranial electrical stimulation in acute spike suppression. Ann Clin Transl Neurol 2019; 6:2579-2585. [PMID: 31709777 PMCID: PMC6917336 DOI: 10.1002/acn3.50934] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/07/2019] [Accepted: 10/09/2019] [Indexed: 11/12/2022] Open
Abstract
We examined the effects of slow‐pulsed transcranial electrical stimulation (TES) in suppressing epileptiform discharges in seven adults with refractory epilepsy. An MRI‐based realistic head model was constructed for each subject and co‐registered with 256‐channel dense EEG (dEEG). Interictal spikes were localized, and TES targeted the cortical source of each subject's principal spike population. Targeted spikes were suppressed in five subject's (29/35 treatment days overall), and nontargeted spikes were suppressed in four subjects. Epileptiform activity did not worsen. This study suggests that this protocol, designed to induce long‐term depression (LTD), is safe and effective in acute suppression of interictal epileptiform discharges.
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Affiliation(s)
- Mark D Holmes
- Regional Epilepsy Center, Department of Neurology, University of Washington, Seattle, Washington
| | - Rui Feng
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Mackenzie V Wise
- Regional Epilepsy Center, Department of Neurology, University of Washington, Seattle, Washington
| | - Chengxin Ma
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Ceon Ramon
- Regional Epilepsy Center, Department of Neurology, University of Washington, Seattle, Washington.,Department of Electrical Engineering, University of Washington, Seattle, Washington
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Phan Luu
- Brain Electrophysiology Laboratory Company, Eugene, Oregon
| | | | - Li Pan
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Don M Tucker
- Brain Electrophysiology Laboratory Company, Eugene, Oregon
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High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease. ELECTRONICS 2019. [DOI: 10.3390/electronics8091031] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network.
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Dhindsa K, Acai A, Wagner N, Bosynak D, Kelly S, Bhandari M, Petrisor B, Sonnadara RR. Individualized pattern recognition for detecting mind wandering from EEG during live lectures. PLoS One 2019; 14:e0222276. [PMID: 31513622 PMCID: PMC6742406 DOI: 10.1371/journal.pone.0222276] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 08/26/2019] [Indexed: 01/10/2023] Open
Abstract
Neural correlates of mind wandering The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis. Mind wandering detection To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80–83%. Conclusions Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.
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Affiliation(s)
- Kiret Dhindsa
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- Research and High-Performance Computing Support, McMaster University, Hamilton, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Anita Acai
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Natalie Wagner
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Dan Bosynak
- Research and High-Performance Computing Support, McMaster University, Hamilton, Ontario, Canada
- LIVELab, McMaster University, Hamilton, Ontario, Canada
| | - Stephen Kelly
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Mohit Bhandari
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Brad Petrisor
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Ranil R. Sonnadara
- Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- Research and High-Performance Computing Support, McMaster University, Hamilton, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, Ontario, Canada
- LIVELab, McMaster University, Hamilton, Ontario, Canada
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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Montani V, Chanoine V, Grainger J, Ziegler JC. Frequency-tagged visual evoked responses track syllable effects in visual word recognition. Cortex 2019; 121:60-77. [PMID: 31550616 DOI: 10.1016/j.cortex.2019.08.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/11/2019] [Accepted: 08/11/2019] [Indexed: 01/05/2023]
Abstract
The processing of syllables in visual word recognition was investigated using a novel paradigm based on steady-state visual evoked potentials (SSVEPs). French words were presented to proficient readers in a delayed naming task. Words were split into two segments, the first of which was flickered at 18.75 Hz and the second at 25 Hz. The first segment either matched (congruent condition) or did not match (incongruent condition) the first syllable. The SSVEP responses in the congruent condition showed increased power compared to the responses in the incongruent condition, providing new evidence that syllables are important sublexical units in visual word recognition and reading aloud. With respect to the neural correlates of the effect, syllables elicited an early activation of a right hemisphere network. This network is typically associated with the programming of complex motor sequences, cognitive control and timing. Subsequently, responses were obtained in left hemisphere areas related to phonological processing.
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Affiliation(s)
- Veronica Montani
- Aix-Marseille University and CNRS, Brain and Language Research Institute, Marseille Cedex 3, France.
| | - Valérie Chanoine
- Aix-Marseille University, Institute of Language, Communication and the Brain, Brain and Language Research Institute, Aix-en-Provence, France
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Van Eyndhoven S, Hunyadi B, Dupont P, Van Paesschen W, Van Huffel S. Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone With EEG-Correlated fMRI. Front Neurol 2019; 10:805. [PMID: 31428036 PMCID: PMC6688528 DOI: 10.3389/fneur.2019.00805] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/11/2019] [Indexed: 11/13/2022] Open
Abstract
Objective: To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis. Methods: Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity and noisy influences. Subsequently, we use EEG-correlated fMRI to find the brain regions in which the BOLD signal fluctuation corresponds to the filtered signals' time-varying power (after convolving with the hemodynamic response function), and validate the identified regions by quantitatively comparing them to ground-truth maps of the (resected or hypothesized) ictal onset zone. We validate the performance of this novel predictor vs. that of commonly used unitary or power-weighted predictors and a recently introduced connectivity-based metric, on a cohort of 12 patients with refractory epilepsy. Results: The novel predictor, derived from the filtered EEG signals, allowed the detection of the ictal onset zone in a larger percentage of epileptic patients (92% vs. at most 83% for the other predictors), and with higher statistical significance, compared to existing predictors. At the same time, the new method maintains maximal specificity by not producing false positive activations in healthy controls. Significance: The findings of this study advocate for the use of the MWF to maximize the signal-to-noise ratio of IED-like events in the interictal EEG, and subsequently use time-varying power as a sensitive predictor of the BOLD signal, to localize the ictal onset zone.
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Affiliation(s)
- Simon Van Eyndhoven
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
| | | | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute, Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium.,Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
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He B, Astolfi L, Valdés-Sosa PA, Marinazzo D, Palva SO, Bénar CG, Michel CM, Koenig T. Electrophysiological Brain Connectivity: Theory and Implementation. IEEE Trans Biomed Eng 2019; 66:10.1109/TBME.2019.2913928. [PMID: 31071012 PMCID: PMC6834897 DOI: 10.1109/tbme.2019.2913928] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, University of Rome Sapienza, and with IRCCS Fondazione Santa Lucia, Rome, Italy
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Technical Description of Long-Term High-Density EEG Monitoring Using 128-Channel Cap Applied With a Conductive Paste. J Clin Neurophysiol 2019; 36:175-180. [PMID: 30694941 DOI: 10.1097/wnp.0000000000000557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE EEG is a common diagnostic tool to localize epileptic activity with excellent temporal resolution and, however, with relatively low spatial resolution. High-density EEG recording is limited in clinical practice, mainly because of electrode placement difficulties, need of high technical skills, and advanced equipment requirement. METHODS We described the technique of long-term EEG recording using a 128-channel neoprene cap placed with a dielectric paste in 7 patients with refractory epilepsy. We captured electrographic seizures in six of seven patients. The 128-channel EEG cap was well tolerated except for a mild headache. Daily impedance checks and reapplication of the high impedance leads maintained the recording with impedances below 10 kΩ. RESULTS Successful long-term recording of high-density EEG was able to capture seizures in six of seven patients. The time needed to apply the electrodes was approximately 1 hour and approximately 30 minutes daily for maintenance. The EEG source localization was obtained in six of seven patients, concordant within the sublobar region for both standard and high-density EEG recordings. Three patients reported a mild headache not leading to discontinuation of the recording. CONCLUSIONS In general, long-term high-density scalp EEG recording with a dielectric paste is well tolerated and allows capturing both interictal and ictal data for localization. This small sample does not show a significant advantage in terms of sublobar localization when high-density EEG source is compared with standard 10 to 20 placement as long as the subtemporal areas are recorded.
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58
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Li MA, Wang YF, Jia SM, Sun YJ, Yang JF. Decoding of motor imagery EEG based on brain source estimation. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.02.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Vivas AB, Paraskevopoulos E, Castillo A, Fuentes LJ. Neurophysiological Activations of Predictive and Non-predictive Exogenous Cues: A Cue-Elicited EEG Study on the Generation of Inhibition of Return. Front Psychol 2019; 10:227. [PMID: 30800091 PMCID: PMC6376955 DOI: 10.3389/fpsyg.2019.00227] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 01/23/2019] [Indexed: 11/13/2022] Open
Abstract
In cueing tasks, predictive and non-predictive exogenous spatial cues produce distinct patterns of behavioral effects. Although both cues initially attract attention, only non-predictive cues lead to inhibitory effects (worse performance at the cued location as compared to the uncued location) if the time elapsed between the cue and the target is long enough. However, the process/processes leading to the later inhibitory effect, named inhibition of return (IOR), are still under debate. In the present study, we used cue-elicited EEG activations from predictive and non-predictive exogenous spatial cues to further investigate the neural processes involved in IOR. Unlike previous similar studies, we intermixed both types of cues in a block of trials, in an attempt to identify the unique neurophysiological activations associated with the generation of IOR. We found that predictive and non-predictive cues significantly differed in activation just at 400-470 ms post-cue window. Activation was greater for non-predictive cues in the intraparietal sulcus (IPS), and this activation correlated significantly with IOR effects. These findings support the hypothesis that the posterior parietal cortex plays a crucial role in the generation of IOR.
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Affiliation(s)
- Ana B Vivas
- Psychology Department, The University of Sheffield International Faculty, City College, Thessaloniki, Greece
| | - Evangelos Paraskevopoulos
- Psychology Department, The University of Sheffield International Faculty, City College, Thessaloniki, Greece.,Medical Physics Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Alejandro Castillo
- Department of Basic Psychology and Methodology, University of Murcia, Murcia, Spain
| | - Luis J Fuentes
- Department of Basic Psychology and Methodology, University of Murcia, Murcia, Spain
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Mouthaan BE, Rados M, Boon P, Carrette E, Diehl B, Jung J, Kimiskidis V, Kobulashvili T, Kuchukhidze G, Larsson PG, Leitinger M, Ryvlin P, Rugg-Gunn F, Seeck M, Vulliémoz S, Huiskamp G, Leijten FSS, Van Eijsden P, Trinka E, Braun KPJ. Diagnostic accuracy of interictal source imaging in presurgical epilepsy evaluation: A systematic review from the E-PILEPSY consortium. Clin Neurophysiol 2019; 130:845-855. [PMID: 30824202 DOI: 10.1016/j.clinph.2018.12.016] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2018] [Revised: 11/16/2018] [Accepted: 12/20/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Interictal high resolution (HR-) electric source imaging (ESI) and magnetic source imaging (MSI) are non-invasive tools to aid epileptogenic zone localization in epilepsy surgery candidates. We carried out a systematic review on the diagnostic accuracy and quality of evidence of these modalities. METHODS Embase, Pubmed and the Cochrane database were searched on 13 February 2017. Diagnostic accuracy studies taking post-surgical seizure outcome as reference standard were selected. Quality appraisal was based on the QUADAS-2 framework. RESULTS Eleven studies were included: eight MSI (n = 267), three HR-ESI (n = 127) studies. None was free from bias. This mostly involved: selection of operated patients only, interference of source imaging with surgical decision, and exclusion of indeterminate results. Summary sensitivity and specificity estimates were 82% (95% CI: 75-88%) and 53% (95% CI: 37-68%) for overall source imaging, with no statistical difference between MSI and HR-ESI. Specificity is higher when partially concordant results were included as non-concordant (p < 0.05). Inclusion of indeterminate test results as non-concordant lowered sensitivity (p < 0.05). CONCLUSIONS Source imaging has a relatively high sensitivity but low specificity for identification of the epileptogenic zone. SIGNIFICANCE We need higher quality studies allowing unbiased test evaluation to determine the added value and diagnostic accuracy of source imaging in the presurgical workup of refractory focal epilepsy.
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Affiliation(s)
- Brian E Mouthaan
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Matea Rados
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Paul Boon
- Reference Center for Refractory Epilepsy, Department of Neurology, Ghent University Hospital, Belgium
| | - Evelien Carrette
- Reference Center for Refractory Epilepsy, Department of Neurology, Ghent University Hospital, Belgium
| | - Beate Diehl
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, United Kingdom; Department of Clinical and Experimental Epilepsy, University College, London, UK
| | - Julien Jung
- Department of Functional Neurology and Epileptology, Institute of Epilepsies (IDEE), Hospices Civils de Lyon, Lyon, France
| | - Vasilios Kimiskidis
- Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Teia Kobulashvili
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Giorgi Kuchukhidze
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Pål G Larsson
- Department of Neurosurgery, Clinic of Surgery and Neuroscience, Oslo University Hospital, Norway
| | - Markus Leitinger
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - Fergus Rugg-Gunn
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, United Kingdom; Department of Clinical and Experimental Epilepsy, University College, London, UK
| | - Margitta Seeck
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Switzerland
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Switzerland
| | - Geertjan Huiskamp
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Frans S S Leijten
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Pieter Van Eijsden
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Eugen Trinka
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria; Institute of Public Health, Medical Decision Making and HTA, UMIT, Private University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria
| | - Kees P J Braun
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands.
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Jafadideh AT, Asl BM. Modified Dominant Mode Rejection Beamformer for Localizing Brain Activities When Data Covariance Matrix Is Rank Deficient. IEEE Trans Biomed Eng 2018; 66:2241-2252. [PMID: 30561337 DOI: 10.1109/tbme.2018.2886251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Minimum variance beamformer (MVB) and its extensions fail in localizing short time brain activities particularly evoked potentials because of rank deficiency or inaccurate estimation of a data covariance matrix. In this paper, the conventional dominant mode rejection (DMR) adaptive beamformer is modified to localize brain short time activities. METHODS In the modified DMR, it is attempted to obtain a well-conditioned covariance matrix by dividing the eigenvalues of the data covariance matrix into dominant, medium, and small eigenvalues and then modifying medium and small parts. The performance of the proposed approach is compared with diagonal loading MVB (DL_MVB) and fast fully adaptive (FFA) beamformer by using simulated event-related potentials and real event-related field data. Eigenspace versions of DL_MVB and modified DMR are also implemented. RESULTS In all simulations, the modified DMR obtains the least localization error (0-5 mm) and spread radius (0-8 mm) when the signal-to-noise ratio (SNR) varies from 0 to 10 dB with step 1 dB. In real data, the new approach in comparison to two other ones attains the most concentrated power spectrum. Eigenspace projection of DL_MVB presents better results than DL_MVB but worse results than the modified DMR. Applying eigenspace projection on the proposed method improves its performance at high SNR levels. CONCLUSION Empirical results illustrate the superiority of the proposed DMR method to the DL_MVB and FFA in localizing brain short time activities. SIGNIFICANCE The proposed method can be utilized in source localization of epilepsy for presurgical clinical evaluation purpose and also in applications dealing with the localization of evoked potentials and fields.
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Kuo CC, Tucker DM, Luu P, Jenson K, Tsai JJ, Ojemann JG, Holmes MD. EEG source imaging of epileptic activity at seizure onset. Epilepsy Res 2018; 146:160-171. [DOI: 10.1016/j.eplepsyres.2018.07.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 07/06/2018] [Accepted: 07/16/2018] [Indexed: 01/16/2023]
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De Pretto M, Deiber MP, James CE. Steady-state evoked potentials distinguish brain mechanisms of self-paced versus synchronization finger tapping. Hum Mov Sci 2018; 61:151-166. [PMID: 30098488 DOI: 10.1016/j.humov.2018.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 07/12/2018] [Accepted: 07/18/2018] [Indexed: 10/28/2022]
Abstract
Sensorimotor synchronization (SMS) requires aligning motor actions to external events and represents a core part of both musical and dance performances. In the current study, to isolate the brain mechanisms involved in synchronizing finger tapping with a musical beat, we compared SMS to pure self-paced finger tapping and listen-only conditions at different tempi. We analyzed EEG data using frequency domain steady-state evoked potentials (SSEPs) to identify sustained electrophysiological brain activity during repetitive tasks. Behavioral results revealed different timing modes between SMS and self-paced finger tapping, associated with distinct scalp topographies, thus suggesting different underlying brain sources. After subtraction of the listen-only brain activity, SMS was compared to self-paced finger tapping. Resulting source estimations showed stronger activation of the left inferior frontal gyrus during SMS, and stronger activation of the bilateral inferior parietal lobule during self-paced finger tapping. These results point to the left inferior frontal gyrus as a pivot for perception-action coupling. We discuss our findings in the context of the ongoing debate about SSEPs interpretation given the variety of brain events contributing to SSEPs and similar EEG frequency responses.
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Affiliation(s)
- Michael De Pretto
- Faculty of Psychology and Educational Sciences, Department of Psychology, University of Geneva, 40 Boulevard du Pont-d'Arve, CH-1211 Geneva, Switzerland; Neurology Unit, Medicine Department, Faculty of Sciences, University of Fribourg, Chemin du Musée 5, CH-1700 Fribourg, Switzerland; School of Philosophy, Psychology and Language Sciences, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK.
| | - Marie-Pierre Deiber
- Psychiatry Department, Division of Psychiatric Specialties, University Hospitals of Geneva, 20 bis rue de Lausanne, CH-1201 Geneva, Switzerland; NCCR Synapsy, 9 Chemin des Mines, CH-1202 Geneva, Switzerland
| | - Clara E James
- Faculty of Psychology and Educational Sciences, Department of Psychology, University of Geneva, 40 Boulevard du Pont-d'Arve, CH-1211 Geneva, Switzerland; School of Health Sciences Geneva, HES-SO University of Applied Sciences and Arts Western Switzerland, 47 Avenue de Champel, CH-1206 Geneva, Switzerland
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Improved Back-Projection Cortical Potential Imaging by Multi-resolution Optimization Technique. Brain Topogr 2018; 32:66-79. [PMID: 30076487 DOI: 10.1007/s10548-018-0668-1] [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: 03/15/2018] [Accepted: 07/31/2018] [Indexed: 10/28/2022]
Abstract
Electroencephalogram (EEG) has evolved to be a well-established tool for imaging brain activity. This progress is mainly due to the development of high-resolution (HR) EEG methods. One class of HR-EEG is the cortical potential imaging (CPI), which aims to estimate the potential distribution on the cortical surface, which is much more informative than EEG. Even though these methods exhibit good performance, most of them have inherent inaccuracies that originate from their operating principles that constrain the solution or require a complex calculation process. The back-projection CPI (BP-CPI) method is relatively new and has the advantage of being constraint-free and computation inexpensive. The method has shown relatively good accuracy, which is necessary to become a clinical tool. However, better performance must be achieved. In the present study, two improvements are proposed. Both are embedded as adjacent stages to the BP-CPI and are based on the multi-resolution optimization approach (MR-CPI). A series of Monte-Carlo simulations were performed to examine the characteristics of the proposed improvements. Additional tests were done, including different EEG noise levels and variation in electrode-numbers. The results showed highly accurate cortical potential estimations, with a reduction in estimation error by a factor of 3.75 relative to the simple BP-CPI estimation error. We also validated these results with true EEG data. Analyzing these EEGs, we have demonstrated the MR-CPI competence to correctly localize cortical activations in a real environment. The MR-CPI methods were shown to be reliable for estimating cortical potentials, enabling researchers to obtain fast and robust high-resolution EEGs.
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Safavi SM, Lopour B, Chou PH. Reducing the Computational Complexity of EEG Source Localization With Cortical Patch Decomposition and Optimal Electrode Selection. IEEE Trans Biomed Eng 2018; 65:2298-2310. [PMID: 29993520 DOI: 10.1109/tbme.2018.2793882] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Real-time implementation of EEG source localization can be employed in a broad area of applications such as clinical diagnosis of neurologic diseases and brain-computer interface. However, a power-efficient, low-complexity, and real-time implementation of EEG source localization is still challenging due to extensive iterations in the solutions. In this study, two techniques are introduced to reduce the computational burden of the subspace-based MUltiple SIgnal Classification (MUSIC) algorithm. METHODS To shrink the exhaustive search inherent in MUSIC, the cortex is parsed into cortical regions. A novel nomination procedure involving a dictionary learning step will pick a number of regions to be searched for the active sources. In addition, a new electrode selection algorithm based on the Cramer-Rao bound of the errors is introduced to pick the best set of an arbitrary number of electrodes out of the total. RESULTS The performance of the proposed techniques were evaluated using simulated EEG signal under variation of different parameters such as the number of nominated regions, the signal to noise ratio, and the number of electrodes. The proposed techniques can reduce the computational complexity by up to $90\%$. Furthermore, the proposed techniques were tested on EEG data from an auditory oddball experiment. CONCLUSION A good concordance was observed in the comparison of the topographies and the localization errors derived from the proposed technique and regular MUSIC. SIGNIFICANCE Such reduction can be exploited in the real-time, long-run, and mobile monitoring of cortical activity for clinical diagnosis and research purposes.
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Abstract
Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Emery Brown
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, and Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana 47906, USA
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68
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Kumar S, Sharma A. A new parameter tuning approach for enhanced motor imagery EEG signal classification. Med Biol Eng Comput 2018; 56:1861-1874. [PMID: 29616456 DOI: 10.1007/s11517-018-1821-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 03/19/2018] [Indexed: 12/13/2022]
Abstract
A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals. Graphical abstract ᅟ.
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Affiliation(s)
- Shiu Kumar
- Department of Electronics, Instrumentation & Control Engineering, School of Electrical & Electronics Engineering, Fiji National University, Samabula, Fiji
- School of Engineering and Physics, Faculty of Science, Technology & Environment, The University of the South Pacific, Suva, Fiji
| | - Alok Sharma
- School of Engineering and Physics, Faculty of Science, Technology & Environment, The University of the South Pacific, Suva, Fiji.
- Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Brisbane, Australia.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
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Kumar S, Sharma A, Tsunoda T. An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information. BMC Bioinformatics 2017; 18:545. [PMID: 29297303 PMCID: PMC5751568 DOI: 10.1186/s12859-017-1964-6] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. METHODS In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. RESULTS The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. CONCLUSIONS Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.
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Affiliation(s)
- Shiu Kumar
- Department of Electronics, Instrumentation and Control Engineering, School of Electrical & Electronics Engineering, Fiji National University, Suva, Fiji. .,School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji.
| | - Alok Sharma
- School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji.,Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Brisbane, Australia.,RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,CREST, JST, Yokohama, 230-0045, Japan
| | - Tatsuhiko Tsunoda
- RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,CREST, JST, Yokohama, 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan
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Kumar S, Mamun K, Sharma A. CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI. Comput Biol Med 2017; 91:231-242. [PMID: 29100117 DOI: 10.1016/j.compbiomed.2017.10.025] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/08/2017] [Accepted: 10/23/2017] [Indexed: 11/18/2022]
Abstract
BACKGROUND Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. METHOD We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. RESULTS The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. CONCLUSION The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems.
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Affiliation(s)
- Shiu Kumar
- Department of Electronics, Instrumentation and Control, School of Electrical & Electronics Engineering, College of Engineering, Science and Technology, Fiji National University, Suva, Fiji; School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji.
| | - Kabir Mamun
- School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji.
| | - Alok Sharma
- School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji; Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Brisbane, Australia; RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
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Seeck M, Koessler L, Bast T, Leijten F, Michel C, Baumgartner C, He B, Beniczky S. The standardized EEG electrode array of the IFCN. Clin Neurophysiol 2017; 128:2070-2077. [DOI: 10.1016/j.clinph.2017.06.254] [Citation(s) in RCA: 204] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 05/10/2017] [Accepted: 06/20/2017] [Indexed: 11/17/2022]
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Hedrich T, Pellegrino G, Kobayashi E, Lina JM, Grova C. Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG. Neuroimage 2017; 157:531-544. [PMID: 28619655 DOI: 10.1016/j.neuroimage.2017.06.022] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 04/29/2017] [Accepted: 06/09/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The present study aims at evaluating and comparing electrical and magnetic distributed source imaging methods applied to high-density Electroencephalography (hdEEG) and Magnetoencephalography (MEG) data. We used resolution matrices to characterize spatial resolution properties of Minimum Norm Estimate (MNE), dynamic Statistical Parametric Mapping (dSPM), standardized Low-Resolution Electromagnetic Tomography (sLORETA) and coherent Maximum Entropy on the Mean (cMEM, an entropy-based technique). The resolution matrix provides information of the Point Spread Functions (PSF) and of the Crosstalk functions (CT), this latter being also called source leakage, as it reflects the influence of a source on its neighbors. METHODS The spatial resolution of the inverse operators was first evaluated theoretically and then with real data acquired using electrical median nerve stimulation on five healthy participants. We evaluated the Dipole Localization Error (DLE) and the Spatial Dispersion (SD) of each PSF and CT map. RESULTS cMEM showed the smallest spatial spread (SD) for both PSF and CT maps, whereas localization errors (DLE) were similar for all methods. Whereas cMEM SD values were lower in MEG compared to hdEEG, the other methods slightly favored hdEEG over MEG. In real data, cMEM provided similar localization error and significantly less spatial spread than other methods for both MEG and hdEEG. Whereas both MEG and hdEEG provided very accurate localizations, all the source imaging methods actually performed better in MEG compared to hdEEG according to all evaluation metrics, probably due to the higher signal-to-noise ratio of the data in MEG. CONCLUSION Our overall results show that all investigated methods provide similar localization errors, suggesting very accurate localization for both MEG and hdEEG when similar number of sensors are considered for both modalities. Intrinsic properties of source imaging methods as well as their behavior for well-controlled tasks, suggest an overall better performance of cMEM in regards to spatial resolution and spatial leakage for both hdEEG and MEG. This indicates that cMEM would be a good candidate for studying source localization of focal and extended generators as well as functional connectivity studies.
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Affiliation(s)
- T Hedrich
- Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University, Montreal, Canada.
| | - G Pellegrino
- Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University, Montreal, Canada; Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; San Camillo Hospital IRCCS, Venice, Italy
| | - E Kobayashi
- Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - J M Lina
- Département de Génie Électrique, École de Technologie Supérieure, Canada; Centre de recherches mathémathiques, Université de Montréal, Montreal, Canada; Center for Advanced Research on Sleep Medecine (CEAMS), hôpital du Sacré-Coeur, Montreal, Canada
| | - C Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University, Montreal, Canada; Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Physics Dpt., PERFORM Centre, Concordia University, Canada; Centre de recherches mathémathiques, Université de Montréal, Montreal, Canada
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73
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Nemtsas P, Birot G, Pittau F, Michel CM, Schaller K, Vulliemoz S, Kimiskidis VK, Seeck M. Source localization of ictal epileptic activity based on high-density scalp EEG data. Epilepsia 2017; 58:1027-1036. [DOI: 10.1111/epi.13749] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2017] [Indexed: 02/05/2023]
Affiliation(s)
- Petros Nemtsas
- Laboratory of Clinical Neurophysiology; AHEPA Hospital; Aristotle University of Thessaloniki; Thessaloniki Greece
| | - Gwenael Birot
- Department of Fundamental Neurosciences; Functional Brain Mapping Lab; University of Geneva; Geneva Switzerland
| | - Francesca Pittau
- EEG & Epilepsy Unit; Department of Clinical Neurosciences; University Hospital of Geneva; Geneva Switzerland
| | - Christoph M. Michel
- Department of Fundamental Neurosciences; Functional Brain Mapping Lab; University of Geneva; Geneva Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne; Geneva Switzerland
| | - Karl Schaller
- Department of Clinical Neurosciences; Neurosurgery Clinic; University Hospital of Geneva; Geneva Switzerland
| | - Serge Vulliemoz
- EEG & Epilepsy Unit; Department of Clinical Neurosciences; University Hospital of Geneva; Geneva Switzerland
| | - Vasilios K. Kimiskidis
- Laboratory of Clinical Neurophysiology; AHEPA Hospital; Aristotle University of Thessaloniki; Thessaloniki Greece
| | - Margitta Seeck
- EEG & Epilepsy Unit; Department of Clinical Neurosciences; University Hospital of Geneva; Geneva Switzerland
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Sohrabpour A, Worrell G. Identifying epileptic source location and extent: an iterative sparse electromagnetic source imaging algorithm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:109-112. [PMID: 28268292 DOI: 10.1109/embc.2016.7590652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper we have introduced a novel electromagnetic source imaging (ESI) technique and demonstrated its validity and excellent performance in imaging the location and extent of underlying epileptic sources in patients suffering from focal epilepsy. The proposed algorithm employs ideas from sparse signal processing literature and convex optimization theories to improve source imaging results obtained from scalp-recorded electroencephalogram (EEG). EEG source imaging results generally use subjective methods to determine the extent of the underlying brain activity. The proposed technique provides significant improvement in dealing with such shortcomings and eliminates the need for thresholding. The results of our computer simulations and clinical validation study demonstrate the excellent performance of the proposed algorithm and suggest it may become a useful tool for objectively determining the location and extent of focal epileptic activity in a noninvasive fashion.
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Al-Subari K, Al-Baddai S, Tomé AM, Volberg G, Ludwig B, Lang EW. Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. PLoS One 2016; 11:e0167957. [PMID: 27936219 PMCID: PMC5148586 DOI: 10.1371/journal.pone.0167957] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 11/15/2016] [Indexed: 11/18/2022] Open
Abstract
Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data.
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Affiliation(s)
- Karema Al-Subari
- Department of Biology, Institute of Biophysics, University of Regensburg, Regensburg, Germany
- Department of Linguistics, Literature and Culture, Institute of Information Science, University of Regensburg, Regensburg, Germany
| | - Saad Al-Baddai
- Department of Biology, Institute of Biophysics, University of Regensburg, Regensburg, Germany
- Department of Linguistics, Literature and Culture, Institute of Information Science, University of Regensburg, Regensburg, Germany
| | - Ana Maria Tomé
- Department of Electrical Engineering, Telecommunication and Informatics, Institut of Electrical Engineering and Electronics, Universidade de Aveiro, Aveiro, Portugal
| | - Gregor Volberg
- Department of Psychology, Pedagogics and Sport, Institute of Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Bernd Ludwig
- Department of Linguistics, Literature and Culture, Institute of Information Science, University of Regensburg, Regensburg, Germany
| | - Elmar W. Lang
- Department of Biology, Institute of Biophysics, University of Regensburg, Regensburg, Germany
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Sohrabpour A, Ye S, Worrell GA, Zhang W, He B. Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach. IEEE Trans Biomed Eng 2016; 63:2474-2487. [PMID: 27740473 PMCID: PMC5152676 DOI: 10.1109/tbme.2016.2616474] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. METHODS Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). RESULTS Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. CONCLUSION Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). SIGNIFICANCE The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.
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Affiliation(s)
- Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Shuai Ye
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | | | - Wenbo Zhang
- Minnesota Epilepsy Group, United Hospital, MN 55102 USA and also with the Department of Neurology, University of Minnesota, Minneapolis, 55455 USA
| | - Bin He
- Department of Biomedical Engineering, and the Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455 USA
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Sohrabpour A, Lu Y, Worrell G, He B. Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy. Neuroimage 2016; 142:27-42. [PMID: 27241482 PMCID: PMC5124544 DOI: 10.1016/j.neuroimage.2016.05.064] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Revised: 05/09/2016] [Accepted: 05/26/2016] [Indexed: 11/23/2022] Open
Abstract
Estimating extended brain sources using EEG/MEG source imaging techniques is challenging. EEG and MEG have excellent temporal resolution at millisecond scale but their spatial resolution is limited due to the volume conduction effect. We have exploited sparse signal processing techniques in this study to impose sparsity on the underlying source and its transformation in other domains (mathematical domains, like spatial gradient). Using an iterative reweighting strategy to penalize locations that are less likely to contain any source, it is shown that the proposed iteratively reweighted edge sparsity minimization (IRES) strategy can provide reasonable information regarding the location and extent of the underlying sources. This approach is unique in the sense that it estimates extended sources without the need of subjectively thresholding the solution. The performance of IRES was evaluated in a series of computer simulations. Different parameters such as source location and signal-to-noise ratio were varied and the estimated results were compared to the targets using metrics such as localization error (LE), area under curve (AUC) and overlap between the estimated and simulated sources. It is shown that IRES provides extended solutions which not only localize the source but also provide estimation for the source extent. The performance of IRES was further tested in epileptic patients undergoing intracranial EEG (iEEG) recording for pre-surgical evaluation. IRES was applied to scalp EEGs during interictal spikes, and results were compared with iEEG and surgical resection outcome in the patients. The pilot clinical study results are promising and demonstrate a good concordance between noninvasive IRES source estimation with iEEG and surgical resection outcomes in the same patients. The proposed algorithm, i.e. IRES, estimates extended source solutions from scalp electromagnetic signals which provide relatively accurate information about the location and extent of the underlying source.
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Affiliation(s)
- Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Yunfeng Lu
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | | | - Bin He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA.
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Mălîia MD, Meritam P, Scherg M, Fabricius M, Rubboli G, Mîndruţă I, Beniczky S. Epileptiform discharge propagation: Analyzing spikes from the onset to the peak. Clin Neurophysiol 2016; 127:2127-33. [DOI: 10.1016/j.clinph.2015.12.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 12/22/2015] [Accepted: 12/31/2015] [Indexed: 11/30/2022]
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Hülsdünker T, Mierau A, Strüder HK. Higher Balance Task Demands are Associated with an Increase in Individual Alpha Peak Frequency. Front Hum Neurosci 2016; 9:695. [PMID: 26779005 PMCID: PMC4702132 DOI: 10.3389/fnhum.2015.00695] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 12/07/2015] [Indexed: 12/03/2022] Open
Abstract
Balance control is fundamental for most daily motor activities, and its impairment is associated with an increased risk of falling. Growing evidence suggests the human cortex is essentially contributing to the control of standing balance. However, the exact mechanisms remain unclear and need further investigation. In a previous study, we introduced a new protocol to identify electrocortical activity associated with performance of different continuous balance tasks with the eyes opened. The aim of this study was to extend our previous results by investigating the individual alpha peak frequency (iAPF), a neurophysiological marker of thalamo-cortical information transmission, which remained unconsidered so far in balance research. Thirty-seven subjects completed nine balance tasks varying in surface stability and base of support. Electroencephalography (EEG) was recorded from 32 scalp locations throughout balancing with the eyes closed to ensure reliable identification of the iAPF. Balance performance was quantified as the sum of anterior-posterior and medio-lateral movements of the supporting platform. The iAPF, as well as power in the theta, lower alpha and upper alpha frequency bands were determined for each balance task after applying an ICA-based artifact rejection procedure. Higher demands on balance control were associated with a global increase in iAPF and a decrease in lower alpha power. These results may indicate increased thalamo-cortical information transfer and general cortical activation, respectively. In addition, a significant increase in upper alpha activity was observed in the fronto-central region whereas it decreased in the centro-parietal region. Furthermore, midline theta increased with higher task demands probably indicating activation of error detection/processing mechanisms. IAPF as well as theta and alpha power were correlated with platform movements. The results provide new insights into spectral and spatial characteristics of cortical oscillations subserving balance control. This information may be particularly useful in a clinical context as it could be used to reveal cortical contributions to balance dysfunction in specific populations such as Parkinson’s or vestibular loss. However, this should be addressed in future studies.
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Affiliation(s)
- Thorben Hülsdünker
- Institute of Movement and Neurosciences, German Sport University Cologne Cologne, Germany
| | - Andreas Mierau
- Institute of Movement and Neurosciences, German Sport University Cologne Cologne, Germany
| | - Heiko K Strüder
- Institute of Movement and Neurosciences, German Sport University Cologne Cologne, Germany
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80
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EEG source localization: Sensor density and head surface coverage. J Neurosci Methods 2015; 256:9-21. [PMID: 26300183 DOI: 10.1016/j.jneumeth.2015.08.015] [Citation(s) in RCA: 197] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 08/10/2015] [Accepted: 08/12/2015] [Indexed: 11/21/2022]
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81
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Adebimpe A, Aarabi A, Bourel-Ponchel E, Mahmoudzadeh M, Wallois F. Functional Brain Dysfunction in Patients with Benign Childhood Epilepsy as Revealed by Graph Theory. PLoS One 2015; 10:e0139228. [PMID: 26431333 PMCID: PMC4592214 DOI: 10.1371/journal.pone.0139228] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 09/10/2015] [Indexed: 11/24/2022] Open
Abstract
There is growing evidence that brain networks are altered in epileptic subjects. In this study, we investigated the functional connectivity and brain network properties of benign childhood epilepsy with centrotemporal spikes using graph theory. Benign childhood epilepsy with centrotemporal spikes is the most common form of idiopathic epilepsy in young children under the age of 16 years. High-density EEG data were recorded from patients and controls in resting state with eyes closed. Data were preprocessed and spike and spike-free segments were selected for analysis. Phase locking value was calculated for all paired combinations of channels and for five frequency bands (δ, θ, α, β1 and β2). We computed the degree and small-world parameters—clustering coefficient (C) and path length (L)—and compared the two patient conditions to controls. A higher degree at epileptic zones during interictal epileptic spikes (IES) was observed in all frequency bands. Both patient conditions reduced connection at the occipital and right frontal regions close to the epileptic zone in the α band. The “small-world” features (high C and short L) were deviated in patients compared to controls. A changed from an ordered network in the δ band to a more randomly organized network in the α band was observed in patients compared to healthy controls. These findings show that the benign epileptic brain network is disrupted not only at the epileptic zone, but also in other brain regions especially frontal regions.
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Affiliation(s)
- Azeez Adebimpe
- Institut National de la Santé et de la Recherche Médicale (INSERM U1105), Centre Universitaire de Recheche en Santé (CURS), University Hospital, Amiens, France
| | - Ardalan Aarabi
- Institut National de la Santé et de la Recherche Médicale (INSERM U1105), Centre Universitaire de Recheche en Santé (CURS), University Hospital, Amiens, France
- * E-mail:
| | - Emilie Bourel-Ponchel
- Institut National de la Santé et de la Recherche Médicale (INSERM U1105), Centre Universitaire de Recheche en Santé (CURS), University Hospital, Amiens, France
- Explorations Fonctionnelles du Système Nerveux (EFSN) pédiatrique, University Hospital, Amiens, France
| | - Mahdi Mahmoudzadeh
- Institut National de la Santé et de la Recherche Médicale (INSERM U1105), Centre Universitaire de Recheche en Santé (CURS), University Hospital, Amiens, France
- Explorations Fonctionnelles du Système Nerveux (EFSN) pédiatrique, University Hospital, Amiens, France
| | - Fabrice Wallois
- Institut National de la Santé et de la Recherche Médicale (INSERM U1105), Centre Universitaire de Recheche en Santé (CURS), University Hospital, Amiens, France
- Explorations Fonctionnelles du Système Nerveux (EFSN) pédiatrique, University Hospital, Amiens, France
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82
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Thalamocortical relationship in epileptic patients with generalized spike and wave discharges--A multimodal neuroimaging study. NEUROIMAGE-CLINICAL 2015; 9:117-27. [PMID: 26448912 PMCID: PMC4552814 DOI: 10.1016/j.nicl.2015.07.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 05/30/2015] [Accepted: 07/05/2015] [Indexed: 01/01/2023]
Abstract
Unlike focal or partial epilepsy, which has a confined range of influence, idiopathic generalized epilepsy (IGE) often affects the whole or a larger portion of the brain without obvious, known cause. It is important to understand the underlying network which generates epileptic activity and through which epileptic activity propagates. The aim of the present study was to investigate the thalamocortical relationship using non-invasive imaging modalities in a group of IGE patients. We specifically investigated the roles of the mediodorsal nuclei in the thalami and the medial frontal cortex in generating and spreading IGE activities. We hypothesized that the connectivity between these two structures is key in understanding the generation and propagation of epileptic activity in brains affected by IGE. Using three imaging techniques of EEG, fMRI and EEG-informed fMRI, we identified important players in generation and propagation of generalized spike-and-wave discharges (GSWDs). EEG-informed fMRI suggested multiple regions including the medial frontal area near to the anterior cingulate cortex, mediodorsal nuclei of the thalamus, caudate nucleus among others that related to the GSWDs. The subsequent seed-based fMRI analysis revealed a reciprocal cortical and bi-thalamic functional connection. Through EEG-based Granger Causality analysis using (DTF) and adaptive DTF, within the reciprocal thalamocortical circuitry, thalamus seems to serve as a stronger source in driving cortical activity from initiation to the propagation of a GSWD. Such connectivity change starts before the GSWDs and continues till the end of the slow wave discharge. Thalamus, especially the mediodorsal nuclei, may serve as potential targets for deep brain stimulation to provide more effective treatment options for patients with drug-resistant generalized epilepsy.
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83
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Sohrabpour A. Estimating underlying neuronal activity from EEG using an iterative sparse technique. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:634-637. [PMID: 26736342 DOI: 10.1109/embc.2015.7318442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this paper a novel technique for solving the bio-electromagnetic inverse problem is proposed. This method provides information about the location and extent of underlying neuronal activity. This is essential for the presurgical planning for partial epilepsy patients who are resistant to anti-epileptic drugs. The proposed algorithm takes advantage of the fact that neuronal activity transparent to EEG, arises from a spatially extended brain region. This spatial coherence is modeled within the framework of sparse signal processing techniques and makes better use of the limited number of EEG recordings. An iterative data-driven weighting is also introduced to better the extent estimation as well as eliminating the need to threshold estimated solutions.
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84
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He B, Sohrabpour A. Imaging epileptogenic brain using high density EEG source imaging and MRI. Clin Neurophysiol 2015; 127:5-7. [PMID: 26051752 DOI: 10.1016/j.clinph.2015.04.074] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Revised: 04/30/2015] [Accepted: 04/30/2015] [Indexed: 10/23/2022]
Affiliation(s)
- Bin He
- Department of Biomedical Engineering, University of Minnesota, 312 Church Street, SE, Minneapolis, MN 55455, USA.
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, 312 Church Street, SE, Minneapolis, MN 55455, USA
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85
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High density EEG-what do we have to lose? Clin Neurophysiol 2014; 126:433-4. [PMID: 25113273 DOI: 10.1016/j.clinph.2014.07.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 06/30/2014] [Accepted: 07/02/2014] [Indexed: 11/22/2022]
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