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Abul Hasan M, Shahid H, Ahmed Qazi S, Ejaz O, Danish Mujib M, Vuckovic A. Underpinning the neurological source of executive function following cross hemispheric tDCS stimulation. Int J Psychophysiol 2023; 185:1-10. [PMID: 36634750 DOI: 10.1016/j.ijpsycho.2023.01.004] [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: 05/08/2022] [Revised: 12/22/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023]
Abstract
Transcranial direct current stimulation (tDCS) is a promising technique for enhancement of executive functions in healthy as well as neurologically disturbed patients. However, the evidence regarding the neuropsychological and behavioral change with neurophysiological shifts as well as the mechanism of tDCS action as evidenced by activation of neuronal sources important for executive functions have remained unaddressed. The study thereby endeavors to (1) determine the neuropsychological, behavioral, and neurophysiological change induced with five sessions of bilateral tDCS stimulation and (2) identify putative neuronal sources related to the executive functions responsible for neuropsychological and behavioral change. For this single blinded study, a total of 40 healthy participants, randomly allocated to active (n = 19) or sham (n = 21) groups completed five sessions of 2 mA tDCS stimulation administered over Dorso-Lateral Prefrontal Cortex (DLPFC) (F3 as anode, F4 as cathode). Repeated measure analysis was performed on neuropsychological (Everyday Memory Questionnaire and Mindful Attention Awareness Scale), and behavioral assessment (n-Back and Stroop tests) to investigate within and between group differences. Pre and post neurophysiological (Electroencephalogram) results showed that bilateral tDCS stimulation activates cortical regions responsible for executive functions including updation (working memory) and inhibition (interference control or attention). Multiple sessions of bilateral tDCS stimulation results in a significant increase in theta, alpha, and beta-band activity in the DLPFC, cingulate and parietal cortex. This study provides evidence that tDCS can be used for performance enhancement of executive functions in able-bodied people.
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Affiliation(s)
- Muhammad Abul Hasan
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi, Pakistan; Neurocomputation Laboratory, National Center of Artificial Intelligence, Karachi, Pakistan
| | - Hira Shahid
- Neurocomputation Laboratory, National Center of Artificial Intelligence, Karachi, Pakistan; Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom.
| | - Saad Ahmed Qazi
- Neurocomputation Laboratory, National Center of Artificial Intelligence, Karachi, Pakistan; Department of Electrical Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Osama Ejaz
- Neurocomputation Laboratory, National Center of Artificial Intelligence, Karachi, Pakistan
| | - Muhammad Danish Mujib
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Aleksandra Vuckovic
- Biomedical Engineering Division, University of Glasgow, Glasgow, United Kingdom
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Olamat A, Ozel P, Atasever S. Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition. Int J Neural Syst 2022; 32:2250021. [DOI: 10.1142/s0129065722500216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Currently, Fourier-based, wavelet-based, and Hilbert-based time–frequency techniques have generated considerable interest in classification studies for emotion recognition in human–computer interface investigations. Empirical mode decomposition (EMD), one of the Hilbert-based time–frequency techniques, has been developed as a tool for adaptive signal processing. Additionally, the multi-variate version strongly influences designing the common oscillation structure of a multi-channel signal by utilizing the common instantaneous concepts of frequency and bandwidth. Additionally, electroencephalographic (EEG) signals are strongly preferred for comprehending emotion recognition perspectives in human–machine interactions. This study aims to herald an emotion detection design via EEG signal decomposition using multi-variate empirical mode decomposition (MEMD). For emotion recognition, the SJTU emotion EEG dataset (SEED) is classified using deep learning methods. Convolutional neural networks (AlexNet, DenseNet-201, ResNet-101, and ResNet50) and AutoKeras architectures are selected for image classification. The proposed framework reaches 99% and 100% classification accuracy when transfer learning methods and the AutoKeras method are used, respectively.
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Affiliation(s)
- Ali Olamat
- Biomedical Engineering Department, Yildiz Technical University, Istanbul, Turkey
| | - Pinar Ozel
- Biomedical Engineering Department, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey
| | - Sema Atasever
- Computer Engineering Department, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey
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Hasan MA, Vuckovic A, Qazi SA, Yousuf Z, Shahab S, Fraser M. Immediate effect of neurofeedback training on the pain matrix and cortical areas involved in processing neuropsychological functions. Neurol Sci 2021; 42:4551-4561. [PMID: 33624179 DOI: 10.1007/s10072-021-05125-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 02/09/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVE This study investigated the impact of neurofeedback training on the deeper cortical structures that comprise the "pain matrix" and are involved in processing neuropsychological functions. METHODS Five paraplegic patients with central neuropathic pain received up to 40 sessions of neurofeedback training. They were asked to simultaneously modulate the relative power of the theta, alpha and beta bands, provided as a feedback from the sensorimotor cortex. The source localization technique was applied on EEG data recorded with 16 electrodes placed over the whole head. RESULTS Neurofeedback training from the sensorimotor cortex induced effects on the pain matrix and in the areas involved in processing neuropsychological functions such as memory, executive functions and emotional regulations. Alpha and beta band activity was most increased in insular, cingulate and frontal cortex regions, and other areas corresponding to executive and emotional function processing. Theta band decreases were noted in the frontal, cingulate and motor cortices. In group analysis, theta and beta band activity was significantly reduced. CONCLUSION The single channel electroencephalogram-based neurofeedback training produced effects on similar areas that are targeted in 19 channels standardized low-resolution brain electromagnetic tomography and expensive time-delayed functional magnetic resonance imaging feedback studies.
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Affiliation(s)
- Muhammad Abul Hasan
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi, Pakistan. .,Neurocomputation Laboratory, National Center of Artificial Intelligence, Karachi, Pakistan.
| | - Aleksandra Vuckovic
- Centre for Rehabilitation Engineering, Biomedical Engineering Division, School of Engineering, University of Glasgow, Glasgow, UK
| | - Saad A Qazi
- Neurocomputation Laboratory, National Center of Artificial Intelligence, Karachi, Pakistan.,Department of Electrical Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Zuha Yousuf
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi, Pakistan.,Neurocomputation Laboratory, National Center of Artificial Intelligence, Karachi, Pakistan.,Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Sania Shahab
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Matthew Fraser
- Queen Elizabeth National Spinal Injuries Unit, Southern General Hospital, Glasgow, UK
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Fathima S, Kore SK. Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems-A Review. Front Neurosci 2021; 14:546656. [PMID: 33551716 PMCID: PMC7859253 DOI: 10.3389/fnins.2020.546656] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 12/18/2020] [Indexed: 11/13/2022] Open
Abstract
Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.
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Affiliation(s)
- Shireen Fathima
- Department of Electronics and Communication Engineering, HKBK College of Engineering, Bengaluru, India
| | - Sheela Kiran Kore
- Department of Electronics and Communication Engineering, KLE Dr. M. S. Sheshagiri College of Engineering and Technology, Belgaum, India
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Khosropanah P, Ho ETW, Lim KS, Fong SL, Thuy Le MA, Narayanan V. EEG Source Imaging (ESI) utility in clinical practice. BIOMED ENG-BIOMED TE 2020; 65:/j/bmte.ahead-of-print/bmt-2019-0128/bmt-2019-0128.xml. [PMID: 32623371 DOI: 10.1515/bmt-2019-0128] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 02/21/2020] [Indexed: 11/15/2022]
Abstract
Epilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73-91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.
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Affiliation(s)
- Pegah Khosropanah
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Eric Tatt-Wei Ho
- Center for Intelligent Signal & Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
- Department of Electrical & Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Kheng-Seang Lim
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Si-Lei Fong
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Minh-An Thuy Le
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Department of Neurology, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam
| | - Vairavan Narayanan
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Soler A, Muñoz-Gutiérrez PA, Bueno-López M, Giraldo E, Molinas M. Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition. Front Neurosci 2020; 14:175. [PMID: 32180702 PMCID: PMC7059768 DOI: 10.3389/fnins.2020.00175] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 02/17/2020] [Indexed: 11/13/2022] Open
Abstract
Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide a priori time-frequency information to improve the estimation of neural activity. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. However, attempts to use EMD in EEG analysis have not yet provided optimal reconstructions, due to the intrinsic mode-mixing problem of EMD. Several studies have used single-channel analysis, whereas others have used multiple-channel analysis for other applications. Here, we present the results of multiple-channel analysis using multivariate empirical mode decomposition (MEMD) to reduce the mode-mixing problem and provide useful a priori time-frequency information for the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated using real and synthetic EEG data, in which the reconstructions were performed using the multiple sparse priors (MSP) algorithm with EEG electrode montages of 32, 16, and 8 electrodes. The quality of the source reconstruction was assessed using the Wasserstein metric. A comparison of the solutions without pre-processing and those after applying MEMD showed the source reconstructions to be improved using MEMD as a priori information for the low-density montages of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced by 59.42 and 66.04% for the 8 and 16 electrode montages, respectively, and that on a simulated EEG with three active sources, by 87.31 and 31.45% for the same electrode montages.
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Affiliation(s)
- Andres Soler
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Pablo A. Muñoz-Gutiérrez
- Department of Electronic Engineering, Universidad del Quindío, Armenia, Colombia
- Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia
| | - Maximiliano Bueno-López
- Department of Electronics, Instrumentation, and Control, Universidad del Cauca, Popayán, Colombia
| | - Eduardo Giraldo
- Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia
| | - Marta Molinas
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
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A Novel Noninvasive Approach Based on SPECT and EEG for the Location of the Epileptogenic Zone in Pharmacoresistant Non-Lesional Epilepsy. MEDICINA-LITHUANIA 2019; 55:medicina55080478. [PMID: 31416172 PMCID: PMC6722599 DOI: 10.3390/medicina55080478] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/05/2019] [Accepted: 08/08/2019] [Indexed: 11/30/2022]
Abstract
Background and objectives: The aim of this study is to propose a methodology that combines non-invasive functional modalities electroencephalography (EEG) and single photon emission computed tomography (SPECT) to estimate the location of the epileptogenic zone (EZ) for the presurgical evaluation of patients with drug-resistant non-lesional epilepsy. Materials and Methods: This methodology consists of: (i) Estimation of ictal EEG source imaging (ESI); (ii) application of the subtraction of ictal and interictal SPECT co-registered with MRI (SISCOM) methodology; and (iii) estimation of ESI but using the output of the SISCOM as a priori information for the estimation of the sources. The methodology was implemented in a case series as an example of the application of this novel approach for the presurgical evaluation. A gold standard and a coincidence analysis based on measures of sensitivity and specificity were used as a preliminary assessment of the proposed methodology to localize EZ. Results: In patients with good postoperative evolution, the estimated EZ presented a spatial coincidence with the resection site represented by high values of sensitivity and specificity. For the patient with poor postoperative evolution, the methodology showed a partial incoherence between the estimated EZ and the resection site. In cases of multifocal epilepsy, the method proposed spatially extensive epileptogenic zones. Conclusions: The results of the case series provide preliminary evidence of the methodology’s potential to epileptogenic zone localization in non-lesion drug-resistant epilepsy. The novelty of the article consists in estimating the sources of ictal EEG using SISCOM result as a prior for the inverse solution. Future studies are necessary in order to validate the described methodology. The results constitute a starting point for further studies in order to support the clinical reliability of the proposed methodology and advocate for their implementation in the presurgical evaluation of patients with intractable non-lesional epilepsy.
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