Journal articles on the topic 'SCALP framework'
To see the other types of publications on this topic, follow the link: SCALP framework.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'SCALP framework.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Pflieger, Mark E. "Inferring contextual field interactions from scalp EEG." Behavioral and Brain Sciences 26, no. 1 (February 2003): 99–100. http://dx.doi.org/10.1017/s0140525x03390028.
Abstract:
AbstractThis commentary highlights methods for using scalp EEG to make inferences about contextual field interactions, which, in view of the target article, may be specially relevant to the study of schizophrenia. Although scalp EEG has limited spatial resolution, prior knowledge combined with experimental manipulations may be used to strengthen inferences about underlying brain processes. Both spatial and temporal context are discussed within the framework of nonlinear interactions. Finally, results from a visual contour integration EEG pilot study are summarized in view of a hypothesis that relates receptive field and contextual field processing to evoked and induced activity, respectively.
2
Faes, Luca, Daniele Marinazzo, Giandomenico Nollo, and Alberto Porta. "An Information-Theoretic Framework to Map the Spatiotemporal Dynamics of the Scalp Electroencephalogram." IEEE Transactions on Biomedical Engineering 63, no. 12 (December 2016): 2488–96. http://dx.doi.org/10.1109/tbme.2016.2569823.
3
Kotiuchyi, Ivan, Riccardo Pernice, Anton Popov, Luca Faes, and Volodymyr Kharytonov. "A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks." Brain Sciences 10, no. 9 (September 22, 2020): 657. http://dx.doi.org/10.3390/brainsci10090657.
Abstract:
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.
4
Kilicarslan, Atilla, Robert G. Grossman, and Jose Luis Contreras-Vidal. "A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements." Journal of Neural Engineering 13, no. 2 (February 10, 2016): 026013. http://dx.doi.org/10.1088/1741-2560/13/2/026013.
5
Carvalhaes, Claudio G., and Patrick Suppes. "A Spline Framework for Estimating the EEG Surface Laplacian Using the Euclidean Metric." Neural Computation 23, no. 11 (November 2011): 2974–3000. http://dx.doi.org/10.1162/neco_a_00192.
Abstract:
This letter develops a framework for EEG analysis and similar applications based on polyharmonic splines. This development overcomes a basic problem with the method of splines in the Euclidean setting: that it does not work on low-degree algebraic surfaces such as spherical and ellipsoidal scalp models. The method’s capability is illustrated through simulations on the three-sphere model and using empirical data.
6
Wei, Boxuan, Xiaohui Zhao, Lijuan Shi, Lu Xu, Tao Liu, and Jicong Zhang. "A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram." Journal of Neural Engineering 18, no. 4 (July 21, 2021): 0460b3. http://dx.doi.org/10.1088/1741-2552/ac0d60.
7
Tuckute, Greta, Sofie Therese Hansen, Nicolai Pedersen, Dea Steenstrup, and Lars Kai Hansen. "Single-Trial Decoding of Scalp EEG under Natural Conditions." Computational Intelligence and Neuroscience 2019 (April 17, 2019): 1–11. http://dx.doi.org/10.1155/2019/9210785.
Abstract:
There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding.
8
Sharma, Shanu, Ashwani Kumar Dubey, and Priya Ranjan. "Affective Video Tagging Framework using Human Attention Modelling through EEG Signals." International Journal of Intelligent Information Technologies 18, no. 1 (January 1, 2022): 1–18. http://dx.doi.org/10.4018/ijiit.306968.
Abstract:
The explosion of multimedia content over the past years is not surprising, thus their efficient management and analysis methods are always in demand. The affectiveness of any multimedia content deals with analyzing human perception and cognition while watching it. Human attention is also one of the important parameters, as it describes the engagement and interestingness of the user while watching that content. Considering this aspect, a video tagging framework is proposed in which the EEG signals of participants are used to analyze human perception while watching videos. A rigorous analysis has been performed on different scalp locations and frequency rhythms of brain signals to formulate significant features corresponding to affective and interesting video content. The analysis presented in this paper shows that the extracted human attention-based features are generating promising results with the accuracy of 93.2% using SVM-based classification model which supports the applicability of the model for various BCI-based applications for automatic classification of multimedia content.
9
Gorina-Careta, Natàlia, Teresa Ribas-Prats, Sonia Arenillas-Alcón, Marta Puertollano, M. Dolores Gómez-Roig, and Carles Escera. "Neonatal Frequency-Following Responses: A Methodological Framework for Clinical Applications." Seminars in Hearing 43, no. 03 (August 2022): 162–76. http://dx.doi.org/10.1055/s-0042-1756162.
Abstract:
AbstractThe frequency-following response (FFR) to periodic complex sounds is a noninvasive scalp-recorded auditory evoked potential that reflects synchronous phase-locked neural activity to the spectrotemporal components of the acoustic signal along the ascending auditory hierarchy. The FFR has gained recent interest in the fields of audiology and auditory cognitive neuroscience, as it has great potential to answer both basic and applied questions about processes involved in sound encoding, language development, and communication. Specifically, it has become a promising tool in neonates, as its study may allow both early identification of future language disorders and the opportunity to leverage brain plasticity during the first 2 years of life, as well as enable early interventions to prevent and/or ameliorate sound and language encoding disorders. Throughout the present review, we summarize the state of the art of the neonatal FFR and, based on our own extensive experience, present methodological approaches to record it in a clinical environment. Overall, the present review is the first one that comprehensively focuses on the neonatal FFRs applications, thus supporting the feasibility to record the FFR during the first days of life and the predictive potential of the neonatal FFR on detecting short- and long-term language abilities and disruptions.
10
Bhagubai, Miguel, Kaat Vandecasteele, Lauren Swinnen, Jaiver Macea, Christos Chatzichristos, Maarten De Vos, and Wim Van Paesschen. "The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG." Bioengineering 10, no. 4 (April 20, 2023): 491. http://dx.doi.org/10.3390/bioengineering10040491.
Abstract:
Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm’s detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.
11
Placidi, Giuseppe, Luigi Cinque, and Matteo Polsinelli. "A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of Independent Components." Computers in Biology and Medicine 132 (May 2021): 104347. http://dx.doi.org/10.1016/j.compbiomed.2021.104347.
12
Seery, Gerard E. "Guidelines for Designing and Locating Hairlines." American Journal of Cosmetic Surgery 15, no. 1 (March 1998): 21–26. http://dx.doi.org/10.1177/074880689801500106.
Abstract:
Hair restoration literature contains a paucity of information on how to design and locate an aesthetically pleasing hairline. Simple guidelines are formulated for accurately designing and locating hairlines. Certain key topographical landmarks identified on the frontal scalp are used to construct a framework of three parallel lines on which the hairline is designed and positioned. The most appropriate hairline takes into consideration many factors unique for each individual. Regardless of how expertly a hairline zone is constructed, the ultimate aesthetic result is contingent on design and placement.
13
Monin, Daniel L., Ken Kazahaya, and Kevin H. Franck. "Routine Use of the Crystal Device Integrity Testing System in Pediatric Patients." Journal of the American Academy of Audiology 17, no. 10 (November 2006): 722–32. http://dx.doi.org/10.3766/jaaa.17.10.4.
Abstract:
Crystal Device Integrity Testing System (CITS), the first commercially available testing system of its type, allows rapid assessment of cochlear implant function by measuring averaged electrode voltages—the scalp-recorded fields generated by electrode currents. We describe our experience performing routine integrity tests on 44 pediatric cochlear implant patients using the CITS. We present our findings focusing on the monopolar and common ground scans to provide a framework from which CITS scans can be evaluated in the future. We also describe selected cases in which abnormal results using the CITS influenced clinical treatment, demonstrating the utility of performing routine integrity tests.
14
Barzegaran, Elham, Sebastian Bosse, Peter J. Kohler, and Anthony M. Norcia. "EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise." Journal of Neuroscience Methods 328 (December 2019): 108377. http://dx.doi.org/10.1016/j.jneumeth.2019.108377.
15
Wan, Guihong, Meng Jiao, Xinglong Ju, Yu Zhang, Haim Schweitzer, and Feng Liu. "Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (June 26, 2023): 12491–99. http://dx.doi.org/10.1609/aaai.v37i10.26471.
Abstract:
Electrophysiological Source Imaging (ESI) refers to reconstructing the underlying brain source activation from non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) measurements on the scalp. Estimating the source locations and their extents is a fundamental tool in clinical and neuroscience applications. However, the estimation is challenging because of the ill-posedness and high coherence in the leadfield matrix as well as the noise in the EEG/MEG data. In this work, we proposed a combinatorial search framework to address the ESI problem with a provable optimality guarantee. Specifically, by exploiting the graph neighborhood information in the brain source space, we converted the ESI problem into a graph search problem and designed a combinatorial search algorithm under the framework of A* to solve it. The proposed algorithm is guaranteed to give an optimal solution to the ESI problem. Experimental results on both synthetic data and real epilepsy EEG data demonstrated that the proposed algorithm could faithfully reconstruct the source activation in the brain.
16
Chaibi, Sahbi, Tarek Lajnef, Abdelbacet Ghrob, Mounir Samet, and Abdennaceur Kachouri. "A Robustness Comparison of Two Algorithms Used for EEG Spike Detection." Open Biomedical Engineering Journal 9, no. 1 (July 31, 2015): 151–56. http://dx.doi.org/10.2174/1874120701509010151.
Abstract:
Spikes and sharp waves recorded on scalp EEG may play an important role in identifying the epileptogenic network as well as in understanding the central nervous system. Therefore, several automatic and semi-automatic methods have been implemented to detect these two neural transients. A consistent gold standard associated with a high degree of agreement among neuroscientists is required to measure relevant performance of different methods. In fact, scalp EEG data can often be corrupted by a set of artifacts and are not always served as data of gold standard. For this reason, the use of intracerebral EEG data mixed with gaussian noise seems to best resemble the output of scalp EEG brain and serves as a consistent gold standard. In the present framework, we test the robustness of two important methods that have been previously used for the automatic detection of epileptiform transients (spikes and sharp waves). These methods are based respectively on Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). Our purpose is to elaborate a comparative study in terms of sensitivity and selectivity changes via the decrease of Signal to Noise Ratio (SNR), which is ranged from 10 dB up to -10 dB. The results demonstrate that, DWT approach turns to be more stable in terms of sensitivity, and it successfully follows the detection of relevant spikes with the decrease of SNR. However, CWT-based approach remains more stable in terms of selectivity, so that, it performs well in terms of rejecting false spikes compared to DWT approach.
17
Kim, Jong-Hwan, Segi Kwon, Jirui Fu, and Joon-Hyuk Park. "Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN." Journal of Imaging 8, no. 10 (October 14, 2022): 283. http://dx.doi.org/10.3390/jimaging8100283.
Abstract:
Early and accurate detection of scalp hair loss is imperative to provide timely and effective treatment plans to halt further progression and save medical costs. Many techniques have been developed leveraging deep learning to automate the hair loss detection process. However, the accuracy and robustness of assessing hair loss severity still remain a challenge and barrier for transitioning such a technique into practice. The presented work proposes an efficient and accurate algorithm to classify hair follicles and estimate hair loss severity, which was implemented and validated using a multitask deep learning method via a Mask R-CNN framework. A microscopic image of the scalp was resized, augmented, then processed through pre-trained ResNet models for feature extraction. The key features considered in this study concerning hair loss severity include the number of hair follicles, the thickness of the hair, and the number of hairs in each hair follicle. Based on these key features, labeling of hair follicles (healthy, normal, and severe) were performed on the images collected from 10 men in varying stages of hair loss. More specifically, Mask R-CNN was applied for instance segmentation of the hair follicle region and to classify the hair follicle state into three categories, following the labeling convention (healthy, normal and severe). Based on the state of each hair follicle captured from a single image, an estimation of hair loss severity was determined for that particular region of the scalp, namely local hair loss severity index (P), and by combining P of multiple images taken and processed from different parts of the scalp, we constructed the hair loss severity estimation (Pavg) and visualized in a heatmap to illustrate the overall hair loss type and condition. The proposed hair follicle classification and hair loss severity estimation using Mask R-CNN demonstrated a more efficient and accurate algorithm compared to other methods previously used, enhancing the classification accuracy by 4 to 15%. This performance supports its potential for use in clinical settings to enhance the accuracy and efficiency of current hair loss diagnosis and prognosis techniques.
18
Zhang, Chenlong, Jian He, Yu Liang, Zaitian Wang, and Xiaoyang Xie. "A Fusion Framework for Confusion Analysis in Learning Based on EEG Signals." Applied Sciences 13, no. 23 (November 29, 2023): 12832. http://dx.doi.org/10.3390/app132312832.
Abstract:
Human–computer interaction (HCI) plays a significant role in modern education, and emotion recognition is essential in the field of HCI. The potential of emotion recognition in education remains to be explored. Confusion is the primary cognitive emotion during learning and significantly affects student engagement. Recent studies show that electroencephalogram (EEG) signals, obtained through electrodes placed on the scalp, are valuable for studying brain activity and identifying emotions. In this paper, we propose a fusion framework for confusion analysis in learning based on EEG signals, combining feature extraction and temporal self-attention. This framework capitalizes on the strengths of traditional feature extraction and deep-learning techniques, integrating local time-frequency features and global representation capabilities. We acquire localized time-frequency features by partitioning EEG samples into time slices and extracting Power Spectral Density (PSD) features. We introduce the Transformer architecture to capture the comprehensive EEG characteristics and utilize a multi-head self-attention mechanism to extract the global dependencies among the time slices. Subsequently, we employ a classification module based on a fully connected layer to classify confusion emotions accurately. To assess the effectiveness of our method in the educational cognitive domain, we conduct thorough experiments on a public dataset CAL, designed for confusion analysis during the learning process. In both subject-dependent and subject-independent experiments, our method attained an accuracy/F1 score of 90.94%/0.94 and 66.08%/0.65 for the binary classification task and an accuracy/F1 score of 87.59%/0.87 and 41.28%/0.41 for the four-class classification task. It demonstrated superior performance and stronger generalization capabilities than traditional machine learning classifiers and end-to-end methods. The evidence demonstrates that our proposed framework is effective and feasible in recognizing cognitive emotions.
19
Inman, Jared, Farhad Ardeshirpour, and Erin Ostby. "Use of Tissue Expander for Contracted Scarred Saddle Deformity Rhinoplasty." Facial Plastic Surgery 35, no. 01 (December 19, 2018): 068–72. http://dx.doi.org/10.1055/s-0038-1675632.
Abstract:
AbstractThe main purpose of this article is to present an alternative technique for the reconstruction of saddle nose deformity in patients with severely scarred or contracted soft tissue envelopes. In this single surgeon case series, the authors present a two-staged reconstruction performed on four adult patients with saddle nose deformities and contracted soft tissue envelope stemming from a variety of etiologic factors including vasculitis, sarcoidosis, and trauma. Stage one involved placement of a 1 × 4 cm tissue expander along the nasal dorsum through anterior scalp and endonasal incisions. The tissue expander port was positioned under the anterior scalp and injected with saline over 3 weeks in-office. Stage two involved removal of the tissue expander and rhinoplasty using osteocartilaginous rib grafts. All four patients had successful expansion of the contracted soft tissue envelope, creating adequate space for the newly reconstructed nasal framework. One patient developed exposure of the tissue expander through the endonasal incision, which did not lead to any adverse outcome. All patients in this series tolerated expansion well, without complaints of pain or external skin breakdown. The use of soft tissue expanders along the nasal dorsum can successfully expand contracted and scarred soft tissue envelopes prior to reconstructive rhinoplasty. This technique may be an effective alternative to the use of interpolated flaps in treating these patients.
20
Marbountin, E., C. Duchamp, P. Rouland, Y. Léonard, J. Boyer, D. Michallet, M. Catusse, P. Migot, J. M. Vandel, and P. Stahl. "Survey of the Lynx distribution in the French Alps: 2000–2004 population status analysis." Acta Biologica Slovenica 49, no. 1 (July 1, 2006): 19–26. http://dx.doi.org/10.14720/abs.49.1.13530.
Abstract:
Within the SCALP framework, the status of the pan-alpine population of Eurasian Lynx is assessed every 5 years, based on the compilation of national reports and standardized classification of lynx presence signs according to data confidence levels (C1, C2, C3). From2000 to 2004, the French national network of lynx experts collected N= 393 data, out of which 224 (compared to only 69 in 1995–1999) were considered as robust enough to evidence the presence of lynx (C1 = 1%; C2 = 42%; C3 = 57%) and were used for further analysis. A majority of the signs concerned the northern part of the Alps, however, in mostly two regions (Chartreuse/Epine: 34% of the signs; Maurienne: 21%). Other data were more scattered over space, from the Cha- blais region close to Switzerland down to the Haut-Verdon close to the Mercantour mountains. A negative trend was noticed from north to south in proportions of best quality signs(C1+C2), and a positive one in low quality ones – C3 – (c² = 3.56, 1 df, p = 0.06), which could point at some methodological artefacts. Discarding C3 may however be too conservative a strategy to assess the species range and status. Using spatial recurrence and trend over time of all signs available (C1+C2+C3) could, therefore, provide the right balance between being too much versus not enough conservative. – When doing so, the area with lynx signs regularly detected sharply increased between 1996–1998 (100 km²), 1999–2001(250 km²), and 2002–2004 (1195 km²). The latter area is still quite small regarding what is required for a viable large carnivore population.A simple demographic model suggested that even a quite moderate proportion of immigrants (e.g. dispersal inflow from neighbouring coreareas – French Jura or Swiss Alps) could considerably decrease the theoretical demographic extinction risk of such a small population, but stilldepending upon adult survival rates, which also strongly influenced the extinction risk. The factors that may influence this sensitivity analysis(such as habitat connectivity and management of wooded corridors) should be evaluated within the Scalp framework.
21
Kwon, Chan-Young, Jungtae Leem, Da-Woon Kim, Hui-Ju Kwon, Hyun-Seo Park, and Sang-Ho Kim. "Effects of acupuncture on earthquake survivors with major psychiatric disorders and related symptoms: A scoping review of clinical studies." PLOS ONE 18, no. 6 (June 8, 2023): e0286671. http://dx.doi.org/10.1371/journal.pone.0286671.
Abstract:
Background This scoping review aimed to determine the current research status of acupuncture for major psychiatric disorder (MPD) in earthquake survivors. Method We followed the scoping review process described previously. A literature search on 14 electronic databases was conducted from inception to November 29, 2022. Data from the included studies were collected and descriptively analyzed to address our research question. Extracted data were collated, synthesized, and summarized the according to the analytical framework of a scoping review. Result This scoping review included nine clinical studies: four randomized controlled trials (RCTs) and five before–after studies. The most frequent MPD type among the included acupuncture studies was posttraumatic stress disorder (PTSD; 6/9, 66.67%). The most frequent acupuncture type was scalp electro-acupuncture (4/9, 44.44%), followed by manual acupuncture and ear acupressure/ear acupuncture (3/9, 33.33%). Studies using scalp electro-acupuncture all used common acupoints, including GB20, GV20, GV24, and EX-HN1. In general, the treatment period lasted between 4 and 12 weeks. Validated assessment tools for PTSD severity and accompanying symptoms were used for patients with PTSD, while the corresponding evaluation tools were used for patients with other diagnoses or clinical symptoms. Acupuncture-related adverse events were generally mild and temporary, such as mild bleeding and hematoma, and syncope was a rare but potentially serious adverse event (1/48 patients and 1/864 sessions over a treatment period of 4 weeks). Conclusion Acupuncture studies for MPD after an earthquake mainly focused on PTSD. RCTs accounted for around half of the included studies. Scalp electro-acupuncture was the most common acupuncture type, and EX-HN1 and GV24 were the most important acupoints in the acupuncture procedures for MPD. The included studies mostly used validated symptom assessment tools, though some did not. Clinical studies in this field need to be further expanded regardless of the study type. Protocol registration https://osf.io/wfru7/.
22
Yang, Zhixian, Yinghua Wang, and Gaoxiang Ouyang. "Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/140863.
Abstract:
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.
23
Alhourani, Ahmad, Michael M. McDowell, Michael J. Randazzo, Thomas A. Wozny, Efstathios D. Kondylis, Witold J. Lipski, Sarah Beck, Jordan F. Karp, Avniel S. Ghuman, and R. Mark Richardson. "Network effects of deep brain stimulation." Journal of Neurophysiology 114, no. 4 (October 2015): 2105–17. http://dx.doi.org/10.1152/jn.00275.2015.
Abstract:
The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies.
24
Kishi, Akifumi, Fumiharu Togo, Toru Nakamura, Yoshiharu Yamamoto, and Ikuhiro Yamaguchi. "A Robust Method with High Time Resolution for Estimating the Cortico-Thalamo-Cortical Loop Strength and the Delay when Using a Scalp Electroencephalography Applied to the Wake-Sleep Transition." Methods of Information in Medicine 57, no. 03 (May 2018): 122–28. http://dx.doi.org/10.3414/me17-01-0151.
Abstract:
Summary Objectives: This study aimed to describe a robust method with high time resolution for estimating the cortico-thalamo-cortical (CTC) loop strength and the delay when using a scalp electroencephalography (EEG) and to illustrate its applicability for analyzing the wake-sleep transition. Methods: The basic framework for the proposed method is the parallel use of a physiological model and a parametric phenomenological model: a neural field theory (NFT) of the corticothalamic system and an autoregressive (AR) model. The AR model is a “stochastic” model that shortens the time taken to extract spectral features and is also a “linear” model that is free from the local-minimum problem. From the relationship between the transfer function of the AR model and the transfer function of the NFT in the low frequency limit, we successfully derived a direct expression of CTC loop strength and the loop delay using AR coefficients. Results: Using this method to analyze sleep-EEG data, we were able to clearly track the wake-to-sleep transition, as the estimated CTC loop strength (c 2) decreased to almost zero. We also found that the c 2-distribution during nocturnal sleep is clearly bimodal in nature, which can be well approximated by the superposition of two Gaussian distributions that correspond to sleep and wake states, respectively. The estimated loop delay distributed ∼0.08 s, which agrees well with the previously reported value estimated by other methods, confirming the validity of our method. Conclusions: A robust method with high time resolution was developed for estimating the cortico-thalamo-cortical loop strength and the delay when using a scalp electroencephalography. This method can contribute not only to detecting the wake-sleep transition, but also to further understanding of the transition, where the cortico-thalamo-cortical loop is thought to play an important role.
25
Gorur, Kutlucan. "Fourier Synchrosqueezing Transform-ICA-EMD Framework Based EOG-Biometric Sustainable and Continuous Authentication via Voluntary Eye Blinking Activities." Biomimetics 8, no. 4 (August 18, 2023): 378. http://dx.doi.org/10.3390/biomimetics8040378.
Abstract:
In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC/Accuracy: ≥99.99% and AUC/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (≥98%) and low FAR (false acceptance rate) scores (≤3.33%) in seven individuals. On the other hand, authentication and monitoring for online users/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users/students.
26
Vallarino, Elisabetta, Alberto Sorrentino, Michele Piana, and Sara Sommariva. "The Role of Spectral Complexity in Connectivity Estimation." Axioms 10, no. 1 (March 16, 2021): 35. http://dx.doi.org/10.3390/axioms10010035.
Abstract:
The study of functional connectivity from magnetoecenphalographic (MEG) data consists of quantifying the statistical dependencies among time series describing the activity of different neural sources from the magnetic field recorded outside the scalp. This problem can be addressed by utilizing connectivity measures whose computation in the frequency domain often relies on the evaluation of the cross-power spectrum of the neural time series estimated by solving the MEG inverse problem. Recent studies have focused on the optimal determination of the cross-power spectrum in the framework of regularization theory for ill-posed inverse problems, providing indications that, rather surprisingly, the regularization process that leads to the optimal estimate of the neural activity does not lead to the optimal estimate of the corresponding functional connectivity. Along these lines, the present paper utilizes synthetic time series simulating the neural activity recorded by an MEG device to show that the regularization of the cross-power spectrum is significantly correlated with the signal-to-noise ratio of the measurements and that, as a consequence, this regularization correspondingly depends on the spectral complexity of the neural activity.
27
Tosoni, Annalisa, Emanuele Cosimo Altomare, Marcella Brunetti, Pierpaolo Croce, Filippo Zappasodi, and Giorgia Committeri. "Sensory-Motor Modulations of EEG Event-Related Potentials Reflect Walking-Related Macro-Affordances." Brain Sciences 11, no. 11 (November 13, 2021): 1506. http://dx.doi.org/10.3390/brainsci11111506.
Abstract:
One fundamental principle of the brain functional organization is the elaboration of sensory information for the specification of action plans that are most appropriate for interaction with the environment. Using an incidental go/no-go priming paradigm, we have previously shown a facilitation effect for the execution of a walking-related action in response to far vs. near objects/locations in the extrapersonal space, and this effect has been called “macro-affordance” to reflect the role of locomotion in the coverage of extrapersonal distance. Here, we investigated the neurophysiological underpinnings of such an effect by recording scalp electroencephalography (EEG) from 30 human participants during the same paradigm. The results of a whole-brain analysis indicated a significant modulation of the event-related potentials (ERPs) both during prime and target stimulus presentation. Specifically, consistent with a mechanism of action anticipation and automatic activation of affordances, a stronger ERP was observed in response to prime images framing the environment from a far vs. near distance, and this modulation was localized in dorso-medial motor regions. In addition, an inversion of polarity for far vs. near conditions was observed during the subsequent target period in dorso-medial parietal regions associated with spatially directed foot-related actions. These findings were interpreted within the framework of embodied models of brain functioning as arising from a mechanism of motor-anticipation and subsequent prediction error which was guided by the preferential affordance relationship between the distant large-scale environment and locomotion. More in general, our findings reveal a sensory-motor mechanism for the processing of walking-related environmental affordances.
28
Bertollo, Maurizio, Selenia di Fronso, Edson Filho, Silvia Conforto, Maurizio Schmid, Laura Bortoli, Silvia Comani, and Claudio Robazza. "Proficient brain for optimal performance: the MAP model perspective." PeerJ 4 (May 25, 2016): e2082. http://dx.doi.org/10.7717/peerj.2082.
Abstract:
Background.The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances.Methods.Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged.Results.We found ERS to be mainly associated with optimal-automatic performance, in agreement with the “neural efficiency hypothesis.” We also observed more ERD as related to optimal-controlled performance in conditions of “neural adaptability” and proficient use of cortical resources.Discussion.These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback techniques.
29
Scheffler, Aaron, Donatello Telesca, Qian Li, Catherine A. Sugar, Charlotte Distefano, Shafali Jeste, and Damla Şentürk. "Hybrid principal components analysis for region-referenced longitudinal functional EEG data." Biostatistics 21, no. 1 (August 3, 2018): 139–57. http://dx.doi.org/10.1093/biostatistics/kxy034.
Abstract:
Summary Electroencephalography (EEG) data possess a complex structure that includes regional, functional, and longitudinal dimensions. Our motivating example is a word segmentation paradigm in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. For each subject, continuous EEG signals recorded at each electrode were divided into one-second segments and projected into the frequency domain via fast Fourier transform. Following a spectral principal components analysis, the resulting data consist of region-referenced principal power indexed regionally by scalp location, functionally across frequencies, and longitudinally by one-second segments. Standard EEG power analyses often collapse information across the longitudinal and functional dimensions by averaging power across segments and concentrating on specific frequency bands. We propose a hybrid principal components analysis for region-referenced longitudinal functional EEG data, which utilizes both vector and functional principal components analyses and does not collapse information along any of the three dimensions of the data. The proposed decomposition only assumes weak separability of the higher-dimensional covariance process and utilizes a product of one dimensional eigenvectors and eigenfunctions, obtained from the regional, functional, and longitudinal marginal covariances, to represent the observed data, providing a computationally feasible non-parametric approach. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group level inference, both geared towards sparse data applications. Analysis of the data from the word segmentation paradigm leads to valuable insights about group-region differences among the TD and verbal and minimally verbal children with ASD. Finite sample properties of the proposed estimation framework and bootstrap inference procedure are further studied via extensive simulations.
30
Gonzalez-Gadea, Maria Luz, Srivas Chennu, Tristan A. Bekinschtein, Alexia Rattazzi, Ana Beraudi, Paula Tripicchio, Beatriz Moyano, et al. "Predictive coding in autism spectrum disorder and attention deficit hyperactivity disorder." Journal of Neurophysiology 114, no. 5 (November 2015): 2625–36. http://dx.doi.org/10.1152/jn.00543.2015.
Abstract:
Predictive coding has been proposed as a framework to understand neural processes in neuropsychiatric disorders. We used this approach to describe mechanisms responsible for attentional abnormalities in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). We monitored brain dynamics of 59 children (8–15 yr old) who had ASD or ADHD or who were control participants via high-density electroencephalography. We performed analysis at the scalp and source-space levels while participants listened to standard and deviant tone sequences. Through task instructions, we manipulated top-down expectation by presenting expected and unexpected deviant sequences. Children with ASD showed reduced superior frontal cortex (FC) responses to unexpected events but increased dorsolateral prefrontal cortex (PFC) activation to expected events. In contrast, children with ADHD exhibited reduced cortical responses in superior FC to expected events but strong PFC activation to unexpected events. Moreover, neural abnormalities were associated with specific control mechanisms, namely, inhibitory control in ASD and set-shifting in ADHD. Based on the predictive coding account, top-down expectation abnormalities could be attributed to a disproportionate reliance (precision) allocated to prior beliefs in ASD and to sensory input in ADHD.
31
Gaho, Anwar Ali, Ahmed Muddassir Khan, Sayed Hyder Abbas Musavi, Muhammad Abul Hasan, and Muhammad Shafiq. "MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain." Pakistan Journal of Engineering and Technology 5, no. 2 (September 19, 2022): 204–10. http://dx.doi.org/10.51846/vol5iss2pp204-210.
Abstract:
This paper is focused on optimizing EEG source localization of visual neural activities generated in the posterior lobe of the human brain. The visual and neural systems of the human brain process the captured images of faces or scenes into optical and chemical neurons in order to produce electrical potentials over the scalp surface, where EEG electrodes measure these signals to sense the underneath visual brain activity. However, it is categorically hard to localize the true neural sources in the human brain's visual cortex due to overlapping and interaction of other active areas of the lobes of the brain. Thus, a novel algorithm of MSP inversion-based Bayesian framework with varying patches for providing the optimal free energy and minimum location in the visual cortex is proposed to address this issue. This algorithm is integrated with a synthetic EEG dataset generation scheme to validate active neural sources. This proposed algorithm provides satisfactory results in terms of optimal free energy with minimum localization error and validates true active sources in the brain. The SPM12 Toolbox is applied in processing the visual EEG dataset in this research. The application of this proposed algorithm is beneficial in terms of localizing the optimum visual sources and identifying the visual disorders or diseases in the human brain.
32
Monaco, Alfonso, Antonio Lacalamita, Nicola Amoroso, Armando D’Orta, Andrea Del Buono, Francesco di Tuoro, Sabina Tangaro, Aldo Innocente Galeandro, and Roberto Bellotti. "Random Forests Highlight the Combined Effect of Environmental Heavy Metals Exposure and Genetic Damages for Cardiovascular Diseases." Applied Sciences 11, no. 18 (September 10, 2021): 8405. http://dx.doi.org/10.3390/app11188405.
Abstract:
Heavy metals are a dangerous source of pollution due to their toxicity, permanence in the environment and chemical nature. It is well known that long-term exposure to heavy metals is related to several chronic degenerative diseases (cardiovascular diseases, neoplasms, neurodegenerative syndromes, etc.). In this work, we propose a machine learning framework to evaluate the severity of cardiovascular diseases (CVD) from Human scalp hair analysis (HSHA) tests and genetic analysis and identify a small group of these clinical features mostly associated with the CVD risk. Using a private dataset provided by the DD Clinic foundation in Caserta, Italy, we cross-validated the classification performance of a Random Forests model with 90 subjects affected by CVD. The proposed model reached an AUC of 0.78 ± 0.01 on a three class classification problem. The robustness of the predictions was assessed by comparison with different cross-validation schemes and two state-of-the-art classifiers, such as Artificial Neural Network and General Linear Model. Thus, is the first work that studies, through a machine learning approach, the tight link between CVD severity, heavy metal concentrations and SNPs. Then, the selected features appear highly correlated with the CVD phenotype, and they could represent targets for future CVD therapies.
33
Direito, Bruno, César A. Teixeira, Francisco Sales, Miguel Castelo-Branco, and António Dourado. "A Realistic Seizure Prediction Study Based on Multiclass SVM." International Journal of Neural Systems 27, no. 03 (February 27, 2017): 1750006. http://dx.doi.org/10.1142/s012906571750006x.
Abstract:
A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.
34
Sengupta, Ranit, and Sazzad M. Nasir. "Redistribution of neural phase coherence reflects establishment of feedforward map in speech motor adaptation." Journal of Neurophysiology 113, no. 7 (April 2015): 2471–79. http://dx.doi.org/10.1152/jn.00731.2014.
Abstract:
Despite recent progress in our understanding of sensorimotor integration in speech learning, a comprehensive framework to investigate its neural basis is lacking at behaviorally relevant timescales. Structural and functional imaging studies in humans have helped us identify brain networks that support speech but fail to capture the precise spatiotemporal coordination within the networks that takes place during speech learning. Here we use neuronal oscillations to investigate interactions within speech motor networks in a paradigm of speech motor adaptation under altered feedback with continuous recording of EEG in which subjects adapted to the real-time auditory perturbation of a target vowel sound. As subjects adapted to the task, concurrent changes were observed in the theta-gamma phase coherence during speech planning at several distinct scalp regions that is consistent with the establishment of a feedforward map. In particular, there was an increase in coherence over the central region and a decrease over the fronto-temporal regions, revealing a redistribution of coherence over an interacting network of brain regions that could be a general feature of error-based motor learning in general. Our findings have implications for understanding the neural basis of speech motor learning and could elucidate how transient breakdown of neuronal communication within speech networks relates to speech disorders.
35
Pei, Alexander, and Barbara G. Shinn-Cunningham. "Closed-Loop Current Stimulation Feedback Control of a Neural Mass Model Using Reservoir Computing." Applied Sciences 13, no. 3 (January 18, 2023): 1279. http://dx.doi.org/10.3390/app13031279.
Abstract:
Transcranial electrical stimulation (tES) is a non-invasive neuromodulatory technique that alters ongoing neural dynamics by injecting an exogenous electrical current through the scalp. Although tES protocols are becoming more common in both clinical and experimental settings, the neurophysiological mechanisms through which tES modulates cortical dynamics are unknown. Most existing tES protocols ignore the potential effect of phasic interactions between endogenous and exogenous currents by stimulating in an open-looped fashion. To better understand the mechanisms of closed-loop tES, we first instantiated a two-column Jansen and Rit model to simulate neuronal dynamics of pyramidal cells and interneurons. An echo-state network (ESN) reservoir computer inverted the dynamics of the model without access to the internal state equations. After inverting the model dynamics, the ESN was used as a closed-loop feedback controller for the neural mass model by predicting the current stimulation input for a desired future output. The ESN was used to predict the endogenous membrane currents of the model from the observable pyramidal cell membrane potentials and then inject current stimulation to destructively interfere with endogenous membrane currents, thereby reducing the energy of the PCs. This simulation approach provides a framework for a model-free closed-loop feedback controller in tES experiments.
36
Almeida, Fernando, and Eduardo Espinheira. "Large-Scale Agile Frameworks: A Comparative Review." Journal of Applied Sciences, Management and Engineering Technology 2, no. 1 (March 31, 2021): 16–29. http://dx.doi.org/10.31284/j.jasmet.2021.v2i1.1832.
Abstract:
This study aims to identify and systematically compare the main large-scale agile frameworks that companies can adopt to manage the work of large-scale and distributed teams. Through this, companies can more consciously perform a better-informed decision on the choice of the framework that best fits the practices and challenges of their organizations. This work employs a qualitative approach supported by an exploratory analysis that identifies and explores the processes of migration to a large-scale agile. In the first phase, fifteen assessment criteria for scaling agile are discussed. In a second phase, these criteria are used to perform a comparative analysis of six large-scale agile frameworks (i.e., DAD, LeSS, Nexus, SAFe, Scrum at Scale, and Spotify). The findings reveal there isn't a dominant large-scale agile framework in all dimensions. However, it is possible to identify frameworks like Nexus and Spotify that target smaller teams and offer low technical complexity. These frameworks easily accommodate changes, while there are other frameworks like SAFe and DAD that offer high levels of scalability but require more demanding and deep efforts in changing work processes in an organization.
37
Tsipoura, Xanthi, Spyros Sakellariou, Michail Glykas, and Athina Tsimpidou. "SPORT MANAGEMENT MATURITY ASSESSMENT: APPLICATION TO COSMA COMPETENCIES SCALE." Balkans Journal of Emerging Trends in Social Sciences 5, no. 2 (December 30, 2022): 125–42. http://dx.doi.org/10.31410/balkans.jetss.2022.5.2.125-142.
Abstract:
The results of a literature survey on the application of management science fields to sport management are presented. The most prominent Critical Success Factors and Enablers for their achievement, identified in the SM maturity assessment frameworks are specified. Existing published work in sport management per critical success factor and enabler category is also presented thus providing a theoretical basis for their significance in the sport management field. A novel holistic sports management maturity assessment framework is proposed. The framework is based on and includes, a ten-by-ten matrix, the most prominent critical success factors and enablers identified in the literature survey. The proposed framework is then applied to the most known sport management competencies accreditation framework called COSMA. The result of this application is a proposed novel “tracking matrix”. Both the proposed maturity frameworks and the tracking matrix can be used by both academics and practitioners in the SM field.
38
George, Marie St, Suzanne Mannes, and James E. Hoffman. "Individual Differences in Inference Generation: An ERP Analysis." Journal of Cognitive Neuroscience 9, no. 6 (November 1997): 776–87. http://dx.doi.org/10.1162/jocn.1997.9.6.776.
Abstract:
Readers routinely draw inferences with remarkable efficiency and seemingly little cognitive effort. The present study was designed to explore different types of inferences during the course of reading, and the potential effects of differing levels of working memory capacity on the likelihood that inferences would be made. The electroencephalogram (EEG) was recorded from five scalp sites while participants read 90 paragraphs, composed of 60 experimental paragraphs and 30 filler paragraphs. Each experimental paragraph was four sentences long, and the final sentence stated explicitly the inference that readers did or did not make. There were four types of experimental paragraphs: (1) Bridging inference, (2) Elaborative inference, (3) Word-Based Priming control, and (4) No Inference control. Participants were tested using the Daneman and Carpenter (1980) Reading Span Task and categorized as having low or high working memory capacity. The average peaks of the N400 component of the event-related brain potential (EM) were used as a measure of semantic priming and integration, such that the lower the N400 was in response to the explicitly stated inference concept, the more likely it was that the reader made the inference. Results indicate that readers with high working memory capacity made both bridging (necessary) and elaborative (optional) inferences during reading, whereas readers with low working memory capacity made only bridging inferences during reading. We interpret the findings within the framework of the Capacity Constrained Comprehension model of Just and Carpenter (1992).
39
Shevchenko, R. S., А. Yu Doroshenko, and O. A. Yatsenko. "Embedding a family of logic languages with custom monadic unification in Scala." PROBLEMS IN PROGRAMMING, no. 1 (January 2024): 03–11. http://dx.doi.org/10.15407/pp2024.01.003.
Abstract:
The paper proposes a framework for embedding logic programming and constraint programming methods in Scala by building a logical object-oriented language around the unification of typed logic based on monads. Two types of API are considered — high-level for language embeddings and low-level for organization of the bidirectional flow of data during the execution of logic programs. Differences in the capabilities of logical mechanisms can be expressed as subclasses of the class of unification monad types. This design makes it possible to share the implementation of custom unification between different frameworks and to use other languages’ embeddings in Scala from the declarative side. The monadic API provides the application developer with a simple and intuitive tool to implement custom logic within the unification. Our frameworks provide a clear representation of logical deduction: Scala code is only used for ad hoc unification. But the overall goal execution is an external interpretation that can implement different strategies. This design provides modularity and good integration with the rest of the ecosystem.
40
He, Zhong Hai, Xiang Zhang, and Xiang Yin Zhu. "Design and Implementation of Automation Testing Framework Based on Keyword Driven." Applied Mechanics and Materials 602-605 (August 2014): 2142–46. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2142.
Abstract:
For the purpose of settling problems in the present automated testing frameworks, the paper presents an automated testing framework based on keyword driven technology. At first, it summarized and analyzed the recent automated testing frameworks; and then it proposed the framework’s system architecture, and also presented the key technology details of the framework. At last, this paper compared this paper’s framework with the recent frameworks by the IP phone, which proved that this framework had superiority in reducing the scale of test scripts, raising the overall efficiency of testing and so on.
41
von Larcher, Thomas, Andrea Beck, Rupert Klein, Illia Horenko, Philipp Metzner, Matthias Waidmann, Dimitri Igdalov, Gregor Gassner, and Claus-Dieter Munz. "Towards a Framework for the Stochastic Modelling of Subgrid Scale Fluxes for Large Eddy Simulation." Meteorologische Zeitschrift 24, no. 3 (July 16, 2015): 313–42. http://dx.doi.org/10.1127/metz/2015/0581.
42
de Haan, Peter, Mahmoud Efatmaneshnik, and Ady James. "Revisiting Large Scale Defence Acquisition: Uncertainty and Acquisition Responsiveness." INCOSE International Symposium 33, S1 (December 2023): 183–98. http://dx.doi.org/10.1002/iis2.13123.
Abstract:
AbstractThis article re‐examines the model for large‐scale defence acquisition, aiming to establish a more adaptable framework that can effectively navigate the uncertainties of a dynamic and evolving operational environment. Drawing upon principles derived from Beer's Viable System Model (VSM), coupled with software engineering concepts from DevOps and innovation frameworks, the article proposes the development of a preliminary acquisition framework. The primary objective of this endeavour is to refine the framework, facilitating the creation of naval platforms that possess enhanced future‐proofing capabilities. Simultaneously, the aim is to minimize the time gap between significant acquisition decisions and achieving initial operating capability, all while upholding the fundamental requisites of the prevailing acquisition frameworks. Considering the extended operational lifespan of valuable assets, which can exceed two decades, there is a discernible risk that naval resources might become obsolete due to technological advancements driven by evolving adversarial threats. To counteract this potential scenario, it becomes imperative to regard naval acquisition as a continuous process, necessitating ongoing evolution and timely updates that align with the swiftly changing environmental conditions. This article asserts the significance of embracing a future‐proofing mindset. Such an approach holds the potential to curtail expenses associated with the swift and cost‐effective implementation of new capabilities as we advance into the future.
43
Liu, Zhetong, Qiugang Zhan, Xiurui Xie, Bingchao Wang, and Guisong Liu. "Federal SNN Distillation: A Low-Communication-Cost Federated Learning Framework for Spiking Neural Networks." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012078. http://dx.doi.org/10.1088/1742-6596/2216/1/012078.
Abstract:
Abstract In recent years, research on the federated spiking neural network (SNN) framework has attracted increasing attention in the area of on-chip learning for embedded devices, because of its advantages of low power consumption and privacy security. Most of the existing federated SNN frameworks are based on the classical federated learning framework -- Federated Average (FedAvg) framework, where internal communication is achieved by exchanging network parameters or gradients. However, although these frameworks take a series of methods to reduce the communication cost, the communication of frameworks still increases with the scale of the backbone network. To solve the problem, we propose a new federated SNN framework, Federal SNN distillation (FSD), whose communication is independent of the scale of the network. Through the idea of knowledge distillation, FSD replaces the network parameters or gradients with the output spikes of SNN, which greatly reduces the communication while ensuring the effect. In addition, we propose a lossless compression algorithm to further compress the binary output spikes of SNN. The proposed framework FSD is compared with the existing FedAvg frameworks on MNIST, Fashion MNIST and CIFAR10 datasets. The experiment results demonstrate that FSD communication is decreased by 1-2 orders of magnitude when reaching the same accuracy.
44
Obukhov, Yury Vladimirovich, Ivan Andreevich Kershner, Renata Alekseevna Tolmacheva, Mikhail Vladimirovich Sinkin, and Ludmila Alekseevna Zhavoronkova. "Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury." Sensors 21, no. 18 (September 7, 2021): 5989. http://dx.doi.org/10.3390/s21185989.
Abstract:
Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for example, after traumatic brain injury, the destruction of interneuronal connections occurs, which leads to a violation of interchannel synchronization when performing motor or cognitive tests. Within the framework of a unified approach to the analysis of interchannel EEG synchronization using the ridges of wavelet spectra, two problems were solved. First, the segmentation of the initial data of long-term monitoring of scalp EEG with various artifacts into fragments suspicious of epileptic seizures in order to reduce the total duration of the fragments analyzed by the doctor. Second, assessments of recovery after rehabilitation of cognitive functions in patients with moderate traumatic brain injury. In the first task, the initial EEG was segmented into fragments in which at least two channels were synchronized, and by the adaptive threshold method into fragments with a high value of the EEG power spectral density. Overlapping in time synchronized fragments with fragments of high spectral power density was determined. As a result, the total duration of the fragments for analysis by the doctor was reduced by more than 60 times. In the second task, the network of phase-related EEG channels was determined during the cognitive test before and after rehabilitation. Calculation-logical and spatial-pattern cognitive tests were used. The positive dynamics of rehabilitation was determined during the initialization of interhemispheric connections and connections in the frontal cortex of the brain.
45
Marhl, Urban, Anna Jodko-Władzińska, Rüdiger Brühl, Tilmann Sander, and Vojko Jazbinšek. "Transforming and comparing data between standard SQUID and OPM-MEG systems." PLOS ONE 17, no. 1 (January 19, 2022): e0262669. http://dx.doi.org/10.1371/journal.pone.0262669.
Abstract:
Optically pumped magnetometers (OPMs) have recently become so sensitive that they are suitable for use in magnetoencephalography (MEG). These sensors solve operational problems of the current standard MEG, where superconducting quantum interference device (SQUID) gradiometers and magnetometers are being used. The main advantage of OPMs is that they do not require cryogenics for cooling. Therefore, they can be placed closer to the scalp and are much easier to use. Here, we measured auditory evoked fields (AEFs) with both SQUID- and OPM-based MEG systems for a group of subjects to better understand the usage of a limited sensor count OPM-MEG. We present a theoretical framework that transforms the within subject data and equivalent simulation data from one MEG system to the other. This approach works on the principle of solving the inverse problem with one system, and then using the forward model to calculate the magnetic fields expected for the other system. For the source reconstruction, we used a minimum norm estimate (MNE) of the current distribution. Two different volume conductor models were compared: the homogeneous conducting sphere and the three-shell model of the head. The transformation results are characterized by a relative error and cross-correlation between the measured and the estimated magnetic field maps of the AEFs. The results for both models are encouraging. Since some commercial OPMs measure multiple components of the magnetic field simultaneously, we additionally analyzed the effect of tangential field components. Overall, our dual-axis OPM-MEG with 15 sensors yields similar information to a 62-channel SQUID-MEG with its field of view restricted to the right hemisphere.
46
Sun, Chengfa, Hui Cui, Weidong Zhou, Weiwei Nie, Xiuying Wang, and Qi Yuan. "Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning." International Journal of Neural Systems 29, no. 10 (December 2019): 1950021. http://dx.doi.org/10.1142/s0129065719500217.
Abstract:
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a [Formula: see text]-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level [Formula: see text]-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.
47
Krischer, Lion, Andreas Fichtner, Saule Zukauskaite, and Heiner Igel. "Large‐Scale Seismic Inversion Framework." Seismological Research Letters 86, no. 4 (June 3, 2015): 1198–207. http://dx.doi.org/10.1785/0220140248.
48
Seok, SangCheol, Sara Mednick, and Paola Malerba. "0112 Classification of reconstructed depth profiles shows Global and non-Global slow oscillations differentiate in the hippocampus and thalamus." Sleep 45, Supplement_1 (May 25, 2022): A50—A51. http://dx.doi.org/10.1093/sleep/zsac079.110.
Abstract:
Abstract Introduction Sleep slow oscillations (SOs, 0.5-1.5 Hz) can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. In this study, we estimate the current density within the brain that generates a Global SO, to evaluate which sub-cortical structures are involved in Global SO dynamics. We then train multiple machine learning algorithms to distinguish between Global SOs and other SO types, and probe variance of Global/non-Global SO profiles within and across subjects.Sleep slow oscillations (SOs, 0.5-1.5 Hz) can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. In this study, we estimate the current density within the brain that generates a Global SO, to evaluate which sub-cortical structures are involved in Global SO dynamics. We then train multiple machine learning algorithms to distinguish between Global SOs and other SO types, and probe variance of Global/non-Global SO profiles within and across subjects. Methods 32 volunteers (18 females) slept in the lab with polysomnography including 24 head EEG channels; their sleep was scored according to AASM criteria. SOs were algorithmically detected at each channel and classified as Global or non-Global using our method (Malerba et al., 2019). The depth profile of each SO was reconstructed with current source estimation (in Brainstorm followed by sLORETA), with a standardized head model including 17 regions. Each depth profile was embedded in a matrix averaging current by region and in three 200ms-long time bins: before, during and after the SO trough. Thirty classifiers were applied to this dataset, leveraging Matlab’s supervised learning application. We compared accuracy within and across subjects and identified best-performing algorithms across dataset size. We then used univariate feature selection to quantify the relevance of each region-time pair to successful classification. Results Global/non-Global SOs current depth profiles have higher variance across subjects, and accuracy improves when data is sampled between rather than within individuals. Ensemble subspace methods reached highest accuracy (98.5%). Feature selectivity identified cortical, hippocampal, and thalamic currents at the trough of the SO as the most relevant for Global/non-Global SO classifications. Conclusion We introduce an analytical framework enabling the study of SO depth profiles, including their time evolution, as matrices. The predominant differentiation of Global/non-Global SOs in cortical, hippocampal, and thalamic currents supports the potential functional difference of these SO types. Support (If Any) NIH grant (R01 AG046646) to S.C.M.
49
Sharma, Praval, and Dr Imran. "Integration of Distributed Generating Systems for Non-Linear Loads." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 23, 2021): 1525–45. http://dx.doi.org/10.51201/jusst/21/06459.
Abstract:
The independent small-scale networks including sustainable power sources have been used in remote regions around the globe. Nonetheless, the irregularity of vitality sources may cause an enormous variance of the miniaturized scale framework recurrence. Because of consistently expanding vitality utilization, rising open familiarity with ecological assurance, and relentless advancement in power deregulation, distributed generation (DG) frameworks have pulled in expanded intrigue. Wind and photovoltaic (PV) power age are two of the most encouraging sustainable power source advancements. Fuel cell (FC) frameworks likewise show incredible potential in DG utilizations of things to come because of their quick innovation improvement and numerous benefits they have, for example, high effectiveness, zero or low outflow (of contamination gases), and adaptable measured structure. In proposition investigated work Integration of Distributed Generating Systems for Non-straight Loads will be proposed. A run-of-the-mill wind-PV-diesel reconciliation which comprises of diesel generator, PV framework, wind turbine generator (WTG), BESS, and burden, is utilized for the proposed models and controllers. We reenact and Integration Distributed Generating Systems for Non-straight Loads on the MATLAB/SIMULINK and portions of coordinated vitality frameworks are analyzed. The coordinated PV framework is normally controlled to work in the maximum power point tracking (MPPT) mode. The battery vitality stockpiling framework is worked inconsistent force charging or releasing mode. So as to give an incorporated vitality framework associated with lattice relying upon singular vitality necessities, the Integrated Energy Systems can be extra to a current vitality source to lessen petroleum product utilization or an independent for complete non-renewable energy source uprooting Through the broad joining of vitality foundations it is conceivable to upgrade the supportability, adaptability, steadiness, and productivity of the general vitality framework. The reproduction of incorporated vitality frameworks is done in MATLAB/SIMULINK. And all framework results will be done by Matlab reproduction is proposed for disconnected smaller scale matrices with sustainable sources. In the exhibited method, the pitch point controller is intended for wind turbine generator (WTG) framework to smooth breeze power yield. The proposed procedure is tried in a regular secluded incorporated small-scale network with both PV and wind turbine generators.
50
Khoja, Ahmed, Andrea Moro, and Natalie Essig. "iQRe: An Integrated Cross Scale Urban Resilience Assessment framework." IOP Conference Series: Earth and Environmental Science 1122, no. 1 (December 1, 2022): 012015. http://dx.doi.org/10.1088/1755-1315/1122/1/012015.
Abstract:
Abstract Developing successful urban resilience plans is a very challenging task due to the complex cross scale, multi sectoral dynamics of urban areas. This paper presents a novel integrated, multi sectoral, cross scale urban resilience assessment framework (iQRe) that facilitates bridging the spatial and sectoral gaps in the existing climate change adaption frameworks. The iQRe framework merges the IPCC AR5 risk assessment approach with the generic multi-criteria analysis methodology “SB Method” developed by iiSBE, creating a series of agile and quantitative climate impact chains that allow assigning a normalized numerical value to each of the 3 components needed to assess the climate risk (Hazard, vulnerability, and exposure). The Paper provide a sample application of the iQRe on a residential building.