scholarly journals Smartphone Household Wireless Electroencephalogram Hat

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Harold Szu ◽  
Charles Hsu ◽  
Gyu Moon ◽  
Takeshi Yamakawa ◽  
Binh Q. Tran ◽  
...  
Keyword(s):  
Blind Deconvolution ◽  
Impulse Response Function ◽  
High Density ◽  
Gaming Industry ◽  
Dry Electrodes ◽  
Eeg Data ◽  
Close Proximity ◽  
Ill Posed ◽  
Parallel Frame ◽  
Future Work

Rudimentarybrain machine interfacehas existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate.Spatial sparsenessis addressed by close proximity between active electrodes and desired source locations and using an adaptive selection ofNactive among10Npassive electrodes to formm-organized random linear combinations of readouts,m≪N≪10N.Temporal sparsenessis addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences).

scholarly journals Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment

2020 ◽  
Vol 20 (S12) ◽  
Author(s):  
Juan C. Mier ◽  
Yejin Kim ◽  
Xiaoqian Jiang ◽  
Guo-Qiang Zhang ◽  
Samden Lhatoo
Keyword(s):  
Sensitivity Analysis ◽  
Feature Extraction ◽  
Random Forest ◽  
Window Size ◽  
Extraction Process ◽  
Unexpected Death ◽  
Power Spectral ◽  
Eeg Data ◽  
Boosted Decision Trees ◽  
Future Work

Abstract Background Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997). Methods This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011). Results The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. Conclusion The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

scholarly journals Determination of the excitation origin in the ventricles from the ECG using support vector regression

2017 ◽  
Vol 3 (2) ◽  
pp. 257-260
Author(s):  
Nicolas Pilia ◽  
Christian Ritter ◽  
Danila Potyagaylo ◽  
Walther H. W. Schulze ◽  
Olaf Dössel ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Functional Relation ◽  
Support Vector ◽  
Transmembrane Potentials ◽  
Surface Ecg ◽  
Machine Learning Approach ◽  
Ill Posed ◽  
Ectopic Beats ◽  
Forward Calculation ◽  
Future Work

AbstractA common treatment of focal ventricular tachycardia is the catheter ablation of triggering sites. They have to be found manually by the physician during an intervention in a catheter lab. Thus, a method for determining the position of the focus automatically is desired. The inverse problem of electrocardiography addresses this problem by reconstructing the source of the ectopic beats using the surface ECG. This problem is ill-posed and therefore needs specific methods for solving it. We propose a machine learning approach for localisation of the ectopic foci in the heart to assist cardiologists with their therapy planning.We simulated 600 120-lead ECGs with different known excitation origins in the heart using a cellular automaton followed by a forward calculation. Features from the ECGs were used as input for a support vector regression (SVR). We assumed a functional relation between features from the ECG and the excitation origin. To benchmark SVR, we also used the well-known Tikhonov 0th order regularisation to reconstruct the transmembrane potentials in the heart and detect the location of the ectopic foci. Parameters for SVR and regularisation were chosen using a grid search minimising the error between estimated and true excitation origin. Compared to the Tikhonov regularisation method, SVR achieved a smaller deviation between estimated and real excitation origin evaluated with 6-fold cross validation. Future work could investigate on the behaviour on data from simulations with other torso and electrophysiological models, the influence of other methods for feature extraction and finally the evaluation with clinical data.

scholarly journals Development of a Smart Helmet for Strategical BCI Applications

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1867 ◽  
Author(s):  
Li-Wei Ko ◽  
Yang Chang ◽  
Pei-Lun Wu ◽  
Heng-An Tzou ◽  
Sheng-Fu Chen ◽  
...  
Keyword(s):  
Data Collection ◽  
Signal To Noise Ratio ◽  
The Novel ◽  
Contact Impedance ◽  
Practical Applications ◽  
Dry Electrodes ◽  
Average Accuracy ◽  
Eeg Data ◽  
Electrophysiological Measurements ◽  
User Acceptability

Conducting electrophysiological measurements from human brain function provides a medium for sending commands and messages to the external world, as known as a brain–computer interface (BCI). In this study, we proposed a smart helmet which integrated the novel hygroscopic sponge electrodes and a combat helmet for BCI applications; with the smart helmet, soldiers can carry out extra tasks according to their intentions, i.e., through BCI techniques. There are several existing BCI methods which are distinct from each other; however, mutual issues exist regarding comfort and user acceptability when utilizing such BCI techniques in practical applications; one of the main challenges is the trade-off between using wet and dry electroencephalographic (EEG) electrodes. Recently, several dry EEG electrodes without the necessity of conductive gel have been developed for EEG data collection. Although the gel was claimed to be unnecessary, high contact impedance and low signal-to-noise ratio of dry EEG electrodes have turned out to be the main limitations. In this study, a smart helmet with novel hygroscopic sponge electrodes is developed and investigated for long-term usage of EEG data collection. The existing electrodes and EEG equipment regarding BCI applications were adopted to examine the proposed electrode. In the impedance test of a variety of electrodes, the sponge electrode showed performance averaging 118 kΩ, which was comparable with the best one among existing dry electrodes, which averaged 123 kΩ. The signals acquired from the sponge electrodes and the classic wet electrodes were analyzed with correlation analysis to study the effectiveness. The results indicated that the signals were similar to each other with an average correlation of 90.03% and 82.56% in two-second and ten-second temporal resolutions, respectively, and 97.18% in frequency responses. Furthermore, by applying the proposed differentiable power algorithm to the system, the average accuracy of 21 subjects can reach 91.11% in the steady-state visually evoked potential (SSVEP)-based BCI application regarding a simulated military mission. To sum up, the smart helmet is capable of assisting the soldiers to execute instructions with SSVEP-based BCI when their hands are not available and is a reliable piece of equipment for strategical applications.

F10. Co-localization of ictal and interictal activity using high density EEG data

2018 ◽  
Vol 129 ◽  
pp. e70
Author(s):  
Walter F. Heine ◽  
Mary-Ann Dobrota ◽  
Rebekah Wigton ◽  
Donald L. Schomer ◽  
Susan T. Herman
Keyword(s):  
High Density ◽  
Eeg Data

scholarly journals Toward a better understanding of task demands, workload, and performance during physician-computer interactions

2016 ◽  
Vol 23 (6) ◽  
pp. 1113-1120 ◽  
Author(s):  
Lukasz M Mazur ◽  
Prithima R Mosaly ◽  
Carlton Moore ◽  
Elizabeth Comitz ◽  
Fei Yu ◽  
...  
Keyword(s):  
Task Demands ◽  
Systematic Assessment ◽  
Quality Metric ◽  
Task Load ◽  
Simulated Environment ◽  
Eeg Data ◽  
And Performance ◽  
Nasa Task Load Index ◽  
Future Work ◽  
Computer Interactions

Abstract Objective To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment. Methods Two experiments were performed in 2 different electronic medical record (EMR) environments: WebCIS ( n = 12) and Epic ( n = 17). Each participant was instructed to complete a set of prespecified tasks on 3 routine clinical EMR-based scenarios: urinary tract infection (UTI), pneumonia (PN), and heart failure (HF). Task demands were quantified using behavioral responses (click and time analysis). At the end of each scenario, subjective workload was measured using the NASA-Task-Load Index (NASA-TLX). Physiological workload was measured using pupillary dilation and electroencephalography (EEG) data collected throughout the scenarios. Performance was quantified based on the maximum severity of omission errors. Results Data analysis indicated that the PN and HF scenarios were significantly more demanding than the UTI scenario for participants using WebCIS ( P < .01), and that the PN scenario was significantly more demanding than the UTI and HF scenarios for participants using Epic ( P < .01). In both experiments, the regression analysis indicated a significant relationship only between task demands and performance ( P < .01). Discussion Results suggest that task demands as experienced by participants are related to participants' performance. Future work may support the notion that task demands could be used as a quality metric that is likely representative of performance, and perhaps patient outcomes. Conclusion The present study is a reasonable next step in a systematic assessment of how task demands and workload are related to performance in EMR-evolving environments.

Density of tobacco advertising around schools

2020 ◽  
Vol 24 (7) ◽  
pp. 674-680
Author(s):  
N. Nurjanah ◽  
Y. M. Manglapy ◽  
S. Handayani ◽  
A. Ahsan ◽  
R. Sutomo ◽  
...  
Keyword(s):  
Smoking Prevalence ◽  
High Density ◽  
Policy Implications ◽  
Adult Males ◽  
Tobacco Advertising ◽  
National Regulation ◽  
Close Proximity ◽  
The World ◽  
Geospatial Analyses

BACKGROUND: Indonesia has the second highest smoking prevalence among adult males in the world, with over 61.4 million current smokers. However, there is no national regulation on outdoor tobacco advertising.OBJECTIVE: >To assess the density of outdoor tobacco advertising around schools in Semarang City, Indonesia.METHODS: We conducted geospatial analyses using buffer and hotspot analyses based on advertising and school data in ArcMap 10.6. We statistically tested the significance of different densities, including between 100 m and 100–300-m buffers from schools using Stata 15.1.RESULTS: We found a total of 3453 tobacco advertisements, of which 3026 (87%) were at least medium in size (1.3 m x l.9 m), and 2556 (74%) were within 300 m of schools. We also found hotspots with a 45% higher density of adverts within 100 m of schools (compared to within 100–300 m). A total of 378 schools (39%) were in these advertising hotspots.CONCLUSION: There was high density of outdoor tobacco advertising, with significant clusters in close proximity to schools in Semarang City. The policy implications of this are discussed.

Hierarchical simulated annealing vs. a Gauss‐Newton scheme applying analytical Jacobians for the solution of a source current distribution problem

2001 ◽  
Vol 20 (2) ◽  
pp. 497-506
Author(s):  
Bernhard Brandstätter ◽  
Christian Magele
Keyword(s):  
Simulated Annealing ◽  
Linear Problem ◽  
Computational Cost ◽  
Search Space ◽  
Quadratic Program ◽  
Source Current ◽  
Ill Posed ◽  
Newton Scheme ◽  
Future Work ◽  
The Magnetic Field

Considers, without loss of generality, a simple linear problem, where in a certain domain the magnetic field, generated by infinitely long conductors, whose locations as well as the currents are unknown, has to meet a certain figure. The problem is solved by applying hierarchical simulated annealing, which iteratively reduces the dimension of the search space to save computational cost. A Gauss‐Newton scheme, making use of analytical Jacobians, preceding a sequential quadratic program (SQP), will be applied as a second approach to tackle this severely ill‐posed problem. The results of these two techniques will be analyzed and discussed and some comments on future work will be given.

scholarly journals High-Density EEG in a Charles Bonnet Syndrome Patient during and without Visual Hallucinations: A Case-Report Study

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1991
Author(s):  
Andrea Piarulli ◽  
Jitka Annen ◽  
Ron Kupers ◽  
Steven Laureys ◽  
Charlotte Martial
Keyword(s):  
Case Report ◽  
Resting State ◽  
High Density ◽  
Alpha Power ◽  
Visual Hallucinations ◽  
Charles Bonnet Syndrome ◽  
Eeg Data ◽  
Signal Diversity ◽  
Signal Complexity ◽  
Cortical Regions

Charles Bonnet syndrome (CBS) is a rare clinical condition characterized by complex visual hallucinations in people with loss of vision. So far, the neurobiological mechanisms underlying the hallucinations remain elusive. This case-report study aims at investigating electrical activity changes in a CBS patient during visual hallucinations, as compared to a resting-state period (without hallucinations). Prior to the EEG, the patient underwent neuropsychological, ophthalmologic, and neurological examinations. Spectral and connectivity, graph analyses and signal diversity were applied to high-density EEG data. Visual hallucinations (as compared to resting-state) were characterized by a significant reduction of power in the frontal areas, paralleled by an increase in the midline posterior regions in delta and theta bands and by an increase of alpha power in the occipital and midline posterior regions. We next observed a reduction of theta connectivity in the frontal and right posterior areas, which at a network level was complemented by a disruption of small-worldness (lower local and global efficiency) and by an increase of network modularity. Finally, we found a higher signal complexity especially when considering the frontal areas in the alpha band. The emergence of hallucinations may stem from these changes in the visual cortex and in core cortical regions encompassing both the default mode and the fronto-parietal attentional networks.

scholarly journals Wavelet deconvolution technique for impact force reconstruction: mutual deconvolution approach

2021 ◽  
Vol 3 (SI2) ◽  
pp. first
Author(s):  
Hai Tran ◽  
Tat-Hien Le
Keyword(s):  
Indirect Method ◽  
Impact Force ◽  
Impulse Response Function ◽  
Reconstruction Process ◽  
Deconvolution Technique ◽  
Force Reconstruction ◽  
History Of ◽  
Ill Posed ◽  
And Shifts ◽  
Large Magnification

In the field of impact engineering, one of the most concerned issues is how to exactly know the history of impact force which often difficult or impossible to be measured directly. In reality, information of impact force apply to structure can be identified by means of indirect method from using information of corresponding output responses measured on structure. Namely, by using the output responses (caused by the unknown impact force) such as acceleration, displacement, or strain, etc. in cooperation with the impulse response function, the profile of unknown impact force can be rebuilt. A such indirect method is well known as impact force reconstruction or impact force deconvolution technique. Unfortunately, a simple deconvolution technique for reconstructing impact force has often encountered difficulty due to the ill-posed nature of inversion. Deconvolution technique thus often results in unexpected reconstruction of impact force with the influences of unavoidable errors which is often magnified to a large value in reconstructed result. This large magnification of errors dominates profile of desired impact force. Although there have been some regularization methods in order to improve this ill-posed problem so far, most of these regularizations are considered in the whole-time domain, and this may make the reconstruction inefficient and inaccurate because impact force is normally limited to some portions of impact duration. This work is concerned with the development of deconvolution technique using wavelets transform. Based on the advantages of wavelets (i.e., localized in time and the possibility to be analyzed at different scales and shifts), the mutual reconstruction process is proposed and formulated by considering different scales of wavelets. The experiment is conducted to verify the proposed technique. Results demonstrated the robustness of the present technique when reconstructing impact force with more stability and higher accuracy.

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