Wearable Sensing’s wireless DSI-7 is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-7 takes on average less than 1 minute to set up, making it the ideal solution for scientists in need of a simple, easy to use, EEG system. Our patented sensor technology not only delivers uncompromised signal quality but also enables our system to be virtually immune against motion and electrical artifacts. As a result, the DSI-7 can be utilized in virtual or augmented reality, while also allowing researchers to take their experiments out of the lab, and into the real world.
The DSI-7 has sensor locations covering the frontal, center, and posterior areas of the brain. There are 7 sensors whose locations can be customized upon order, and 2 earclip sensors. It also has a 4-bit trigger input to synchronize with other devices such as Eye-Tracking, Motion (IMU), and more.
Used around the world by leaders in Research, Neurofeedback, Neuromarketing, Brain-Computer Interfaces, & Neuroergonomics.
With over 90% correlation to research-grade wet EEG systems, the dry sensor interface (DSI) offers unparalleled quality and performance
Multiple adjustment points and a foam pad lined interior enable the system to be worn for up to 8 hours on any head shape or size
All DSI systems include free, unlimited licenses of DSI-Streamer, our data acquisition software which can record raw data, in .csv and .edf file formats
Faraday cage's, spring-loaded electrodes, and our patented common-mode follower technology, provides near immunity against electrical and motion artifacts
Using 70% isopropyl alcohol and a cleaning brush, the DSI-24 only takes a minute to clean, 3 minutes to dry, and can be up and running on the next subject in minutes
All DSI systems include our free C based .dll API, which enables users to pull the raw data directly from the headset, for custom software on Windows, Mac OS, Linux, and ARM
The DSI-7 was designed for ultra-rapid setup, taking on average less than 1 minute to don, and works on any type of hair, including long hair, thick hair, afros, and more
DSI headsets have active sensors, amplifiers, digitizers, batteries, onboard storage, and wireless transmission, making them complete, mobile, wearable EEG systems
DSI systems exclusively work with QStates, a machine learning algorithm for cognitive classification on states such as mental workload, engagement, and fatigue
Our Wireless Trigger Hub simplifies the synchronization of DSI headsets with other devices. It features:
An additional benefit of the Trigger Hub design is that it allows synchronization across multiple data sources that are distributed across multiple systems, each of which running at its own clock rate. One such case commonly experienced in EEG experiments involves the synchronization of EEG and eye-tracking measurements, where the inevitable clock drift that arises between two systems during extended measurements creates difficulty in aligning data to events across the two systems.
The DSI-7 can be customized to have ECG, EMG, EOG, GSR, RESP, & TEMP. To do this, you can choose to either remove an EEG sensor in exchange for an auxiliary sensor, or you can modify the earclips to record ExG. The sensor data is collected and recorded in our data acquisition software, DSI-Streamer, where you can view the EEG and Aux sensors in real-time.
EEG Channels
Can be customized on demand by manufacturer
Reference / Ground
Common Mode Follower / Fz
Head Size Range
Adult Size: 52cm – 62cm circumference
Child Size: 48cm – 54cm circumference
Sampling Rate
300 Hz (600Hz upgrade available)
Bandwidth
0.003 – 150 Hz
A/D resolution
0.317 μV referred to input
Input Impedance (1Hz)
47 GΩ
CMRR
> 120 dB
Amplifier / Digitizer
16 bits / 7 channels
Wireless
Bluetooth
Wireless Range
10 m
Run-time
> 12 Hours
Onboard Storage
~ 68 Hours (available option)
Data Acquisition
Real time, evoked potentials
Signal Quality Monitoring
Continuous impedance, Baseline offset, Noise (1-50 Hz)
Data Type
Raw and Filtered Data available
File Type
.CSV and .EDF
Data Output Streaming
TCP/IP socket, API (C Based), LSL
Cognitive State Classification
Brain Computer Interface
SSVEP BCI Algorithms; BCI2000; OpenViBE; PsychoPy; BCILab
Data Integration / Analysis
CAPTIV; Lab Streaming Layer; NeuroPype; BrainStorm; NeuroVIS
Neurofeedback
Brainmaster Brain Avatar; EEGer
Neuromarketing
CAPTIV Neurolab
Presentation
Presentation; E-Prime
Jang, Dajeong; Kim, Han-Jong; Choi, Kyungah
Enhancing Student Learning in Virtual Classrooms: Effects of Window View Content and Time of Day Journal Article
In: IEEE Access, 2024.
@article{jang2024enhancing,
title = {Enhancing Student Learning in Virtual Classrooms: Effects of Window View Content and Time of Day},
author = {Dajeong Jang and Han-Jong Kim and Kyungah Choi},
doi = {10.1109/ACCESS.2024.3476982},
year = {2024},
date = {2024-10-09},
urldate = {2024-01-01},
journal = {IEEE Access},
publisher = {IEEE},
abstract = {As virtual classrooms, traditional physical classroom environments are transformed into flexible virtual environments, allowing customization of environmental elements to enhance student learning. This study explored the effects of window settings in virtual classrooms on learning experiences of students. Utilizing a within-subjects design, we simulated a virtual classroom environment with seven unique window settings and varied its view content (nature vs. urban) and time of day (daytime, sunset, and night). We also simulated a windowless condition. Thirty-five university students participated in the study and performed subjective evaluations and cognitive tasks. Moreover, their physiological responses were recorded using electroencephalogram measurements. The results indicated that environments with windows increased the perception of spaciousness and promoted a state of relaxed alertness, as evidenced by increased fast alpha brainwave activity. In contrast, settings without windows or with urban views increased the sense of presence. Daytime views positively affected valence, motivation, spaciousness, and concentration, whereas nighttime views were the least preferred. No significant differences were observed in cognitive task performance across the different conditions. These findings underscore the necessity of customizing virtual learning environments to meet individual user needs. By allowing students to adjust their virtual environments, educators and space designers can create more flexible and personalized virtual-reality educational spaces, ultimately improving learning outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kamti, Mukesh Kumar; Iqbal, Rauf; Kakoti, Pallabjyoti
Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions Journal Article
In: Transportation Research Part F: Traffic Psychology and Behaviour, vol. 99, pp. 98–112, 2023.
@article{kamti2023eeg,
title = {Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions},
author = {Mukesh Kumar Kamti and Rauf Iqbal and Pallabjyoti Kakoti},
doi = {https://doi.org/10.1016/j.trf.2023.10.011},
year = {2023},
date = {2023-10-19},
urldate = {2023-01-01},
journal = {Transportation Research Part F: Traffic Psychology and Behaviour},
volume = {99},
pages = {98–112},
publisher = {Elsevier},
abstract = {Driving in diverse and challenging conditions, including inclement weather, poses potential risks to road safety. While previous studies have primarily focused on examining driver behavior and reactions in different weather and road conditions, there is a lack of research on assessing drivers' mental states during such situations, particularly considering the influence of factors such as road complexity, traffic, demographics, and adverse environmental conditions. This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels through simulated driving scenarios encompassing various environments and traffic conditions. A three-wheeler driving simulator was employed, along with the DSI 7 EEG headset and Q states software, to classify and analyze the drivers' mental states. The findings of this study highlight that young novice drivers exhibit higher fluctuations in mental state compared to their mid and high-experienced counterparts. Furthermore, mid-age drivers face an elevated risk of collision due to frequent changes in mental state and attention. Additionally, it was observed that highly skilled drivers display a transition in attention level and mental state between sessions, shifting from a focused to a relaxed state—an aspect absent in inexperienced drivers. These findings enhance our comprehension of the intricate interaction among drivers' emotional states, age, experience, and driving abilities, consequently opening avenues for tailored interventions and training initiatives focused on improving road safety.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Haugland, Mathias Ramm; Borovykh, Anastasia; Tai, Yen; Haar, Shlomi
2023 Conference on Cognitive Computational Neuroscience , Cognitive Computational Neuroscience, 2023.
@conference{hauglandexplainable,
title = {Explainable deep learning for arm classification during deep brain stimulation-towards digital biomarkers for closed-loop stimulation},
author = {Mathias Ramm Haugland and Anastasia Borovykh and Yen Tai and Shlomi Haar},
doi = {10.32470/ccn.2023.1368-0},
year = {2023},
date = {2023-08-24},
booktitle = {2023 Conference on Cognitive Computational Neuroscience
},
pages = {59-61},
publisher = {Cognitive Computational Neuroscience},
abstract = {Deep brain stimulation (DBS) is an effective technique for treating motor symptoms in neurological conditions like Parkinson’s disease and dystonic and essential tremor (DT and ET). The DBS delivery could be improved if reliable biomarkers could be found. We propose a deep learning (DL) framework based on EEGNet to search for digital biomarkers in EEG recordings for discriminating neural response from changes in DBS parameters. Here we present a proof-of-concept by distinguishing left and right arm movement in raw EEG recorded during a DBS programming session of a DT patient. Based on the classification of 1s segments from six-channel EEG, we achieve an average accuracy of up to 93.8%. In addition, we propose a simple, yet effective model-agnostic filtering strategy for explaining the network’s performance, showing which frequency band features it mostly uses to classify the EEG.
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Swerdloff, Margaret M; Hargrove, Levi J
Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task Journal Article
In: 2023.
@article{swerdloffevaluating,
title = {Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task},
author = {Margaret M Swerdloff and Levi J Hargrove},
url = {https://arinex.com.au/EMBC/pdf/full-paper_1084.pdf},
year = {2023},
date = {2023-07-01},
abstract = {The cognitive load of a precisely timed task, such as the Stroop task, may be measured through the use of eventrelated potentials (ERPs). To determine the time at which cognitive load is at its peak, oddball tones may be applied at various times surrounding a cognitive task. However, we need to determine whether the simultaneous presentation of auditory and visual stimuli would mask a potential change in P3 in an ERP-producing task. If the contribution of the Stroop stimulus is too large, then Stroop ERP with oddball stimuli occurring at different timepoints may not be directly comparable across the various timepoints due to the contribution of the Stroop ERP. The aim of this study was to measure the magnitude of the difference wave between that of simultaneously presented stimuli and that of linearly added stimuli of separate responses. Participants were fitted with a dry-sensor EEG cap and were presented with a series of Stroop and auditory stimuli. For some Stroop stimuli, auditory stimuli occurred simultaneously or in a close time proximity to the Stroop stimuli. We sought to estimate the linear contribution of the ERP from Stroop and oddball stimuli. We found that the magnitude of the difference waves were 3.07 ± 1.65 µV and 2.82 ± 1.34 µV for congruent and incongruent stimuli, respectively. As the average amplitude in the P3 region for both the congruent and incongruent difference waves was lower than the magnitude of the auditory oddball presented simultaneously with Stroop stimuli (12.13 ± 1.00 µV for congruent and 11.78 ± 1.05 µV for incongruent Stroop), we expect that the contribution of P3 auditory oddball would not mask a potential Stroop effect even if the timing of the auditory oddball stimuli were experimentally manipulated, a direction that we hope to explore in future work. In conclusion, we determine this paradigm is suitable for measuring cognitive load in precisely timed tasks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Browne, Connor
Evaluating the Effectiveness of Preprocessing Methods on Motor Classification Scores in EEG Data Masters Thesis
University of Washington, 2023.
@mastersthesis{browne2023evaluating,
title = {Evaluating the Effectiveness of Preprocessing Methods on Motor Classification Scores in EEG Data},
author = {Connor Browne},
url = {https://www.proquest.com/openview/f610ae2952cdc715d3512cd8a8121b89/1?pq-origsite=gscholar&cbl=18750&diss=y},
year = {2023},
date = {2023-06-24},
urldate = {2023-07-24},
school = {University of Washington},
abstract = {Classification of motor tasks is of significant interest in brain-computer interfacing today. Electroencephalograph data contains a large amount of noise obfuscating the signal associated with these motor tasks. Various preprocessing techniques exist to increase the signalto-noise ratio allowing for more accurate classifications. The effectiveness of these techniques varies between motor tasks and in different environments. There is a need to evaluate these different techniques in many different environments and with different motor tasks. This thesis investigates the effectiveness of several preprocessing techniques and classification models for classifying four different motor imagery tasks from EEG data. Specifically, Frequency Filtering, ICA, and CSP are evaluated using Naive Bayes, kNN, Linear SVM, RBF SVM, LDA, Random Forest, and a MLP Neural Network. To control for the environment, data was collected from student volunteers in short sessions designed to demonstrate either eye blinking, eye rolling, jaw clenching, or neck turning. Each task had its own procedure for the session. Motor tasks in data were evaluated for frequency and amplitude commonalities using continuous wavelet transforms and Fourier transforms. Preprocessing Techniques were then iteratively applied to these datasets and evaluated using an ML model. The evaluation metrics used were Accuracy, F1, Precision, and Recall. Results showed that the combination of Frequency Filtering, ICA, and CSP with the Naive Bayes and Random Forest models yielded the highest accuracy and F1 for all motor tasks. These findings contribute to the field of EEG signal processing and could have potential applications in the development of brain-computer interfaces. It also directly contributes to a greater project in spatial neglect rehabilitation by providing novel insights to common artifacts in EEG data, as well as to the creation of a framework for data processing in real-time and offline.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Chiossi, Francesco; Ou, Changkun; Mayer, Sven
In: 2023.
@article{chiossi2023exploring,
title = {Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems},
author = {Francesco Chiossi and Changkun Ou and Sven Mayer},
url = {https://www.researchgate.net/profile/Francesco-Chiossi/publication/369033584_Exploring_Physiological_Correlates_of_Visual_Complexity_Adaptation_Insights_from_EDA_ECG_and_EEG_Data_for_Adaptation_Evaluation_in_VR_Adaptive_Systems/links/64064d5d0cf1030a567a05fb/Exploring-Physiological-Correlates-of-Visual-Complexity-Adaptation-Insights-from-EDA-ECG-and-EEG-Data-for-Adaptation-Evaluation-in-VR-Adaptive-Systems.pdf},
year = {2023},
date = {2023-04-23},
urldate = {2023-04-23},
abstract = {Physiologically-adaptive Virtual Reality can drive interactions and adjust virtual content to better fit users’ needs and support specific goals. However, the complexity of psychophysiological inference hinders efficient adaptation as the relationship between cognitive and physiological features rarely show one-to-one correspondence. Therefore, it is necessary to employ multimodal approaches to evaluate the effect of adaptations. In this work, we analyzed a multimodal dataset (EEG, ECG, and EDA) acquired during interaction with a VR-adaptive system that employed EDA as input for adaptation of secondary task difficulty. We evaluated the effect of dynamic adjustments on different physiological features and their correlation. Our results show that when the adaptive system increased the secondary task difficulty, theta, beta, and phasic EDA features increased. Moreover, we found a high correlation between theta, alpha, and beta oscillations during difficulty adjustments. Our results show how specific EEG and EDA features can be employed for evaluating VR adaptive systems.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chanpornpakdi, Ingon; Noda, Motoi; Tanaka, Toshihisa; Harpaz, Yuval; Geva, Amir B
Clustering of advertising images using electroencephalogram Conference
Proceedings of 2022 APSIPA Annual Summit and Conference, 2022.
@conference{chanpornpakdiclustering,
title = {Clustering of advertising images using electroencephalogram},
author = {Ingon Chanpornpakdi and Motoi Noda and Toshihisa Tanaka and Yuval Harpaz and Amir B Geva},
url = {http://www.apsipa.org/proceedings/2022/APSIPA%202022/TuAM1-8/1570840214.pdf},
year = {2022},
date = {2022-11-07},
booktitle = {Proceedings of 2022 APSIPA Annual Summit and Conference},
abstract = {Packaging and advertisements of brands affect customers’ decision-making on purchasing products and could lead to business loss. Hence, neuromarketing, the application of neuroscience in the marketing field, is introduced aiming to understand customers’ cognitive functions toward advertisements or products. Our study focused on identifying how the brain respond to different types of advertising image of the same brand were perceived using electroencephalogram (EEG). We performed an experiment using 33 different Coca-Cola advertising images in RSVP (rapid serial visual presentation) task on 23 participants. A seven channels EEG dry headset was used to record the visual event-related potential (ERP), specifically, the positive peak found at 300 to 700 ms after image onset; P300, to compare the perception response. We applied k-means and hierarchical clustering to the obtained EEG data, and achieved the best clustering for three clusters, yielding different P300 amplitudes and latencies. The typical Coca-Cola ads, red color with Cola-cola text on the ads, induced a faster and larger response, implying better perception than the unconventional or black color ads. We conclude that ERP clustering may be a useful tool for neuromarketing. However, the relationship between the EEG-based cluster and the image-based cluster should be further investigated to confirm the suggestion.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Dong, Xian; Wu, Yeyu; Tu, Zhijun; Cao, Bin; Li, Xianting; Yang, Zixu; Liu, Fei; Xing, Zheli
Influence of ambient temperature on personnel thermal comfort and working efficiency under isolation condition of underground engineering Journal Article
In: Energy and Buildings, pp. 112438, 2022.
@article{dong2022influence,
title = {Influence of ambient temperature on personnel thermal comfort and working efficiency under isolation condition of underground engineering},
author = {Xian Dong and Yeyu Wu and Zhijun Tu and Bin Cao and Xianting Li and Zixu Yang and Fei Liu and Zheli Xing},
doi = {https://doi.org/10.1016/j.enbuild.2022.112438},
year = {2022},
date = {2022-08-29},
urldate = {2022-01-01},
journal = {Energy and Buildings},
pages = {112438},
publisher = {Elsevier},
abstract = {When attacked by weapons of mass destruction, underground engineering will operate under isolation condition, which results in the increase of temperature, humidity, and CO2 concentration. At present, there are few studies on personnel thermal comfort and working efficiency in underground engineering, especially under isolation condition. To improve the personnel thermal comfort and working efficiency under such condition, the influence of environmental temperature changes on human thermal comfort and working efficiency was investigated through a combination of subjective and objective methods. A subjective questionnaire and working efficiency test were conducted on the subjects in the artificial climate chamber, and synchronously monitored electrocardiography (ECG), electroencephalography (EEG), and other physiological parameters of the subjects were recorded, when isolation condition was achieved in the artificial climate chamber. The results show that: (1) the human neutral temperature is 24.2 °C, and thermal comfort zone is [23 °C, 25.5 °C] for isolation condition; (2) the high working efficiency area is [27.3 °C, 28.8 °C] for isolation condition; (3) the average of the TSV corresponding to the highest working efficiency point is 1.3 under isolation condition; (4) from the correlation analysis of working efficiency and personnel physiological indicators, personnel EEG index and task performance are significantly related, and the ECG index and task performance are not relevant for subjects performing brain work under isolation conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Humphries, Joseph B; Mattos, Daniela JS; Rutlin, Jerrel; Daniel, Andy GS; Rybczynski, Kathleen; Notestine, Theresa; Shimony, Joshua S; Burton, Harold; Carter, Alexandre; Leuthardt, Eric C
Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface Journal Article
In: Brain-Computer Interfaces, vol. 9, no. 3, pp. 179–192, 2022.
@article{humphries2022motor,
title = {Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface},
author = {Joseph B Humphries and Daniela JS Mattos and Jerrel Rutlin and Andy GS Daniel and Kathleen Rybczynski and Theresa Notestine and Joshua S Shimony and Harold Burton and Alexandre Carter and Eric C Leuthardt},
doi = {https://doi.org/10.1080/2326263X.2022.2057757},
year = {2022},
date = {2022-07-01},
urldate = {2022-01-01},
journal = {Brain-Computer Interfaces},
volume = {9},
number = {3},
pages = {179--192},
publisher = {Taylor & Francis},
abstract = {Upper extremity weakness in chronic stroke remains a problem not fully addressed by current therapies. Brain–computer interfaces (BCIs) engaging the unaffected hemisphere are a promising therapy that are entering clinical application, but the mechanism underlying recovery is not well understood. We used resting state functional MRI to assess the impact a contralesionally driven EEG BCI therapy had on motor system functional organization. Patients used a therapeutic BCI for 12 weeks at home. We acquired resting-state fMRI scans and motor function data before and after the therapy period. Changes in functional connectivity (FC) strength between motor network regions of interest (ROIs) and the topographic extent of FC to specific ROIs were analyzed. Most patients achieved clinically significant improvement. Motor FC strength and topographic extent decreased following BCI therapy. Motor recovery correlated with reductions in motor FC strength across the entire motor network. These findings suggest BCI-mediated interventions may reverse pathologic strengthening of dysfunctional network interactions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rustamov, Nabi; Humphries, Joseph; Carter, Alexandre; Leuthardt, Eric C
Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke Journal Article
In: Brain Communications, vol. 4, iss. 3, 2022.
@article{rustamov2022thetab,
title = {Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke},
author = {Nabi Rustamov and Joseph Humphries and Alexandre Carter and Eric C Leuthardt},
doi = {https://doi.org/10.1093/braincomms/fcac136},
year = {2022},
date = {2022-05-25},
urldate = {2022-01-01},
journal = {Brain Communications},
volume = {4},
issue = {3},
abstract = {Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain–computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain–computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain–computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain–computer interface therapy are associated with alterations in phase–amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain–computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain–computer interface therapy and once every 4 weeks after the therapy. Changes in phase–amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta–gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain–computer interface therapy sessions. Importantly, an increase in theta–gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta–gamma coupling increase following brain–computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta–gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha–gamma coupling was not altered by brain–computer interface therapy. Power spectra did not change significantly over the course of the brain–computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain–computer interface therapy was strongly correlated with increased theta–gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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