Wearable Sensing’s wireless DSI-Flex is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-Flex takes on average less than 5 minutes 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.
The DSI-Flex has dry sensors on flexible cables, enabling scientists to place the electrodes in varying configurations on the head. These flexible sensors are designed to be screwed into custom caps, so that scientists can order 1 DSI-Flex, and multiple caps, allowing for rapid application of multiple electrode configurations. Every sensor on the DSI-Flex can be customized as either ExG, GSR, TEMP, and REP. 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, & Brain-Computer Interfaces
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-Flex was designed for ultra-rapid setup, taking on average less than 5 minutes 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-Flex can be customized so that an EEG sensor is replaced with a DSI auxiliary sensor. There are up to 7 locations on the DSI-Flex, enabling any configuration of the following sensors: EEG, ECG, EMG, EOG, GSR, RESP, & TEMP. 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
Up to 7 Custom Sensor Locations
Reference / Ground
Common Mode Follower / Custom
Head Size Range
Custom Caps
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
Applied Neuroscience NeuroGuide; Brainmaster Brain Avatar; EEGer
Neuromarketing
CAPTIV Neurolab
Presentation
Presentation; E-Prime
Kim, Suhye; Kim, Jung-Hwan; Hyung, Wooseok; Shin, Suhkyung; Choi, Myoung Jin; Kim, Dong Hwan; Im, Chang-Hwan
Characteristic Behaviors of Elementary Students in a Low Attention State During Online Learning Identified Using Electroencephalography Journal Article
In: IEEE Transactions on Learning Technologies, 2023.
@article{kim2023characteristic,
title = {Characteristic Behaviors of Elementary Students in a Low Attention State During Online Learning Identified Using Electroencephalography},
author = {Suhye Kim and Jung-Hwan Kim and Wooseok Hyung and Suhkyung Shin and Myoung Jin Choi and Dong Hwan Kim and Chang-Hwan Im},
doi = {10.1109/TLT.2023.3289498},
year = {2023},
date = {2023-06-29},
urldate = {2023-01-01},
journal = {IEEE Transactions on Learning Technologies},
publisher = {IEEE},
abstract = {With the widespread application of online education platforms, the necessity for identifying learner's mental states from webcam videos is increasing as it can be potentially applied to artificial intelligence-based automatic identification of learner's states. However, the behaviors that elementary school students frequently exhibit during online learning particularly when they are in a low attention state have rarely been investigated. This study employed electroencephalography (EEG) to continuously track changes in the learner's attention state during online learning. A new EEG index reflecting elementary students' attention level was developed using an EEG dataset acquired from 30 fourth graders during a computerized d2 test of attention. Characteristic behaviors of 24 elementary students in a low attention state were then identified from the webcam videos showing their upper bodies captured during 40-minute online lectures, with the proposed EEG index being used as a reference to determine their attention level at the time. Various characteristic behaviors were identified regarding participant's mouth, head, arms, and torso. For example, opening mouth or leaning back was observed more frequently in a low attention state than in a high attention state. It is expected that the characteristic behaviors reflecting learner's low attention state would be utilized as a useful reference in developing more interactive and effective online education systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cho, Ji Young; Wang, Ze-Yu; Hong, Yi-Kyung
An EEG Study on the Interior Environmental Design Elements in Promoting Healing Journal Article
In: Journal of the Korean Housing Association, vol. 34, no. 3, pp. 067–079, 2023.
@article{cho2023eeg,
title = {An EEG Study on the Interior Environmental Design Elements in Promoting Healing},
author = {Ji Young Cho and Ze-Yu Wang and Yi-Kyung Hong},
url = {https://journal.khousing.or.kr/articles/xml/XeZw/},
doi = {https://doi.org/10.6107/JKHA.2023.34.3.067},
year = {2023},
date = {2023-06-25},
urldate = {2023-01-01},
journal = {Journal of the Korean Housing Association},
volume = {34},
number = {3},
pages = {067–079},
publisher = {The Korean Housing Association},
abstract = {This study aimed to identify the attributes of environmental design elements that promote healing. Eighteen college students participated in an EEG study, and their Ratio of Alpha to Beta (RAB) was examined in relation to eight interior design stimuli consisting of different conditions of illuminance (high/low), color (green/white), and biophilia ( natural/artificial). The EEGs of the 19 channels were measured using a dry-type EEG device (DSI-24). Participants also completed a survey questionnaire, choosing the best stimuli for healing environments and for wanting to stay. The EEG data were analyzed using a Wilcoxon signed-rank test to identify statistical differences in responses to different stimuli. The main results were as follows: (1) RAB was most frequently observed in the left temporal and parietal lobes; (2) when comparing pre- and post-stimulation, significant differences were observed between background EEG and six stimuli (S1, S3, S5, S6, S7, and S8); (3) RAB tended to be high with low illuminance, white walls, and artificial biophilia; and (4) the psychological and EEG responses did not match; stimuli with high illuminance and natural biophilia were selected the most for healing and wanting to stay environments. The results indicate the necessity for studying environmental responses via both physiological and psychological aspects as they compensate for each other. and artificial biophilia; and (4) the psychological and EEG responses did not match; stimuli with high illuminance and natural biophilia were selected the most for healing and wanting to stay environments. The results indicate the necessity for studying environmental responses via both physiological and psychological aspects as they compensate for each other. and artificial biophilia; and (4) the psychological and EEG responses did not match; stimuli with high illuminance and natural biophilia were selected the most for healing and wanting to stay environments. The results indicate the necessity for studying environmental responses via both physiological and psychological aspects as they compensate for each other.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fan, Tengwen; Zhang, Liming; Liu, Jianyi; Niu, Yanbin; Hong, Tian; Zhang, Wenfang; Shu, Hua; Zhao, Jingjing
Phonemic mismatch negativity mediates the association between phoneme awareness and character reading ability in young Chinese children Journal Article
In: Neuropsychologia, pp. 108624, 2023.
@article{fan2023phonemic,
title = {Phonemic mismatch negativity mediates the association between phoneme awareness and character reading ability in young Chinese children},
author = {Tengwen Fan and Liming Zhang and Jianyi Liu and Yanbin Niu and Tian Hong and Wenfang Zhang and Hua Shu and Jingjing Zhao},
doi = {https://doi.org/10.1016/j.neuropsychologia.2023.108624},
year = {2023},
date = {2023-06-15},
urldate = {2023-01-01},
journal = {Neuropsychologia},
pages = {108624},
publisher = {Elsevier},
abstract = {Poor phonological awareness is associated with greater risk for reading disability. The underlying neural mechanism of such association may lie in the brain processing of phonological information. Lower amplitude of auditory mismatch negativity (MMN) has been associated with poor phonological awareness and with the presence of reading disability. The current study recorded auditory MMN to phoneme and lexical tone contrast with odd-ball paradigm and examined whether auditory MMN mediated the associations between phonological awareness and character reading ability through a three-year longitudinal study in 78 native Mandarin-speaking kindergarten children. Hierarchical linear regression and mediation analysis showed that the effect of phoneme awareness on the character reading ability was mediated by the phonemic MMN in young Chinese children. Findings underscore the key role of phonemic MMN for the underlying neurodevelopmental mechanism linking phoneme awareness and reading ability.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Yu, Heeseung; Han, Eunkyoung
People see what they want to see: an EEG study Journal Article
In: Cognitive Neurodynamics, pp. 1–15, 2023.
@article{yu2023people,
title = {People see what they want to see: an EEG study},
author = {Heeseung Yu and Eunkyoung Han},
url = {https://link.springer.com/article/10.1007/s11571-023-09982-8},
year = {2023},
date = {2023-06-13},
urldate = {2023-01-01},
journal = {Cognitive Neurodynamics},
pages = {1–15},
publisher = {Springer},
abstract = {This study explored selective exposure and confirmation bias in the choices participants made about which political videos to watch, and whether their political positions changed after they watched videos that either agreed with or opposed their positions on two controversial issues in South Korea: North Korea policy and social welfare policy. The participants completed questionnaires before and after they watched the videos, were asked to select thumbnails of videos before they watched any, and had their brain wave activity measured through electroencephalogram (EEG) as they watched both types of videos. The participants demonstrated selective exposure as they primarily selected video thumbnails with content that matched their political orientations, and they demonstrated confirmation bias as their questionnaire responses after they watched the videos indicated that their positions had hardened. There were also statistically significant differences in alpha, beta, sensory motor rhythm, low beta, mid beta, and fast alpha activity depending on the political orientation consistency between the participants and the videos. Future studies could expand this line of research beyond college students and beyond Asia, and longitudinal work could also be conducted to determine if the obtained patterns remain constant over time.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ferrisi, Leonardo M
Optimizing an assistive Brain Computer Interface that uses Auditory Attention as Input Masters Thesis
2023.
@mastersthesis{ferrisi2023optimizing,
title = {Optimizing an assistive Brain Computer Interface that uses Auditory Attention as Input},
author = {Leonardo M Ferrisi},
url = {https://digitalworks.union.edu/cgi/viewcontent.cgi?article=3754&context=theses},
year = {2023},
date = {2023-06-01},
urldate = {2023-01-01},
abstract = {Brain Computer Interfaces (BCIs) allow individuals to operate technology using (typically consciously controllable) aspects of their brain activity. Auditory BCIs utilize principles of Auditory Event Related
Potentials or Auditory Evoked Potentials as a reproducible controllable features that individuals can use to operate a BCI. These Auditory BCIs in their most basic format can allow users to answer yes or no questions by listening to either one auditory stimuli or the other. Current accuracy in intended response detection for these kinds of BCIs can be as good as mean accuracy of 77 % [5]. BCI research tends to optimize the computer side of the system however the ease of use for the human operating the system is an important point of consideration as well. This research project aimed to determine what factors make a human operator able to achieve the highest accuracy using a given previously successfully demonstrated classifier. This research project primarily sought to answer the questions; to what degree people can improve their accuracy in operating an Auditory BCI and what factors of the stimulus used can be altered to achieve this. The results of this project, obtained through the data collected from six individuals, found that slower stimuli speeds for eliciting Auditory Event Related Potentials were significantly better at achieving higher prediction accuracies compared to faster stimulus speeds. The amount of time spent using the system appeared to result in diminishing returns in accuracy regardless of condition however not before an initial spike in greater classifier prediction accuracy for the second condition run on each subject. Although further research is needed to gain more conclusive evidence for or against the hypothesis, the results of this research may be able suggest that individuals can improve their performance using Auditory BCIs with practice at optimal parameters albeit within a given time frame before experiencing diminishing returns. These findings would stand to provide benefit both to continued research in making optimal non-invasive alternative communication technologies as well as making progress in finding the potential ceiling in accuracy that an Auditory BCI can have in interpreting brain activity for the intended action of the user},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Demarest, Phillip; Rustamov, Nabi; Swift, James; Xie, Tao; Adamek, Markus; Cho, Hohyun; Wilson, Elizabeth; Han, Zhuangyu; Belsten, Alexander; Luczak, Nicholas; others,
A Novel Theta-Controlled Vibrotactile Brain-Computer Interface To Treat Chronic Pain: A Pilot Study Journal Article
In: 2023.
@article{demarest2023novel,
title = {A Novel Theta-Controlled Vibrotactile Brain-Computer Interface To Treat Chronic Pain: A Pilot Study},
author = {Phillip Demarest and Nabi Rustamov and James Swift and Tao Xie and Markus Adamek and Hohyun Cho and Elizabeth Wilson and Zhuangyu Han and Alexander Belsten and Nicholas Luczak and others},
doi = {https://doi.org/10.21203/rs.3.rs-2973437/v1},
year = {2023},
date = {2023-06-01},
urldate = {2023-01-01},
abstract = {Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4–7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity and pain interference scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Maffei, Luigi; Masullo, Massimiliano
Sens i-Lab: a key facility to expand the traditional approaches in experimental acoustics Journal Article
In: Institute of Noise Control Engineering, vol. 266, no. 2, pp. 134–140, 2023.
@article{maffei2023sens,
title = {Sens i-Lab: a key facility to expand the traditional approaches in experimental acoustics},
author = {Luigi Maffei and Massimiliano Masullo},
doi = {https://doi.org/10.3397/NC_2023_0019},
year = {2023},
date = {2023-05-25},
urldate = {2023-01-01},
booktitle = {INTER-NOISE and NOISE-CON Congress and Conference Proceedings},
journal = {Institute of Noise Control Engineering},
volume = {266},
number = {2},
pages = {134–140},
organization = {Institute of Noise Control Engineering},
abstract = {Recent advances in developing new tools and miniaturised devices to measure, analyse, model and simulate existing or future projects are more and more influencing the way to investigate and solve problems of various disciplines fostering deep changes in the research paradigms toward human-centred, multisensory and multidisciplinary approaches. In acoustics, beyond the negative effect of noise on individuals and its mitigation, researchers are even more interested in investigating how the complexity of the multisensory environment modulates the individuals' holistic experience. To this aim, the Department of the Università degli Studi della Campania "Luigi Vanvitelli" has built the Sens i-Lab, a key facility integrating, in a single test room, the simulation and control of the physical environment (acoustics, vision, lighting, microclimate, IAQ) with advanced systems for simulation of virtual environments. To complement the simulation and control of the stimuli, the Sens i-Lab is equipped with a set of systems and devices for motion tracking, for the measurement of the biofeedback signals (EEG, EDA, HRV, VAF) and their association with environmental stimuli and self-reported psychological measures of people well-being. Taking advantage of the Sens i-Lab setting, new research fields and applications in acoustics are possible. Some of them are presented.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lim, Hyunmi; Jeong, Chang Hyeon; Kang, Youn Joo; Ku, Jeonghun
Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients Journal Article
In: Cyberpsychology, Behavior, and Social Networking, 2023.
@article{lim2023attentional,
title = {Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients},
author = {Hyunmi Lim and Chang Hyeon Jeong and Youn Joo Kang and Jeonghun Ku},
doi = {https://doi.org/10.1089/cyber.2022.0176},
year = {2023},
date = {2023-04-20},
urldate = {2023-01-01},
journal = {Cyberpsychology, Behavior, and Social Networking},
publisher = {Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…},
abstract = {Brain–computer interface (BCI) is a promising technique that enables patients' interaction with computers or machines by analyzing specific brain signal patterns and provides patients with brain state-dependent feedback to assist in their rehabilitation. Action observation (AO) and peripheral electrical stimulation (PES) are conventional methods used to enhance rehabilitation outcomes by promoting neural plasticity. In this study, we assessed the effects of attentional state-dependent feedback in the combined application of BCI-AO with PES on sensorimotor cortical activation in patients after stroke. Our approach involved showing the participants a video with repetitive grasping actions under four different tasks. A mu band suppression (8–13 Hz) corresponding to each task was computed. A topographical representation showed that mu suppression of the dominant (healthy) and affected hemispheres (stroke) gradually became prominent during the tasks. There were significant differences in mu suppression in the affected motor and frontal cortices of the stroke patients. The involvement of both frontal and motor cortices became prominent in the BCI-AO+triggered PES task, in which feedback was given to the patients according to their attentive watching. Our findings suggest that synchronous stimulation according to patient attention is important for neurorehabilitation of stroke patients, which can be achieved with the combination of BCI-AO feedback with PES. BCI-AO feedback combined with PES could be effective in facilitating sensorimotor cortical activation in the affected hemispheres of stroke patients.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Seo, Seoung Won; Kim, Yong Seong
Stroke Patients: Effects of Combining Sitting Table Tennis Exercise with Neurological Physical Therapy on Brain Waves Journal Article
In: The Journal of Korean Physical Therapy, vol. 35, no. 1, pp. 19–23, 2023.
@article{seo2023stroke,
title = {Stroke Patients: Effects of Combining Sitting Table Tennis Exercise with Neurological Physical Therapy on Brain Waves},
author = {Seoung Won Seo and Yong Seong Kim},
doi = {https://doi.org/10.18857/jkpt.2023.35.1.19},
year = {2023},
date = {2023-02-28},
urldate = {2023-01-01},
journal = {The Journal of Korean Physical Therapy},
volume = {35},
number = {1},
pages = {19--23},
publisher = {The Korea Society of Physical Therapy},
abstract = {Purpose: The purpose of this study is to analyze the brain waves and develop various exercise programs to improve the physical and mental aspects of stroke patients when neurological physical therapy and sitting table tennis exercise are applied to stroke patients.
Methods: In this study, an experiment was conducted on 15 patients diagnosed with stroke, and training was performed after changing the ping-pong table to a sitting position to apply ping-pong exercise to stroke patients. After training was conducted for 40 minutes twice a week for 4 weeks, brain waves were measured before and after. EEG was measured using Laxtha’s DSI-24 equipment as a measurement tool, and data values were extracted through the Telescan program.
Results: Most of the relative beta waves showed a significant difference before and after the intervention. As for the characteristics of beta waves, this result can be seen as being highly activated during exercise or other activities.
Conclusion: Ping-pong exercise in a sitting position is a good intervention method for stroke patients, and it can help to use it as basic data in clinical practice by showing brain activity.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chen, Sheng; Xie, Haiqun; Yang, Hongjun; Fan, Chenchen; Hou, Zengguang; Zhang, Chutian
A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments Journal Article
In: Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022, pp. 349–361, 2023.
@article{chen2023classification,
title = {A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments},
author = {Sheng Chen and Haiqun Xie and Hongjun Yang and Chenchen Fan and Zengguang Hou and Chutian Zhang},
url = {https://link.springer.com/chapter/10.1007/978-981-99-0301-6_27},
year = {2023},
date = {2023-02-18},
urldate = {2023-01-01},
booktitle = {Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022, Xi’an, China, December 16--18, 2022, Proceedings},
journal = {Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022},
pages = {349--361},
organization = {Springer},
abstract = {Mild cognitive impairment (MCI) is the preliminary stage of dementia, and has a high risk of progression to Alzheimer’s disease (AD) in the elderly. Early detection of MCI plays a vital role in preventing progression of AD. Clinical diagnosis of MCI requires many examinations, which are highly demanding on hospital equipment and expensive for patients. Electroencephalography (EEG) offers a non-invasive and less expensive way to diagnose MCI early. In this paper, we propose a multi-modal fusion classification framework for MCI detection. We collect EEG data using a delayed match-to-sample task and analyze the differences between the two groups. Based on analysis results, we extract Power spectral density (PSD), PSD enhanced, Event-related potential (ERP) features in EEG signal along with physiological features and behavioral features of the subjects to classify MCI and healthy elderly. By comparing the effect of different features on classification performance, we find that the time-domain based ERP features are better than the frequency-domain based PSD or PSD enhanced features to overcome inter-individual differences to distinguish MCI, and these two features have good complementarity, fusing ERP and PSD enhanced features can greatly improve the classification accuracy to 84.74%. The final result shows that MCI and healthy elderly can be well classified by using this framework.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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