Wearable Sensing’s wireless DSI-24 is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-24 takes on average less than 3 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. As a result, the DSI-24 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-24 has sensors that provide full head coverage with 19 electrodes on the head, 2 earclip sensors, and also has 3 built-in auxiliary inputs for acquisition of up to 3 auxiliary sensors. It also has an 8-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-24 was designed for ultra-rapid setup, taking on average less than 3 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-24 has 3 auxiliary inputs on the headset, which allows for automatic synchronization of Wearable Sensing’s auxiliary sensors to the EEG. The sensors available include 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
Fp1, Fp2, Fz, F3, F4, F7, F8, Cz, C3, C4, T7/T3, T8/T4, Pz, P3, P4, P7/T5, P8/T6, O1, O2, A1, A2
Reference / Ground
Common Mode Follower / Fpz
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 / 24 channels
Wireless
Bluetooth
Wireless Range
10 m
Run-time
> 24 Hours, Hot-Swappable Batteries
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
Farrens, A.; Torrecilla, M.; Fernandez, L. Garcia; Johnson, C.; Wolbrecht, E. T.; Reinkensmeyer, D. J.; Gupta, D.
Society for Neuroscience 2023.
@conference{nokey,
title = {Behavioral and EEG Features of Finger Proprioception and Passive Movement: Effect of Error Feedback on Proprioception},
author = {A. Farrens and M. Torrecilla and L. Garcia Fernandez and C. Johnson and E.T. Wolbrecht and D.J. Reinkensmeyer and D. Gupta},
url = {https://wearablesensing.com/wp-content/uploads/2024/07/SfN_Poster_2023_Final.pdf},
year = {2023},
date = {2023-11-11},
organization = {Society for Neuroscience},
abstract = {Purpose: Proprioception, the sense of body movement, is critical to motor learning and is predictive of responsiveness to motor rehabilitation after stroke, which often damages proprioception [1-5]. It is of interest to understand how to enhance proprioception. Here, we used the proprioceptive “Crisscross” task on Finger Individuating Grasp Exercise Robot (FINGER) [4-8], to determine if error feedback improves passive proprioceptive acuity, and identify neural markers of proprioceptive processing using EEG.
Methods: 19 healthy right-handed adults (age: 22-34 yrs, 12 male) participated with informed consent. In Crisscross, FINGER passively crossed the right index and middle fingers in symmetric, alternating flexion/extension trajectories (0-36 deg), at random speeds (8,11,16 deg/s) and inter-trial intervals (2-3.5 s), for 120 trials (6 runs, 20 trls/run), with vision of the hand occluded. Participants were tasked to press a button with the left hand at the instance of perceived finger crossing. Feedback group (FB, n=9) received visual feedback as a numerical error (absolute finger separation at press), while the no- Feedback group (nFB, n=10) received a visual cue of ‘button press’. 19 channel EEG was acquired (DSI-24, Wearable Sensing, CA). EEG data was filtered (bandpass [0.1, 30] Hz), ICA denoised, baseline corrected (-200 to 0 ms) and epoched (-200 to 4000 ms) with respect to movement onset; noisy epochs were removed (+/- 100μV, <5% trials). Event Related Potentials (ERPs) were calculated as the mean across trials.
Results: Proprioceptive errors were smaller in the FB group compared to the nFB group (t-test, p < 0.01). 400-600 ms post button press, the FB group showed a lateralized ERP response in frontoparietal regions contralateral to the propriocepting hand, which increased with error magnitude (kw-test, p < 0.01), that was absent in the nFB group. EEG also showed a Contingent Negative Variation (CNV) at Cz, initiated at movement onset (initial stimulus), followed by a negative peak at perceived finger crossing (imperative stimulus). A mixed model (fixed factors: feedback, speed) performed on CNV magnitude (600-800ms post movement onset, 200-1200ms prior to cross over) showed an interaction between feedback and speed (p < 0.03). In the FB group, CNV magnitude increased with finger speed but not in the nFB group.
Conclusions: These results demonstrate that error feedback can improve proprioceptive acuity in healthy adults and is accompanied by an error-dependent ERP response in the sensorimotor cortex. The crisscross task also elicits a CNV response; we are the first to show modulation of CNV by proprioceptively- sensed speed and performance feedback.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Liu, F.; Yang, P.; Shu, Y.; Liu, N.; Sheng, J.; Luo, J.; Wang, X.; Liu, Y.
Emotion Recognition from Few-Channel EEG Signals by Integrating Deep Feature Aggregation and Transfer Learning Journal Article
In: IEEE Transactions on Affective Computing, no. 01, pp. 1-17, 2023, ISSN: 1949-3045.
@article{10328701,
title = {Emotion Recognition from Few-Channel EEG Signals by Integrating Deep Feature Aggregation and Transfer Learning},
author = {F. Liu and P. Yang and Y. Shu and N. Liu and J. Sheng and J. Luo and X. Wang and Y. Liu},
doi = {10.1109/TAFFC.2023.3336531},
issn = {1949-3045},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
journal = {IEEE Transactions on Affective Computing},
number = {01},
pages = {1-17},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Electroencephalogram (EEG) signals have been widely studied in human emotion recognition. The majority of existing EEG emotion recognition algorithms utilize dozens or hundreds of electrodes covering the whole scalp region (denoted as full-channel EEG devices in this paper). Nowadays, more and more portable and miniature EEG devices with only a few electrodes (denoted as few-channel EEG devices in this paper) are emerging. However, emotion recognition from few-channel EEG data is challenging because the device can only capture EEG signals from a portion of the brain area. Moreover, existing full-channel algorithms cannot be directly adapted to few-channel EEG signals due to the significant inter-variation between full-channel and few-channel EEG devices. To address these challenges, we propose a novel few-channel EEG emotion recognition framework from the perspective of knowledge transfer. We leverage full-channel EEG signals to provide supplementary information for few-channel signals via a transfer learning-based model CD-EmotionNet, which consists of a base emotion model for efficient emotional feature extraction and a cross-device transfer learning strategy. This strategy helps to enhance emotion recognition performance on few-channel EEG data by utilizing knowledge learned from full-channel EEG data. To evaluate our cross-device EEG emotion transfer learning framework, we construct an emotion dataset containing paired 18-channel and 5-channel EEG signals from 25 subjects, as well as 5-channel EEG signals from 13 other subjects. Extensive experiments show that our framework outperforms state-of-the-art EEG emotion recognition methods by a large margin.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chan, Melody MY; Choi, Coco XT; Tsoi, Tom CW; Shea, Caroline KS; Yiu, Klaire WK; Han, Yvonne MY
In: Brain Stimulation, vol. 16, iss. 8, pp. P1604-1616, 2023.
@article{chan2023effects,
title = {Effects of multisession cathodal transcranial direct current stimulation with cognitive training on sociocognitive functioning and brain dynamics in ASD: A double-blind, sham-controlled, randomized EEG study},
author = {Melody MY Chan and Coco XT Choi and Tom CW Tsoi and Caroline KS Shea and Klaire WK Yiu and Yvonne MY Han},
doi = {https://doi.org/10.1016/j.brs.2023.10.012},
year = {2023},
date = {2023-10-31},
urldate = {2023-01-01},
journal = {Brain Stimulation},
volume = {16},
issue = {8},
pages = {P1604-1616},
publisher = {Elsevier},
abstract = {Background
Few treatment options are available for targeting core symptoms of autism spectrum disorder (ASD). The development of treatments that target common neural circuit dysfunctions caused by known genetic defects, namely, disruption of the excitation/inhibition (E/I) balance, is promising. Transcranial direct current stimulation (tDCS) is capable of modulating the E/I balance in healthy individuals, yet its clinical and neurobiological effects in ASD remain elusive.
Objective
This double-blind, randomized, sham-controlled trial investigated the effects of multisession cathodal prefrontal tDCS coupled with online cognitive remediation on social functioning, information processing efficiency and the E/I balance in ASD patients aged 14–21 years.
Methods
Sixty individuals were randomly assigned to receive either active or sham tDCS (10 sessions in total, 20 min/session, stimulation intensity: 1.5 mA, cathode: F3, anode: Fp2, size of electrodes: 25 cm2) combined with 20 min of online cognitive remediation. Social functioning, information processing efficiency during cognitive tasks, and theta- and gamma-band E/I balance were measured one day before and after the treatment.
Results
Compared to sham tDCS, active cathodal tDCS was effective in enhancing overall social functioning [F(1, 58) = 6.79, p = .012, ηp2 = 0.105, 90% CI: (0.013, 0.234)] and information processing efficiency during cognitive tasks [F(1, 58) = 10.07, p = .002, ηp2 = 0.148, 90% CI: (0.034, 0.284)] in these individuals. Electroencephalography data showed that this cathodal tDCS protocol was effective in reducing the theta-band E/I ratio of the cortical midline structures [F(1, 58) = 4.65, p = .035, ηp2 = 0.074, 90% CI: (0.010, 0.150)] and that this reduction significantly predicted information processing efficiency enhancement (b = −2.546, 95% BCa CI: [-4.979, −0.113], p = .041).
Conclusion
Our results support the use of multisession cathodal tDCS over the left dorsolateral prefrontal cortex combined with online cognitive remediation for reducing the elevated theta-band E/I ratio in sociocognitive information processing circuits in ASD patients, resulting in more adaptive regulation of global brain dynamics that is associated with enhanced information processing efficiency after the intervention.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mizrahi, Dor; Laufer, Ilan; Zuckerman, Inon
Modulation of Beta Power as a Function of Attachment Style and Feedback Valence Conference
International Conference on Brain Informatics, Springer 2023.
@conference{mizrahi2023modulation,
title = {Modulation of Beta Power as a Function of Attachment Style and Feedback Valence},
author = {Dor Mizrahi and Ilan Laufer and Inon Zuckerman},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43075-6_2},
year = {2023},
date = {2023-09-13},
urldate = {2023-01-01},
booktitle = {International Conference on Brain Informatics},
pages = {14–20},
organization = {Springer},
abstract = {Attachment theory is concerned with the basic level of social connection associated with approach and withdrawal mechanisms. Consistent patterns of attachment may be divided into two major categories: secure and insecure. As secure and insecure attachment style individuals vary in terms of their responses to affective stimuli and negatively valanced cues, the goal of this study was to examine whether there are differences in Beta power activation between secure and insecure individuals to feedback given while performing the arrow flanker task. An interaction emerged between Attachment style (secure or insecure) and Feedback type (success or failure) has shown differences in Beta power as a function of both independent factors. These results corroborate previous findings indicating that secure and insecure individuals differently process affective stimuli.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Georgiadis, Kostas; Kalaganis, Fotis P; Oikonomou, Vangelis P; Nikolopoulos, Spiros; Laskaris, Nikos A; Kompatsiaris, Ioannis
Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry Conference
International Conference on Brain Informatics, Springer 2023.
@conference{georgiadis2023harneshing,
title = {Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry},
author = {Kostas Georgiadis and Fotis P Kalaganis and Vangelis P Oikonomou and Spiros Nikolopoulos and Nikos A Laskaris and Ioannis Kompatsiaris},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43075-6_3},
year = {2023},
date = {2023-09-13},
urldate = {2023-01-01},
booktitle = {International Conference on Brain Informatics},
pages = {21–32},
organization = {Springer},
abstract = {Neuromarketing exploits neuroimaging techniques to study consumers’ responses to various marketing aspects, with the goal of gaining a more thorough understanding of the decision-making process. The neuroimaging technology encountered the most in neuromarketing studies is Electroencephalography (EEG), mainly due to its non-invasiveness, low cost and portability. Opposed to typical neuromarketing practices, which rely on signal-power related features, we introduce an efficient decoding scheme that is based on the principles of Riemannian Geometry and realized by means of a suitable deep learning (DL) architecture (i.e., SPDNet). We take advantage of a recently released, multi-subject, neuromarketing dataset to train SPDNet under the close-to-real-life scenario of product selection from a supermarket leaflet and compare its performance against standard tools in EEG-based neuromarketing. The sample covariance is used as an estimator of the ‘quasi-instantaneous’, brain activation pattern and derived from the multichannel signal recorded while the subject is gazing at a given product. Pattern derivation is followed by proper re-alignment to reduce covariate shift (inter-subject variability) before SPDNet casts its binary decision (i.e., “Buy”-“NoBuy”). The proposed decoder is characterized by sufficient generalizability to derive valid predictions upon unseen brain signals. Overall, our experimental results provide clear evidence about the superiority of the DL-decoder relatively to both conventional neuromarketing and alternative Riemannian Geometry-based approaches, and further demonstrate how neuromarketing can benefit from recent advances in data-centric machine learning and the availability of relevant experimental datasets.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Vall-llossera, Mónica Torrecilla; Farrens, Andria; Gupta, Disha; Reinkensmeyer, David
Electroencephalogram analysis of finger proprioception in healthy subjects Conference
International Brain Research Organization 2023.
@conference{nokey,
title = {Electroencephalogram analysis of finger proprioception in healthy subjects},
author = {Mónica Torrecilla Vall-llossera and Andria Farrens and Disha Gupta and David Reinkensmeyer
},
url = {https://wearablesensing.com/wp-content/uploads/2024/07/Poster_IBRO__Electroencephalogram_analysis_of_finger_proprioception_in_healthy_subjects_DEF_2.pdf},
year = {2023},
date = {2023-09-09},
urldate = {2023-09-09},
organization = {International Brain Research Organization},
abstract = {Proprioception enables us to perceive the position and movement of our body parts, which is essential for motor control and performance. In this study, we investigated the neural processing underlying proprioception using electroencephalogram (EEG) signals during Crisscross, a robotic proprioception task. Nine healthy adults performed the task (aged 22-34) with their right-hand during EEG acquisition. In the Crisscross task, a robot crossed the index and middle fingers in an alternating flexion/extension pattern with vision of the hand occluded. Participants performed Crisscross in two modes; the Non Button Pressing mode, (CC-NBP), where participants simply relaxed the fingers as the robot moved them, and the Button pressing mode (CC-BP), where participants additionally pressed a button when they perceived their fingers were overlapped. We analysed the event-related potential (ERP) of EEG data at the instance of movement onset and of button pressing, and compared responses when participants were actively engaged in making decisions based on proprioception (CC-BP) to when they were not (CC-NBP). In both conditions, we observed a positive ERP in the P3 channel that was significantly larger in the CC-PB condition (MWU, p<0.001), indicating this response is modulated by participants attending to proprioceptive information. In the CC-BP condition observed a contingent negative variation (CNV) in the Cz channel that was time-locked to movement onset and peaked in magnitude at button-press, suggesting that it is associated with the proprioceptively-driven decision of when to press the button. Finally, both responses were larger when the robot moved the fingers at high speed (36 deg/s) compared to slow speeds (16 deg/s, p = 0.03). These EEG features provide insight into proprioceptive processing in healthy individuals, and allow us to assess sensorimotor function at the neural level. Future work will study these responses in post-stroke individuals to better understand sensorimotor deficits.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Georgiadis, Kostas; Kalaganis, Fotis P; Riskos, Kyriakos; Matta, Eleftheria; Oikonomou, Vangelis P; Yfantidou, Ioanna; Chantziaras, Dimitris; Pantouvakis, Kyriakos; Nikolopoulos, Spiros; Laskaris, Nikos A; others,
NeuMa-the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour Journal Article
In: Scientific Data, vol. 10, no. 1, pp. 508, 2023.
@article{georgiadis2023neuma,
title = {NeuMa-the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour},
author = {Kostas Georgiadis and Fotis P Kalaganis and Kyriakos Riskos and Eleftheria Matta and Vangelis P Oikonomou and Ioanna Yfantidou and Dimitris Chantziaras and Kyriakos Pantouvakis and Spiros Nikolopoulos and Nikos A Laskaris and others},
doi = {https://doi.org/10.1038/s41597-023-02392-9},
year = {2023},
date = {2023-08-03},
urldate = {2023-01-01},
journal = {Scientific Data},
volume = {10},
number = {1},
pages = {508},
publisher = {Nature Publishing Group UK London},
abstract = {Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers’ behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Riek, Nathan T; Susam, Busra T; Hudac, Caitlin M; Conner, Caitlin M; Akcakaya, Murat; Yun, Jane; White, Susan W; Mazefsky, Carla A; Gable, Philip A
Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents Journal Article
In: Journal of Autism and Developmental Disorders, pp. 1–11, 2023.
@article{riek2023feedback,
title = {Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents},
author = {Nathan T Riek and Busra T Susam and Caitlin M Hudac and Caitlin M Conner and Murat Akcakaya and Jane Yun and Susan W White and Carla A Mazefsky and Philip A Gable},
url = {https://link.springer.com/article/10.1007/s10803-023-06038-y},
year = {2023},
date = {2023-07-01},
urldate = {2023-01-01},
journal = {Journal of Autism and Developmental Disorders},
pages = {1–11},
publisher = {Springer},
abstract = {The purpose of this study is to investigate if feedback related negativity (FRN) can capture instantaneous elevated emotional reactivity in autistic adolescents. A measurement of elevated reactivity could allow clinicians to better support autistic individuals without the need for self-reporting or verbal conveyance. The study investigated reactivity in 46 autistic adolescents (ages 12–21 years) completing the Affective Posner Task which utilizes deceptive feedback to elicit distress presented as frustration. The FRN event-related potential (ERP) served as an instantaneous quantitative neural measurement of emotional reactivity. We compared deceptive and distressing feedback to both truthful but distressing feedback and truthful and non-distressing feedback using the FRN, response times in the successive trial, and Emotion Dysregulation Inventory (EDI) reactivity scores. Results revealed that FRN values were most negative to deceptive feedback as compared to truthful non-distressing feedback. Furthermore, distressing feedback led to faster response times in the successive trial on average. Lastly, participants with higher EDI reactivity scores had more negative FRN values for non-distressing truthful feedback compared to participants with lower reactivity scores. The FRN amplitude showed changes based on both frustration and reactivity. The findings of this investigation support using the FRN to better understand emotion regulation processes for autistic adolescents in future work. Furthermore, the change in FRN based on reactivity suggests the possible need to subgroup autistic adolescents based on reactivity and adjust interventions accordingly.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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}
}
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