Research

Discover the Why of the Brain

Electroencephalography (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain through electrodes placed on the scalp. EEG can be used for various research applications, including studying brain function and activity, identifying neurological disorders, and investigating the effects of drugs or other interventions on brain activity. EEG is particularly useful for studying brain activity in real-time and identifying the timing and location of brain activity associated with specific cognitive processes or behaviors. It can also be used in clinical settings to diagnose and monitor neurological disorders such as epilepsy, sleep disorders, and traumatic brain injuries. Additionally, EEG can be used to investigate the effects of various interventions, such as cognitive training or neurofeedback, on brain activity and function.

Applications

What Causes the "Stoke" in Surfing?

For surfers, catching the perfect wave can induce a state of pure ecstasy known as the “stoke”. But what’s happening in the brain during this ultimate ride? Wearable Sensing created a custom dry EEG system that measures brainwaves during surfing. They partnered with Red Bull to use this technology on professional surfers to uncover the neurophysiological aspects of surfing. The dry EEG system is worn on the head like a swimming cap, and it allows for the measurement of brain activity in real-time during surfing. By studying the brainwaves of surfers during their best rides, researchers hope to understand what goes on in the brain during moments of flow and peak performance, and ultimately unlock the secrets to achieving that elusive state of “stoke”.

Predicting hypoxic hypoxia using machine learning and wearable sensors

In this study, wearable sensors and machine learning-based algorithms were used to predict hypoxia in-flight. The group used Wearable Sensing’s dry-EEG technology to collect sensor data from 85 participants during a two-phase study. Participants wore aviation flight masks, which regulated their oxygen intake while performing cognitive tests and simulated flying tasks. EEG data was collected and analyzed using principal component analysis and machine learning algorithms, including Naïve Bayes, decision tree, random forest, and neural network algorithms, to classify the data as normal or hypoxic. The results showed high sensitivity and specificity, indicating potential for developing a real-time, in-flight hypoxia detection system.

An experimental protocol for exploration of stress in an immersive VR scenario with EEG

This paper proposes a protocol for assessing stress using wearable sensing technology, including Electroencephalography (EEG), Electrocardiography (ECG), and the Perceived Stress Scale, in combination with a Virtual Reality phobia induction setting. Wearable Sensing’s dry EEG technology is used to measure brain activity and investigate functional brain connectivity associated with stress. The proposed protocol can be expanded with the incorporation of machine learning algorithms for automatic stress level classification.

Hardware

DSI Dry EEG

All of Wearable Sensing's Dry EEG systems can be utilized for Brain Computer Interfaces

Learn More

NeuSenW High-Density EEG

All of Wearable Sensing's Wet EEG systems can be utilized for Brain Computer Interfaces

Learn More

Software

QStates

Machine learning algorithms for cognitive state classification, such as mental workload, engagement, and fatigue

Learn More

3rd Party Compatible Software

List of compatible software, including Neurofeedback, BCI, EEG Analysis, SDK's, and more

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Publications

132 entries « ‹ 1 of 14 › »

2024

Rueda-Parra, S.; Hardesty, R. L.; Gemoets, D.; Hill, J.; Gupta, D.

Reliability testing of EEG spectral features in a robot-based arm movement task Conference

Society for Neuroscience 2024.

Abstract | Links | BibTeX

@conference{nokey,

title = {Reliability testing of EEG spectral features in a robot-based arm movement task},

author = {S. Rueda-Parra and R.L. Hardesty and D. Gemoets and J. Hill and D. Gupta},

url = {https://wearablesensing.com/wp-content/uploads/2024/07/Reliability-testing-of-EEG-spectral-features-in-a-robot-based-arm-movement-task_sfn2024.pdf},

year  = {2024},

date = {2024-10-05},

organization = {Society for Neuroscience},

abstract = {Introduction

We test the intersession reliability of kinematic and EEG low-beta (Lβ, 12 - 20 Hz) power features in a center-out motor task performed with the inMotion Armrehabilitation robot from Bionik labs. Integrating EEG data collection with robotic tasks aims to reduce trial and session movement variability, and to obtain precisemovement onset markers. We hypothesize that this setup would help reduce intersession variability and increase reliability, for potential use of these features inlongitudinal assessments.



Methods

Ten right-handed healthy individuals used the Bionik robot in two sessions (on average 40 days apart) to perform planar movements within a predefined workspace.Targets were situated 10 cm from the center at 4 cardinal positions. Movement and targets were displayed on a screen. Movement was initiated upon visual cue andtarget position maintained till subsequent cue. Targets were presented randomly. Each arm performed a block of 80 movements, with randomized block order. Sessiontwo kept individual block order. Study was IRB approved (1584762); informed consent was obtained.

EEG data (DSI-24, Wearable Sensing) were collected at 300 Hz with BCI2000, referenced to linked earlobes, and synchronized with Bionik’s positional data. Movementonsets were detected using inertial kinematic data with (Xsens, Movella). Lβ power was analyzed for epochs of 0 to 200 ms post-movement onset; expressed as apercentage change from trial baseline. Power distributions of this feature at contralateral hand motor regions (C3 and C4) were computed. Kinematic features (meanspeed and movement duration) were gathered from the robot.

Inter session distribution differences were evaluated at individual (Lβ) and group levels (kinematics and Lβ). Wilcoxon rank sum test was used for intra-individualcomparisons, and Wilcoxon signed rank for paired group comparisons. The group feature was the median Lβ power. Intersession agreement was quantified with theInter-Class Correlation (ICC) coefficient.



Results

Lβ power in contralateral motor regions show expected desynchronization. Group analysis shows excellent agreement between sessions for right-arm (ICC: Lβ = 0.92,Speed = 0.86, Duration = 0.71), and left-arm (ICC: Lβ = 0.90, Speed = 0.86, Duration = 0.83) movements. Individual and group differences were not significant for bothleft- and right-hand movements, supporting group intersession agreement.



Conclusion

This study demonstrates a setup with robust EEG and kinematic features across sessions in healthy individuals, providing some assurance of its reliability for use inlongitudinal assessments. We aim to test this on a larger cohort.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Introduction
We test the intersession reliability of kinematic and EEG low-beta (Lβ, 12 - 20 Hz) power features in a center-out motor task performed with the inMotion Armrehabilitation robot from Bionik labs. Integrating EEG data collection with robotic tasks aims to reduce trial and session movement variability, and to obtain precisemovement onset markers. We hypothesize that this setup would help reduce intersession variability and increase reliability, for potential use of these features inlongitudinal assessments.

Methods
Ten right-handed healthy individuals used the Bionik robot in two sessions (on average 40 days apart) to perform planar movements within a predefined workspace.Targets were situated 10 cm from the center at 4 cardinal positions. Movement and targets were displayed on a screen. Movement was initiated upon visual cue andtarget position maintained till subsequent cue. Targets were presented randomly. Each arm performed a block of 80 movements, with randomized block order. Sessiontwo kept individual block order. Study was IRB approved (1584762); informed consent was obtained.
EEG data (DSI-24, Wearable Sensing) were collected at 300 Hz with BCI2000, referenced to linked earlobes, and synchronized with Bionik’s positional data. Movementonsets were detected using inertial kinematic data with (Xsens, Movella). Lβ power was analyzed for epochs of 0 to 200 ms post-movement onset; expressed as apercentage change from trial baseline. Power distributions of this feature at contralateral hand motor regions (C3 and C4) were computed. Kinematic features (meanspeed and movement duration) were gathered from the robot.
Inter session distribution differences were evaluated at individual (Lβ) and group levels (kinematics and Lβ). Wilcoxon rank sum test was used for intra-individualcomparisons, and Wilcoxon signed rank for paired group comparisons. The group feature was the median Lβ power. Intersession agreement was quantified with theInter-Class Correlation (ICC) coefficient.

Results
Lβ power in contralateral motor regions show expected desynchronization. Group analysis shows excellent agreement between sessions for right-arm (ICC: Lβ = 0.92,Speed = 0.86, Duration = 0.71), and left-arm (ICC: Lβ = 0.90, Speed = 0.86, Duration = 0.83) movements. Individual and group differences were not significant for bothleft- and right-hand movements, supporting group intersession agreement.

Conclusion
This study demonstrates a setup with robust EEG and kinematic features across sessions in healthy individuals, providing some assurance of its reliability for use inlongitudinal assessments. We aim to test this on a larger cohort.

Gupta, D.; Brangaccio, J. A.; Mojtabavi, H.; Wolpaw, J.; Hill, J.

Single-trial decoding of somatosensory evoked potentials: Effect of stimulation frequency Conference

Society for Neuroscience 2024.

Abstract | Links | BibTeX

@conference{nokey,

title = {Single-trial decoding of somatosensory evoked potentials: Effect of stimulation frequency},

author = {D. Gupta and J.A. Brangaccio and H. Mojtabavi and J. Wolpaw and J. Hill},

url = {https://wearablesensing.com/wp-content/uploads/2024/07/cOASIS-The-Online-Abstract-Submission-System.pdf},

year  = {2024},

date = {2024-10-05},

urldate = {2024-10-05},

organization = {Society for Neuroscience},

abstract = {Objective: Single-trial decoding of somatosensory evoked potentials (SEPs) is important for developing Brain Computer Interface applications for rehabilitation post brain injury, However, it is unclear how SEP decoding is affected by stimulation parameters such as the stimulation frequency, We assess this with tibial nerve electrical stimulation at frequencies of 0.2 Hz, 1 Hz and 2Hz. Method: Nine healthy people (50.4+18.8y, 6m/3f) participated with informed consent (IRB #1584762), Referential electroencephalography (EEG) was acquired at 300 Hz (DSI-24, Wearable Sensing; BCI2000). Bipolar electromyography was measured from the soleus muscle (3200 Hz, AMT-8; EPOCS software). Biphasic 1-ms electrical stimuli were delivered to tibial nerve with a constant current stimulator, controlled with our EPOCS software. At each stimulation frequency, the soleus M-wave and Hoffman-reflex recruitment curves were obtained, followed by SEP measurement (75 trials), at a current intensity that elicited an M-wave of 10-20% Mmax. The 3 stimulation frequency blocks were repeated after a short break, presented randomly. EEG was notch and bandpass filtered (0.2 - 40 Hz) and denoised with independent component analysis, Preprocessed data were epoched (-50 to 400 ms); noisy epochs were removed using trial statistics. The SEP was obtained by averaging baseline-corrected epochs. The Friedman Test was used for repeated measure comparisons. Single trial classification was assessed with linear discriminant analysis (LDA) and 5-fold cross-validation. Classification performance was assessed with AUC (ROC curves), Decoding generalization was assessed by applying LDA models from set-1 recordings, to predict the SEPS in set 2, Results: SEP N70 latencies remained similar across stimulation frequencies (p>0.05), while the N70 peak amplitude was significantly different (p=0.0084); higher stimulation frequency elicited a smaller N70 peak. The N70 classification accuracy was 0.89, 0.79 and 0.80 for 0.2 Hz, 1 Hz and 2 Hz respectively. Generalization AUC scores were 0.81, 0.76 and 0.77 for 0.2 Hz, 1 Hz and 2 Hz, respectively. Conclusion: Results show an excellent classification accuracy for SEP N70, elicited by tibial nerve stimulation at 0,2Hz, with a slight decrease in accuracy at higher stimulation frequencies. This may be attributed to attenuated SEP N70 at higher frequencies, possibly due to desensitization. Generalization scores show an expected decrease of accuracy at all frequencies, with the largest decrease at 0.2 Hz. Most importantly, all three frequencies appear to be able to support single-trial SEP decoding and may be usable for an SEP-based brain computer interface.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Objective: Single-trial decoding of somatosensory evoked potentials (SEPs) is important for developing Brain Computer Interface applications for rehabilitation post brain injury, However, it is unclear how SEP decoding is affected by stimulation parameters such as the stimulation frequency, We assess this with tibial nerve electrical stimulation at frequencies of 0.2 Hz, 1 Hz and 2Hz. Method: Nine healthy people (50.4+18.8y, 6m/3f) participated with informed consent (IRB #1584762), Referential electroencephalography (EEG) was acquired at 300 Hz (DSI-24, Wearable Sensing; BCI2000). Bipolar electromyography was measured from the soleus muscle (3200 Hz, AMT-8; EPOCS software). Biphasic 1-ms electrical stimuli were delivered to tibial nerve with a constant current stimulator, controlled with our EPOCS software. At each stimulation frequency, the soleus M-wave and Hoffman-reflex recruitment curves were obtained, followed by SEP measurement (75 trials), at a current intensity that elicited an M-wave of 10-20% Mmax. The 3 stimulation frequency blocks were repeated after a short break, presented randomly. EEG was notch and bandpass filtered (0.2 - 40 Hz) and denoised with independent component analysis, Preprocessed data were epoched (-50 to 400 ms); noisy epochs were removed using trial statistics. The SEP was obtained by averaging baseline-corrected epochs. The Friedman Test was used for repeated measure comparisons. Single trial classification was assessed with linear discriminant analysis (LDA) and 5-fold cross-validation. Classification performance was assessed with AUC (ROC curves), Decoding generalization was assessed by applying LDA models from set-1 recordings, to predict the SEPS in set 2, Results: SEP N70 latencies remained similar across stimulation frequencies (p>0.05), while the N70 peak amplitude was significantly different (p=0.0084); higher stimulation frequency elicited a smaller N70 peak. The N70 classification accuracy was 0.89, 0.79 and 0.80 for 0.2 Hz, 1 Hz and 2 Hz respectively. Generalization AUC scores were 0.81, 0.76 and 0.77 for 0.2 Hz, 1 Hz and 2 Hz, respectively. Conclusion: Results show an excellent classification accuracy for SEP N70, elicited by tibial nerve stimulation at 0,2Hz, with a slight decrease in accuracy at higher stimulation frequencies. This may be attributed to attenuated SEP N70 at higher frequencies, possibly due to desensitization. Generalization scores show an expected decrease of accuracy at all frequencies, with the largest decrease at 0.2 Hz. Most importantly, all three frequencies appear to be able to support single-trial SEP decoding and may be usable for an SEP-based brain computer interface.

Moon, JaeYoung; Cho, EunHye; Jo, Yeabon; Kim, KyungJoong; Song, Eunsung

Investigating the Effect of Emotional Matching between Game and Background Music on Game Experience in a Valence–Arousal Space Journal Article

In: IEEE Transactions on Games, pp. 1-20, 2024.

Abstract | Links | BibTeX

Oikonomou, Vangelis P; Geordiadis, Kostas; Kalaganis, Fotis P; Nikolopoulos, Spiros; Kompatsiaris, Ioannis

Prediction of Successful Memory Formation during Audiovisual advertising using EEG signals Conference

2024 IEEE Conference on Artificial Intelligence (CAI) 2024.

Abstract | Links | BibTeX

Kim, Sanghee; Ryu, Jihye; Lee, Yujeong; Lee, Kweonhyoung

Exploring the Influence of Subjective Thermal Perception on Electroencephalogram Characteristics Based on Prior Thermal Experience Journal Article

In: Building and Environment, pp. 111719, 2024.

Abstract | Links | BibTeX

@article{kim2024exploring,

title = {Exploring the Influence of Subjective Thermal Perception on Electroencephalogram Characteristics Based on Prior Thermal Experience},

author = {Sanghee Kim and Jihye Ryu and Yujeong Lee and Kweonhyoung Lee},

doi = {https://doi.org/10.1016/j.buildenv.2024.111719},

year  = {2024},

date = {2024-06-04},

urldate = {2024-01-01},

journal = {Building and Environment},

pages = {111719},

publisher = {Elsevier},

abstract = {Achieving thermal comfort for occupants in a dynamic environment that reflects their previous thermal experiences is essential for enhancing satisfaction. Current models do not fully consider these dynamic elements, thus limiting advancements in occupants' thermal comfort. This study aims to delineate the characteristics of electroencephalogram (EEG) responses aligned with subjective thermal perception in various thermal environment scenarios, inclusive of past thermal experiences. It focuses on identifying EEG indices, brain regions, and temporal markers that can distinguish between different thermal perceptions. The study involves sixteen male participants who undergo three thermal experience scenarios: predicted mean vote (PMV) 0 → PMV +2, PMV +2 → PMV –2, and PMV -2 → PMV +2. Our results highlight the sensitivity of the F3 channel in the left frontal lobe to thermal environments. The study identifies a pivotal time frame of 6 min after the experiment's start as significant for differentiating thermal perception groups. Additionally, it reveals that the EEG indices reacting to thermal changes vary depending on the participants' previous thermal experiences. These findings indicate that physiological signals, particularly EEG responses, can serve as effective biomarkers for distinguishing thermal perceptions in diverse thermal experience scenarios. This research emphasizes the need to integrate a variety of thermal environment scenarios, considering previous thermal experiences, into personal comfort models for more effective and tailored thermal comfort solutions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Cheng, Chia-Hsiung; Hsieh, Yu-Wei; Chang, Chiung-Chih; Hsiao, Fu-Jung; Chen, Li-Fen; Wang, Pei-Ning

Effects of 6-Month Combined Physical Exercise and Cognitive Training on Neuropsychological and Neurophysiological Function in Older Adults with Subjective Cognitive Decline: A Randomized Controlled Trial Journal Article

In: Journal of Alzheimer's Disease, no. Preprint, pp. 1–18, 2024.

Abstract | Links | BibTeX

@article{cheng2024effects,

title = {Effects of 6-Month Combined Physical Exercise and Cognitive Training on Neuropsychological and Neurophysiological Function in Older Adults with Subjective Cognitive Decline: A Randomized Controlled Trial},

author = {Chia-Hsiung Cheng and Yu-Wei Hsieh and Chiung-Chih Chang and Fu-Jung Hsiao and Li-Fen Chen and Pei-Ning Wang},

doi = {10.3233/JAD-231257},

year  = {2024},

date = {2024-06-04},

urldate = {2024-01-01},

journal = {Journal of Alzheimer's Disease},

number = {Preprint},

pages = {1–18},

publisher = {IOS Press},

abstract = {Background: Multidomain intervention may delay or ameliorate cognitive decline in older adults at risk of Alzheimer's disease, particularly in the memory and inhibitory functions. However, no study systematically investigates the changes of brain function in cognitively-normal elderly with subjective cognitive decline (SCD) when they receive multidomain intervention.



Objective: We aimed to examine whether a multidomain intervention could improve neuropsychological function and neurophysiological activities related to memory and inhibitory function in SCD subjects.



Methods: Eight clusters with a total of 50 community-dwelling SCD older adults were single-blind, randomized into intervention group, which received physical and cognitive training, or control group, which received treatment as usual. For the neuropsychological function, a composite Z score from six cognitive tests was calculated and compared between two groups. For the neurophysiological activities, event-related potentials (ERPs) of memory function, including mismatch negativity (MMN) and memory-P3, as well as ERPs of inhibitory function, including sensory gating (SG) and inhibition-P3, were measured. Assessments were performed at baseline (T1), end of the intervention (T2), and 6 months after T2 (T3).



Results: For the neuropsychological function, the effect was not observed after the intervention. For the neurophysiological activities, improved MMN responses of ΔT2-T1 were observed in the intervention group versus the control group. The multidomain intervention produced a sustained effect on memory-P3 latencies of ΔT3-T1. However, there were no significant differences in changes of SG and inhibition-P3 between intervention and control groups.



Conclusions: While not impactful on neuropsychological function, multidomain intervention enhances specific neurophysiological activities associated with memory function.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Chiossi, Francesco; Ou, Changkun; Mayer, Sven

Optimizing Visual Complexity for Physiologically-Adaptive VR Systems: Evaluating a Multimodal Dataset using EDA, ECG and EEG Features Conference

2024.

Abstract | Links | BibTeX

Jeong, Chang Hyeon; Lim, Hyunmi; Lee, Jiye; Lee, Hye Sun; Ku, Jeonghun; Kang, Youn Joo

Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke Journal Article

In: Frontiers in Neuroscience, vol. 18, pp. 1373589, 2024.

Abstract | Links | BibTeX

@article{jeong2024attentional,

title = {Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke},

author = {Chang Hyeon Jeong and Hyunmi Lim and Jiye Lee and Hye Sun Lee and Jeonghun Ku and Youn Joo Kang},

doi = {10.3389/fnins.2024.1373589},

year  = {2024},

date = {2024-03-18},

urldate = {2024-03-18},

journal = {Frontiers in Neuroscience},

volume = {18},

pages = {1373589},

publisher = {Frontiers},

abstract = {Introduction: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity. Materials and methods: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Introduction: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity. Materials and methods: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.

Li, Jian; Masullo, Massimiliano; Maffei, Luigi; Pascale, Aniello; Chau, Chi-kwan; Lin, Minqi

Improving informational-attentional masking of water sound on traffic noise by spatial variation settings: An in situ study with brain activity measurements Journal Article

In: Applied Acoustics, vol. 218, pp. 109904, 2024.

Abstract | Links | BibTeX

@article{li2024improving,

title = {Improving informational-attentional masking of water sound on traffic noise by spatial variation settings: An in situ study with brain activity measurements},

author = {Jian Li and Massimiliano Masullo and Luigi Maffei and Aniello Pascale and Chi-kwan Chau and Minqi Lin},

doi = {https://doi.org/10.1016/j.apacoust.2024.109904},

year  = {2024},

date = {2024-02-09},

urldate = {2024-01-01},

journal = {Applied Acoustics},

volume = {218},

pages = {109904},

publisher = {Elsevier},

abstract = {According with soundscape strategies to improve the perception of the sound environment, laboratory studies have proven that introducing water sounds into urban spaces can be both an effective strategy for the informational-attentional masking of road traffic noise, and restorativeness creation. To extend previous laboratory findings and test the effectiveness and applicability of different spatial variations of water sounds in urban parks, a sound installation was prepared, and an experiment was conducted. Three different position-varied water-sound sequences were augmented into an existing University campus green park through surround sound design method with four Bluetooth loudspeakers. The mental effects and attention process were assessed by analyzing the EEG signals including aperiodic, oscillatory components and sensor-level functional connectivity, along with psychological scales. The water sounds played in-situ, brought more visual processing related to spatial attention and stimulus-driven salience. And the changes in the alpha band and the related theta/alpha ratio among four conditions showed more relaxation state induced by the introduction of water sounds, consistent with the positive effects on emotion saliency and perceived restorativeness. Moreover, different spatial variations of water sounds, especially for the two-position switching setting, modulated the activity of the attentional network related to the restoration process via the alpha-theta synchronization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Kim, Sanghee; Ryu, Jihye; Lee, Yujeong; Park, Hyejin; Lee, Kweonhyoung

Methods for Selecting Design Alternatives through Integrated Analysis of Energy Performance of Buildings and the Physiological Responses of Occupants Journal Article

In: Buildings, vol. 14, no. 1, pp. 237, 2024.

Abstract | Links | BibTeX

@article{kim2024methods,

title = {Methods for Selecting Design Alternatives through Integrated Analysis of Energy Performance of Buildings and the Physiological Responses of Occupants},

author = {Sanghee Kim and Jihye Ryu and Yujeong Lee and Hyejin Park and Kweonhyoung Lee},

doi = {https://doi.org/10.3390/buildings14010237},

year  = {2024},

date = {2024-01-15},

urldate = {2024-01-01},

journal = {Buildings},

volume = {14},

number = {1},

pages = {237},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {We propose a technique that allows designers to develop energy-efficient buildings focused on occupants from the early design stage. The technique integrates the physiological responses of occupants and the energy performance of buildings. Among the architectural design elements, we considered the aspect ratio, ceiling height, and window-to-wall ratio as design variables and created 30 design alternatives for a single-occupancy room in a postpartum care center. These design alternatives were recreated in virtual reality, allowing 33 female participants to immerse themselves in the designed rooms. During the experiment, we collected electroencephalography (EEG) data from the participants. Furthermore, we used DesignBuilder to simulate 30 design alternatives and calculated the primary energy consumption per unit area for each alternative. By integrating the EEG data and energy performance analysis, we identified the design alternative among the 30 options that positively influenced the physiological responses of occupants while also being energy efficient. The selected alternative was designed with an aspect ratio of 1:1.6, a ceiling height of 2.3 m, and a window-to-wall ratio of 60%. This research represents a creative exploration that demonstrates how studies combining human physiological responses and architecture can evolve through integration with other subjects. Our findings provide a robust framework to explore the relationship between physiological responses and energy optimization for detailed architectural design elements.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

132 entries « ‹ 1 of 14 › »

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Research

Discover the Why of the Brain

Applications

What Causes the "Stoke" in Surfing?

Predicting hypoxic hypoxia using machine learning and wearable sensors

An experimental protocol for exploration of stress in an immersive VR scenario with EEG

Hardware

DSI Dry EEG

NeuSenW High-Density EEG

Software

QStates

3rd Party Compatible Software

Publications

2024

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