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.
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”.
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.
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.
Li, Jian; Masullo, Massimiliano; Maffei, Luigi; Pascale, Aniello; Chau, Chi-kwan; Lin, Minqi
In: Applied Acoustics, vol. 218, pp. 109904, 2024.
@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},
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}
Kim, Sanghee; Ryu, Jihye; Lee, Yujeong; Park, Hyejin; Lee, Kweonhyoung
In: Buildings, vol. 14, no. 1, pp. 237, 2024.
@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}
}
Klee, Daniel; Memmott, Tab; Oken, Barry
In: Signals, vol. 5, no. 1, pp. 18–39, 2024.
@article{klee2024effect,
title = {The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain–Computer Interface Rapid Serial Visual Presentation Paradigm},
author = {Daniel Klee and Tab Memmott and Barry Oken},
doi = {https://doi.org/10.3390/signals5010002},
year = {2024},
date = {2024-01-09},
urldate = {2024-01-01},
journal = {Signals},
volume = {5},
number = {1},
pages = {18–39},
publisher = {MDPI},
abstract = {Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain–computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or “jittered” stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Park, Jaeyoung; Wang, Soyoung; Lee, Seungji; Seo, Seungbeom; Lee, Nayoung; Kim, Seongcheol
Viewer Emotional Response to Webtoon-Based Drama: An EEG Analysis Journal Article
In: International Journal of Human–Computer Interaction, pp. 1–15, 2023.
@article{park2023viewer,
title = {Viewer Emotional Response to Webtoon-Based Drama: An EEG Analysis},
author = {Jaeyoung Park and Soyoung Wang and Seungji Lee and Seungbeom Seo and Nayoung Lee and Seongcheol Kim},
doi = {https://doi.org/10.1080/10447318.2023.2285647},
year = {2023},
date = {2023-11-29},
urldate = {2023-01-01},
journal = {International Journal of Human–Computer Interaction},
pages = {1–15},
publisher = {Taylor & Francis},
abstract = {Amidst entertainment market uncertainties, cross-medium content extension has emerged as a powerful strategy. Webtoons, digital cartoons, stand out as significant resources. However, limited research has delved into effective strategies for extending these narratives. Acknowledging the crucial role of evoking viewer emotions in content success, this study investigates viewer emotional responses. Departing from conventional methods, we employ neuroscientific measurement—specifically, electroencephalography (EEG)—to capture real-time viewer emotions during content consumption, assessing valence and arousal. We examine the impact of webtoon-drama similarity on viewer emotions and scene attributes that heighten emotional responses. By integrating EEG data, interview insights, and scene analysis, our findings underscore intensified emotions when drama scenes mirror webtoon elements, particularly in sets and directing. Effective replication relies on drama-specific attributes such as choreography, original soundtracks, and casting. This study contributes academically by using EEG to evaluate webtoons’ value as original sources and practically by offering concrete webtoon extension strategy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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}
}
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}
}
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}
}
Chiossi, Francesco; Turgut, Yagiz; Welsch, Robin; Mayer, Sven
Adapting Visual Complexity Based on Electrodermal Activity Improves Working Memory Performance in Virtual Reality Journal Article
In: Proc. ACM Hum.-Comput. Interact, vol. 7, 2023.
@article{chiossi2023adapting,
title = {Adapting Visual Complexity Based on Electrodermal Activity Improves Working Memory Performance in Virtual Reality},
author = {Francesco Chiossi and Yagiz Turgut and Robin Welsch and Sven Mayer},
url = {https://www.researchgate.net/profile/Francesco-Chiossi/publication/371539688_Adapting_Visual_Complexity_Based_on_Electrodermal_Activity_Improves_Working_Memory_Performance_in_Virtual_Reality/links/648983619bc5e436682f0b97/Adapting-Visual-Complexity-Based-on-Electrodermal-Activity-Improves-Working-Memory-Performance-in-Virtual-Reality.pdf},
year = {2023},
date = {2023-09-02},
urldate = {2023-01-01},
journal = {Proc. ACM Hum.-Comput. Interact},
volume = {7},
abstract = {Biocybernetic loops encompass users’ state detection and system adaptation based on physiological signals. Current adaptive systems limit the adaptation to task features such as task difficulty or multitasking demands. However, virtual reality allows the manipulation of task-irrelevant elements in the environment. We present a physiologically adaptive system that adjusts the virtual environment based on physiological arousal, i.e., electrodermal activity. We conducted a user study with our adaptive system in social virtual reality to verify improved performance. Here, participants completed an n back task, and we adapted the visual complexity of the environment by changing the number of non-player characters. Our results show that an adaptive virtual reality can control users’ comfort, performance, and workload by adapting the visual complexity based on physiological arousal. Thus, our physiologically adaptive system improves task performance and perceived workload. Finally, we embed our findings in physiological computing and discuss applications in various scenarios},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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