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.
Lim, Hyunmi; Ku, Jeonghun
Effect of repetitive neurofeedback training on brain activation during hand exercise Conference
2021 9th International Winter Conference on Brain-Computer Interface (BCI), IEEE 2021, ISBN: 978-1-7281-8486-9.
@conference{lim2021effect,
title = {Effect of repetitive neurofeedback training on brain activation during hand exercise},
author = {Hyunmi Lim and Jeonghun Ku},
doi = {10.1109/BCI51272.2021.9385315},
isbn = {978-1-7281-8486-9},
year = {2021},
date = {2021-04-05},
booktitle = {2021 9th International Winter Conference on Brain-Computer Interface (BCI)},
pages = {1--3},
organization = {IEEE},
abstract = {In this study, we examined that neurofeedback training encouraging to make mu suppression over the motor cortex would effect on the brain activation of the motor cortex while performing hand movements. To investigate the effects of training with neurofeedback, we analyzed the pattern changes of the amount of the mu suppression during real hand movements just after every neurofeedback training. The healthy subjects were trained to increase the motor cortex activity by using motor imagery, specifically maximizing the difference of the mu power between C3 and C4 during neurofeedback training. We have observed that the changes of the mu suppression over the motor cortex become stronger as the neurofeedback training was repeated. These findings further suggest that motor imagery training using neurofeedback can be applied to patients with stroke or chronic neurological disorders.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Eldeeb, Safaa; Susam, Busra T; Akcakaya, Murat; Conner, Caitlin M; White, Susan W; Mazefsky, Carla A
Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD Journal Article
In: Scientific Reports, vol. 11, no. 1, pp. 1–13, 2021.
@article{eldeeb2021trial,
title = {Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD},
author = {Safaa Eldeeb and Busra T Susam and Murat Akcakaya and Caitlin M Conner and Susan W White and Carla A Mazefsky},
url = {https://www.nature.com/articles/s41598-021-85362-8},
year = {2021},
date = {2021-03-16},
journal = {Scientific Reports},
volume = {11},
number = {1},
pages = {1--13},
publisher = {Nature Publishing Group},
abstract = {Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Memmott, Tab; Koçanaoğullari, Aziz; Lawhead, Matthew; Klee, Daniel; Dudy, Shiran; Fried-Oken, Melanie; Oken, Barry
BciPy: brain--computer interface software in Python Journal Article
In: Brain-Computer Interfaces, pp. 1-18, 2021.
@article{memmott2021bcipy,
title = {BciPy: brain--computer interface software in Python},
author = {Tab Memmott and Aziz Koçanaoğullari and Matthew Lawhead and Daniel Klee and Shiran Dudy and Melanie Fried-Oken and Barry Oken},
doi = {https://doi.org/10.1080/2326263X.2021.1878727},
year = {2021},
date = {2021-02-02},
journal = {Brain-Computer Interfaces},
pages = {1-18},
publisher = {Taylor & Francis},
abstract = {There are high technological and software demands associated with conducting Brain–Computer Interface (BCI) research. In order to accelerate the development and accessibility of BCIs, it is worthwhile to focus on open-source and community desired tooling. Python, a prominent computer language, has emerged as a language of choice for many research and engineering purposes. In this article, BciPy, an open-source, Python-based software for conducting BCI research is presented. It was developed with a focus on restoring communication using Event-Related Potential (ERP) spelling interfaces; however, it may be used for other non-spelling and non-ERP BCI paradigms. Major modules in this system include support for data acquisition, data queries, stimuli presentation, signal processing, signal viewing and modeling, language modeling, task building, and a simple Graphical User Interface (GUI).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rahimi-Nasrabadi, Hamed; Jin, Jianzhong; Mazade, Reece; Pons, Carmen; Najafian, Sohrab; Alonso, Jose-Manuel
Image luminance changes contrast sensitivity in visual cortex Journal Article
In: Cell reports, vol. 34, no. 5, pp. 108692, 2021.
@article{rahimi2021image,
title = {Image luminance changes contrast sensitivity in visual cortex},
author = {Hamed Rahimi-Nasrabadi and Jianzhong Jin and Reece Mazade and Carmen Pons and Sohrab Najafian and Jose-Manuel Alonso},
doi = {https://doi.org/10.1016/j.celrep.2021.108692},
year = {2021},
date = {2021-02-02},
journal = {Cell reports},
volume = {34},
number = {5},
pages = {108692},
publisher = {Elsevier},
abstract = {Accurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neilson, Brittany N; Phillips, Jeffrey B; Snider, Dallas H; Drollinger, Sabrina M; Linnville, Steven E; Mayes, Ryan S
A Data-Driven Approach to Aid in Understanding Brainwave Activity During Hypoxia Conference
2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID), IEEE IEEE, Miramar Beach, FL, USA, 2020, ISBN: 978-1-7281-5890-7.
@conference{neilson2020data,
title = {A Data-Driven Approach to Aid in Understanding Brainwave Activity During Hypoxia},
author = {Brittany N Neilson and Jeffrey B Phillips and Dallas H Snider and Sabrina M Drollinger and Steven E Linnville and Ryan S Mayes},
doi = {10.1109/RAPID49481.2020.9195700},
isbn = {978-1-7281-5890-7},
year = {2020},
date = {2020-09-14},
booktitle = {2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)},
pages = {1--2},
publisher = {IEEE},
address = {Miramar Beach, FL, USA},
organization = {IEEE},
abstract = {Changes in brainwave activity have been associated with hypoxia, but the literature is inconsistent. Twenty-five participants were subjected to normobaric hypoxia while undergoing a variety of cognitive tasks. The detected differences in brain activity between normal and hypoxic conditions are presented.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Haar Millo, S; Faisal, A
Brain Activity Reveals Multiple Motor-Learning Mechanisms in a Real-World Task Journal Article
In: Frontiers in Human Neuroscience, vol. 14, pp. 354, 2020, ISBN: 1662-5161.
@article{haarbrain,
title = {Brain Activity Reveals Multiple Motor-Learning Mechanisms in a Real-World Task},
author = {Haar Millo, S and Faisal, A},
doi = {10.3389/fnhum.2020.00354},
isbn = {1662-5161},
year = {2020},
date = {2020-09-02},
journal = {Frontiers in Human Neuroscience},
volume = {14},
pages = {354},
publisher = {Frontiers Media},
abstract = {Many recent studies found signatures of motor learning in neural beta oscillations (13–30 Hz), and specifically in the post-movement beta rebound (PMBR). All these studies were in controlled laboratory-tasks in which the task designed to induce the studied learning mechanism. Interestingly, these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based and reward-based tasks (increase vs. decrease, respectively). Here, we explored the PMBR dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. Our EEG recordings highlight the opposing dynamics of PMBR magnitudes (increase vs. decrease) between different subjects performing the same task. The groups of subjects, defined by their neural dynamics, also showed behavioral differences expected for different learning mechanisms. Our results suggest that when faced with the complexity of the real-world different subjects might use different learning mechanisms for the same complex task. We speculate that all subjects combine multi-modal mechanisms of learning, but different subjects have different predominant learning mechanisms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kim, Young-June; Park, Jin-Hong; Cho, Young-Suk; Kim, Keum-Sook
In: Journal of Convergence for Information Technology, vol. 10, no. 8, pp. 203–212, 2020.
@article{kim2020effect,
title = {The Effect of Cognitive Rehabilitation Program Using Virtual Reality (VR) Contents on Cognitive function, Depression, Upper Extremity Function and Activities of Daily Living in the Elderly},
author = {Young-June Kim and Jin-Hong Park and Young-Suk Cho and Keum-Sook Kim},
url = {https://www.koreascience.or.kr/article/JAKO202024852036461.page},
year = {2020},
date = {2020-08-28},
journal = {Journal of Convergence for Information Technology},
volume = {10},
number = {8},
pages = {203--212},
publisher = {Convergence Society for SMB},
abstract = {The purpose of this study was to investigate the effects of cognitive rehabilitation programs using Virtual Reality(VR) content on the daily living abilities such as cognitive abilities, depression, and upper extremity functions of the elderly. The study group analyzed the effectiveness by separating the experimental group, which is the virtual reality cognitive rehabilitation application group, and the control group, the universal cognitive stimulation program application group. As a result of the study, the MMSE-K score improved by 13.0% in the experimental group and 2.3% in the control group. The improvement in each area of the experimental group was found to be 3.1% MBI, 7.1% MFT(Rt.), 3.5% MFT(Lt.), and 25.4% K-GDS. As a result of comparing the pre-post score change between each group, there was a significant difference between groups in daily living ability (p<.001) and MFT(Rt.)(p<.01). In addition, as a result of comparing the changes in absolute alpha waves to confirm the degree of depression through brain waves, there was no statistically significant difference. However, in the experimental group, it was confirmed that the average value increased to a positive value. This study is an experiment to verify the effectiveness of the cognitive rehabilitation program using virtual reality contents, and suggests a new intervention method to maintain and improve the daily life ability, cognitive function, depression and upper extremity function of the elderly.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lim, Hyunmi; Kim, Won-Seok; Ku, Jeonghun
Transcranial Direct Current Stimulation Effect on Virtual Hand Illusion Journal Article
In: Cyberpsychology, Behavior, and Social Networking, vol. 23, no. 8, pp. 541–549, 2020.
@article{lim2020transcranial,
title = {Transcranial Direct Current Stimulation Effect on Virtual Hand Illusion},
author = {Hyunmi Lim and Won-Seok Kim and Jeonghun Ku},
doi = {https://doi.org/10.1089/cyber.2019.0741},
year = {2020},
date = {2020-08-04},
urldate = {2020-08-04},
journal = {Cyberpsychology, Behavior, and Social Networking},
volume = {23},
number = {8},
pages = {541--549},
publisher = {Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…},
abstract = {Virtual reality (VR) is effectively used to evoke the mirror illusion, and transcranial direct current stimulation (tDCS) synergistically facilitates this illusion. This study investigated whether a mirror virtual hand illusion (MVHI) induced by an immersive, first-person-perspective, virtual mirror system could be modulated by tDCS of the primary motor cortex. Fourteen healthy adults (average age 21.86 years ±0.47, seven men and seven women) participated in this study, and they experienced VR with and without tDCS—the tDCS and sham conditions, each of which takes ∼30 minutes—on separate days to allow the washout of the tDCS effect. While experiencing VR, the movements of the virtual left hand reflected the flexion and extension of the real right hand. Subsequently, electroencephalogram was recorded, the magnitude of the proprioceptive shift was measured, and the participants provided responses to a questionnaire regarding hand ownership. A significant difference in the proprioceptive shift was observed between the tDCS and sham conditions. In addition, there was significant suppression of the mu power in Pz, and augmentation of the beta power in the Pz, P4, O1, and O2 channels. The difference in proprioceptive deviation between the two conditions showed significant negative correlation with mu suppression over the left frontal lobe in the tDCS condition. Finally, the question “I felt that the virtual hand was my own hand” received a significantly higher score under the tDCS condition. In short, applying tDCS over the motor cortex facilitates the MVHI by activating the attentional network over the parietal and frontal lobes such that the MVHI induces more proprioceptive drift, which suggests that the combination of VR and tDCS can enhance the immersive effect in VR. This result provides better support for the use of the MVHI paradigm in combination with tDCS for recovery from illnesses such as stroke.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Son, Ji Eun; Choi, Hyoseon; Lim, Hyunmi; Ku, Jeonghun
In: Technology and Health Care, vol. 28, no. S1, pp. 509-519, 2020.
@article{son2020development,
title = {Development of a flickering action video based steady state visual evoked potential triggered brain computer interface-functional electrical stimulation for a rehabilitative action observation game},
author = {Ji Eun Son and Hyoseon Choi and Hyunmi Lim and Jeonghun Ku},
editor = {Severin P. Schwarzacher and Carlos Gómez},
doi = {10.3233/THC-209051},
year = {2020},
date = {2020-06-04},
journal = {Technology and Health Care},
volume = {28},
number = {S1},
pages = {509-519},
publisher = {IOS Press},
abstract = {BACKGROUND:
This study focused on developing an upper limb rehabilitation program. In this regard, a steady state visual evoked potential (SSVEP) triggered brain computer interface (BCI)-functional electrical stimulation (FES) based action observation game featuring a flickering action video was designed.
OBJECTIVE:
In particular, the synergetic effect of the game was investigated by combining the action observation paradigm with BCI based FES.
METHODS:
The BCI-FES system was contrasted under two conditions: with flickering action video and flickering noise video. In this regard, 11 right-handed subjects aged between 22–27 years were recruited. The differences in brain activation in response to the two conditions were examined.
RESULTS:
The results indicate that T3 and P3 channels exhibited greater Mu suppression in 8–13 Hz for the action video than the noise video. Furthermore, T4, C4, and P4 channels indicated augmented high beta (21–30 Hz) for the action in contrast to the noise video. Finally, T4 indicated suppressed low beta (14–20 Hz) for the action video in contrast to the noise video.
CONCLUSION:
The flickering action video based BCI-FES system induced a more synergetic effect on cortical activation than the flickering noise based system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wang, Jiahui; Antonenko, Pavlo; Keil, Andreas; Dawson, Kara
Converging subjective and psychophysiological measures of cognitive load to study the effects of instructor-present video Journal Article
In: Mind, Brain, and Education, vol. 14, no. 3, pp. 279–291, 2020.
@article{wang2020converging,
title = {Converging subjective and psychophysiological measures of cognitive load to study the effects of instructor-present video},
author = {Jiahui Wang and Pavlo Antonenko and Andreas Keil and Kara Dawson},
doi = {https://doi.org/10.1111/mbe.12239},
year = {2020},
date = {2020-03-30},
urldate = {2020-01-01},
journal = {Mind, Brain, and Education},
volume = {14},
number = {3},
pages = {279--291},
publisher = {Wiley Online Library},
abstract = {Many online videos feature an instructor on the screen to improve learners' engagement; however, the influence of this design on learners' cognitive load is underexplored. This study investigates the effects of instructor presence on learners' processing of information using both subjective and psychophysiological measures of cognitive load. Sixty university students watched a statistics instructional video either with or without instructor presence, while the spontaneous electrical activity of their brain was recorded using electroencephalography (EEG). At the conclusion of the video, they also self-reported overall load, intrinsic load, extraneous load, and germane load they experienced during the video. Learning from the video was assessed via tests of retention and transfer. Results suggested the instructor-present video improved learners' ability to transfer information and was associated with a lower self-reported intrinsic and extraneous load. Event-related changes in theta band activity also indicated lower cognitive load with instructor-present video.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}