@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{mahdid2020assessing,

title = {Assessing the Quality of Wearable EEG Systems Using Functional Connectivity},

author = {Yacine Mahdid and Uncheol Lee and Stefanie Blain-Moraes},

doi = {10.1109/ACCESS.2020.3033472},

issn = {2169-3536},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {193214--193225},

publisher = {IEEE},

abstract = {Assessing the data quality of wearable electroencephalogram (EEG) systems is critical to collecting reliable neurophysiological data in non-laboratory environments. To date, measures of signal quality and spectral characteristics have been used to characterize wearable EEG systems. We demonstrate that these traditional measures do not provide fine-grained differentiation between the performance of four popular wearable EEG systems (the Epoc+, OpenBCI, DSI-24 and Quick-30 Dry EEG). Using two computationally inexpensive metrics of undirected functional connectivity (phase lag index) and directed functional connectivity (directed phase lag index), we compare the integrity of the phase relationships captured by wearable systems to those recorded from a high-density research-grade EEG system (Electrical Geodesics Inc). Our results demonstrate that functional connectivity analyses provide additional discriminatory information about wearable EEG systems, with clear differentiation of the cosine similarity between research-grade functional connectivity patterns and those generated by each wearable system. We provide a freely available Matlab toolbox containing all metrics described in this paper such that researchers and non-experts interested in wearable EEG systems can easily assess the quality of systems not characterized in this study, thus advancing the translation of EEG research into non-laboratory settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}