@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}

}

@article{islam2020eeg,

title = {EEG mobility artifact removal for ambulatory epileptic seizure prediction applications},

author = {Md Shafiqul Islam and Ahmad M El-Hajj and Hussein Alawieh and Zaher Dawy and Nabil Abbas and Jamil El-Imad},

doi = {https://doi.org/10.1016/j.bspc.2019.101638},

issn = {1746-8094},

year  = {2020},

date = {2020-01-01},

journal = {Biomedical Signal Processing and Control},

volume = {55},

pages = {101638},

publisher = {Elsevier},

abstract = {Mobile monitoring of electroencephalogram (EEG) signals is prone to different sources of artifacts. Most importantly, motion-related artifacts present a major challenge hindering the clean acquisition of EEG data as they spread all over the scalp and across all frequency bands. This leads to additional complexity in the development of neurologically-oriented mobile health solutions. Among the top five most common neurological disorders, epilepsy has increasingly relied on EEG for diagnosis. Separate methods have been used to classify EEG segments in the context of epilepsy while reducing the existing mobility artifacts. This work specifically devises an approach to remove motion-related artifacts in the context of epilepsy. The proposed approach first includes the recording of EEG signals using a wearable EEG headset. The recorded signals are then colored by some motion artifacts generated in a lab-controlled experiment. This stage is followed by temporal and spectral characterization of the signals and artifact removal using independent component analysis (ICA). The proposed approach is tested using real clinical EEG data and results showed an average increase in accuracy of ∼9% in seizure detection and ∼24% in prediction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{choi2019brain,

title = {Brain computer interface-based action observation game enhances mu suppression in patients with stroke},

author = {Hyoseon Choi and Hyunmi Lim and Joon Woo Kim and Youn Joo Kang and Jeonghun Ku},

doi = {https://doi.org/10.3390/electronics8121466},

year  = {2019},

date = {2019-12-02},

journal = {Electronics},

volume = {8},

number = {12},

pages = {1466},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Action observation (AO), based on the mirror neuron theory, is a promising strategy to promote motor cortical activation in neurorehabilitation. Brain computer interface (BCI) can detect a user’s intention and provide them with brain state-dependent feedback to assist with patient rehabilitation. We investigated the effects of a combined BCI-AO game on power of mu band attenuation in stroke patients. Nineteen patients with subacute stroke were recruited. A BCI-AO game provided real-time feedback to participants regarding their attention to a flickering action video using steady-state visual-evoked potentials. All participants watched a video of repetitive grasping actions under two conditions: (1) BCI-AO game and (2) conventional AO, in random order. In the BCI-AO game, feedback on participants’ observation scores and observation time was provided. In conventional AO, a non-flickering video and no feedback were provided. The magnitude of mu suppression in the central motor, temporal, parietal, and occipital areas was significantly higher in the BCI-AO game than in the conventional AO. The magnitude of mu suppression was significantly higher in the BCI-AO game than in the conventional AO both in the affected and unaffected hemispheres. These results support the facilitatory effects of the BCI-AO game on mu suppression over conventional AO},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lim2019multiple,

title = {Multiple-command single-frequency SSVEP-based BCI system using flickering action video},

author = {Hyunmi Lim and Jeonghun Ku},

doi = {https://doi.org/10.1016/j.jneumeth.2019.01.005},

year  = {2019},

date = {2019-01-16},

journal = {Journal of Neuroscience Methods},

volume = {314},

pages = {21-27},

publisher = {Elsevier},

abstract = {Background

The number of commands in a brain–computer interface (BCI) system is important. This study proposes a new BCI technique to increase the number of commands in a single BCI system without loss of accuracy.



New method

We expected that a flickering action video with left and right elbow movements could simultaneously activate the different pattern of event-related desynchronization (ERD) according to the video contents (e.g., left or right) and steady-state visually evoked potential (SSVEP). The classification accuracy to discriminate left, right, and rest states was compared under the three following feature combinations: SSVEP power (19–21 Hz), Mu power (8–13 Hz), and simultaneous SSVEP and Mu power.



Results

The SSVEP feature could discriminate the stimulus condition, regardless of left or right, from the rest condition, while the Mu feature discriminated left or right, but was relatively poor in discriminating stimulus from rest. However, combining the SSVEP and Mu features, which were evoked by the stimulus with a single frequency, showed superior performance for discriminating all the stimuli among rest, left, or right.



Comparison with the existing method

The video contents could activate the ERD differently, and the flickering component increased its accuracy, such that it revealed a better performance to discriminate when considering together.



Conclusions

This paradigm showed possibility of increasing performance in terms of accuracy and number of commands with a single frequency by applying flickering action video paradigm and applicability to rehabilitation systems used by patients to facilitate their mirror neuron systems while training.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arakaki2019alpha,

title = {Alpha desynchronization during simple working memory unmasks pathological aging in cognitively healthy individuals},

author = {Xianghong Arakaki and Ryan Lee and Kevin S King and Alfred N Fonteh and Michael G Harrington},

editor = {Stephen D. Ginsberg, Nathan S Kline Institute},

doi = {https://doi.org/10.1371/journal.pone.0208517},

year  = {2019},

date = {2019-01-02},

journal = {PloS one},

volume = {14},

number = {1},

pages = {e0208517},

publisher = {Public Library of Science San Francisco, CA USA},

abstract = {Our aim is to explore if cognitive challenge combined with objective physiology can reveal abnormal frontal alpha event-related desynchronization (ERD), in early Alzheimer’s disease (AD). We used quantitative electroencephalography (qEEG) to investigate brain activities during N-back working memory (WM) processing at two different load conditions (N = 0 or 2) in an aging cohort. We studied 60–100 year old participants, with normal cognition, and who fits one of two subgroups from cerebrospinal fluid (CSF) proteins: cognitively healthy (CH) with normal amyloid/tau ratio (CH-NAT, n = 10) or pathological amyloid/tau ratio (CH-PAT, n = 14). We recorded behavioral performances, and analyzed alpha power and alpha spectral entropy (SE) at three occasions: during the resting state, and at event-related desynchronization (ERD) [250 ~ 750 ms] during 0-back and 2-back. During 0-back WM testing, the behavioral performance was similar between the two groups, however, qEEG notably differentiated CH-PATs from CH-NATs on the simple, 0-back testing: Alpha ERD decreased from baseline only in the parietal region in CH-NATs, while it decreased in all brain regions in CH-PATs. Alpha SE did not change in CH-NATs, but was increased from baseline in the CH-PATs in frontal and left lateral regions (p<0.01), and was higher in the frontal region (p<0.01) of CH-PATs compared to CH-NATs. The alpha ERD and SE analyses suggest there is frontal lobe dysfunction during WM processing in the CH-PAT stage. Additional power and correlations with behavioral performance were also explored. This study provide pilot information to further evaluate whether this biomarker has clinical significance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{memmott2018t154,

title = {Automated and self-report measures of drowsiness over successive calibrations in a brain-computer interface for communication},

author = {Tab Memmott and Brandon Eddy and Sina Dabiri and Deniz Erdogmus and Melanie Fried-Oken and Barry Oken},

doi = {https://doi.org/10.1016/j.clinph.2018.04.155},

year  = {2018},

date = {2018-05-01},

journal = {Clinical Neurophysiology},

volume = {129},

pages = {e61--e62},

publisher = {Elsevier},

abstract = {Brain computer interfaces (BCI) generally require the user to maintain an attentive state. Potential end-users with severe speech and physical impairments may have limited communication abilities to report their current state, thus an automatic calculation of state may improve performance. It’s not yet known if an effective automatic calculation of drowsiness can be detected reliably in end-user populations or healthy controls.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arakaki2018alpha,

title = {Alpha desynchronization/synchronization during working memory testing is compromised in acute mild traumatic brain injury (mTBI)},

author = {Xianghong Arakaki and Michael Shoga and Lianyang Li and George Zouridakis and Thao Tran and Alfred N Fonteh and Jessica Dawlaty and Robert Goldweber and Janice M Pogoda and Michael G Harrington},

doi = {https://doi.org/10.1371/journal.pone.0188101},

year  = {2018},

date = {2018-02-14},

journal = {PloS one},

volume = {13},

number = {2},

pages = {e0188101},

publisher = {Public Library of Science San Francisco, CA USA},

abstract = {Diagnosing and monitoring recovery of patients with mild traumatic brain injury (mTBI) is challenging because of the lack of objective, quantitative measures. Diagnosis is based on description of injuries often not witnessed, subtle neurocognitive symptoms, and neuropsychological testing. Since working memory (WM) is at the center of cognitive functions impaired in mTBI, this study was designed to define objective quantitative electroencephalographic (qEEG) measures of WM processing that may correlate with cognitive changes associated with acute mTBI. First-time mTBI patients and mild peripheral (limb) trauma controls without head injury were recruited from the emergency department. WM was assessed by a continuous performance task (N-back). EEG recordings were obtained during N-back testing on three occasions: within five days, two weeks, and one month after injury. Compared with controls, mTBI patients showed abnormal induced and evoked alpha activity including event-related desynchronization (ERD) and synchronization (ERS). For induced alpha power, TBI patients had excessive frontal ERD on their first and third visit. For evoked alpha, mTBI patients had lower parietal ERD/ERS at the second and third visits. These exploratory qEEG findings offer new and non-invasive candidate measures to characterize the evolution of injury over the first month, with potential to provide much-needed objective measures of brain dysfunction to diagnose and monitor the consequences of mTBI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hunter2018change,

title = {Change in quantitative EEG theta cordance as a potential predictor of repetitive transcranial magnetic stimulation clinical outcome in major depressive disorder},

author = {Aimee M Hunter and Thien X Nghiem and Ian A Cook and David E Krantz and Michael J Minzenberg and Andrew F Leuchter},

doi = {https://doi.org/10.1177/1550059417746212},

year  = {2017},

date = {2017-12-10},

urldate = {2017-12-10},

journal = {Clinical EEG and Neuroscience},

volume = {49},

number = {5},

pages = {306--315},

publisher = {Sage Publications Sage CA: Los Angeles, CA},

abstract = {Repetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in major depressive disorder (MDD), although clinical outcome is variable. Change in the resting-state quantitative electroencephalogram (qEEG), particularly in theta cordance early in the course of treatment, has been linked to antidepressant medication outcomes but has not been examined extensively in clinical rTMS. This study examined change in theta cordance over the first week of clinical rTMS and sought to identify a biomarker that would predict outcome at the end of 6 weeks of treatment. Clinically stable outpatients (n = 18) received nonblinded rTMS treatment administered to the dorsolateral prefrontal cortex (DLPFC). Treatment parameters (site, intensity, number of pulses) were adjusted on an ongoing basis guided by changes in symptom severity rating scale scores. qEEGs were recorded at pretreatment baseline and after 1 week of left DLPFC (L-DLPFC) rTMS using a 21-channel dry-electrode headset. Analyses examined the association between week 1 regional changes in theta band (4-8 Hz) cordance, and week 6 patient- and physician-rated outcomes. Theta cordance change in the central brain region predicted percent change in Inventory of Depressive Symptomology–Self-Report (IDS-SR) score, and improvement versus nonimprovement on the Clinical Global Impression–Improvement Inventory (CGI-I) (R2 = .38, P = .007; and Nagelkerke R2 = .78, P = .0001, respectively). The cordance biomarker remained significant when controlling for age, gender, and baseline severity. Treatment-emergent change in EEG theta cordance in the first week of rTMS may predict acute (6-week) treatment outcome in MDD. This oscillatory synchrony biomarker merits further study in independent samples.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}