@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).},

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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{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{pereira2018cross,

title = {Cross-Subject EEG Event-Related Potential Classification for Brain-Computer Interfaces Using Residual Networks},

author = {Arnaldo Pereira and Dereck Padden and Jay Jantz and Kate Lin and Ramses Alcaide-Aguirre},

doi = {10.13140/RG.2.2.16257.10086},

year  = {2018},

date = {2018-09-20},

urldate = {2018-01-01},

abstract = {EEG event-related potentials, and the P300 signal in

particular, are promising modalities for brain-computer interfaces (BCI). But the nonstationarity of EEG signals and

their differences across individuals have made it difficult to

implement classifiers that can determine user intent without having to be retrained or calibrated for each new user

and sometimes even each session. This is a major impediment to the development of consumer BCI. Recently, the

EEG BCI literature has begun to apply convolutional neural

networks (CNNs) for classification, but experiments have

largely been limited to training and testing on single subjects. In this paper, we report a study in which EEG data

were recorded from 66 subjects in a visual oddball task

in virtual reality. Using wide residual networks (WideResNets), we obtain state-of-the-art performance on a test set

composed of data from all 66 subjects together. Additionally, a minimal preprocessing stream to convert EEG data

into square images for CNN input while adding regularization is presented and shown to be viable. This study also

provides some guidance on network architecture parameters based on experiments with different models. Our results show that it may possible with enough data to train

a classifier for EEG-based BCIs that can generalize across

individuals without the need for individual training or calibration.

},

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{kang2015investigation,

title = {Investigation of engagement of viewers in movie trailers using electroencephalography},

author = {Dayoon Kang and Jinsoo Kim and Dong-Pyo Jang and Yang Seok Cho and Sung-Phil Kim},

doi = {https://doi.org/10.1080/2326263X.2015.1103591},

year  = {2015},

date = {2015-11-10},

journal = {Brain-Computer Interfaces},

volume = {2},

number = {4},

pages = {193--201},

publisher = {Taylor & Francis},

abstract = {Brain-computer interfaces (BCIs) have been focused on providing direct communications to the disabled. Recently, BCI researchers have expanded BCI applications to non-medical uses and categorized them as active BCI, reactive BCI, and passive BCI. Neurocinematics, a new application of reactive BCIs, aims to understand viewers’ cognitive and affective responses to movies from neural activity, providing more objective information than traditional subjective self-reports. However, studies on analytical indices for neurocinematics have verified their indices by comparisons with self-reports. To overcome this contradictory issue, we proposed using an independent psychophysical index to evaluate a neural engagement index (NEI). We made use of the secondary task reaction time (STRT), which measures participants’ engagement in a primary task by their reaction time to a secondary task; here, responding to a tactile stimulus was the secondary task and watching a movie trailer was the primary task. NEI was developed as changes in the difference between frontal beta and alpha activity of EEG. We evaluated movie trailers using NEI, STRT, and self-reports and found a significant correlation between STRT and NEI across trailers but no correlation between any of the self-report results and STRT or NEI. Our results suggest that NEI developed for neurocinematics may conform well with more objective psychophysical assessments but not with subjective self-reports.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}