284 research outputs found
EEG Data for: "Learning from Label Proportions in Brain-Computer Interfaces"
No full textData about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm.
The dataset is used and described in the following journal article:
Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.
Please cite the above article when using the data.
The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.
In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:
Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.
The following files are available:
description.pdf: Full description of the dataset
offline_auditory.zip: Data from the auditory offline study with N=5 subjects
offline_visual.zip: Data from the visual offline study with N=5 subjects
online_study_1-7.zip: Data from the online study for subjects 1-7
online_study_8-13.zip: Data from the online study for subjects 8-13
sequence.mat: Sequence data necessary for applying LLP to the online study. It is the same for all subjects
We will create a git repository with example code soon.We gratefully acknowledge the support by BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG), grant number EXC 1086
Recent advances in brain-computer interface research: A summary of the 2019 BCI Award and online BCI research activities
No full textContains fulltext :
240302.pdf (Publisher’s version ) (Open Access)The introduction chapter of this book described the BCI Research Awards, selection criteria, nominees, and jury. Developing a good submission for a BCI Research Award is a formidable goal, and being nominated is even more demanding. This book has presented thirteen chapters by the authors of projects nominated for a BCI Research Award in 2019. Some of these chapters detailed the projects that were nominated, while other chapters comprised interviews with nominees. In this chapter, we review the 2019 BCI Research Awards Ceremony and present the winners. We also discuss emerging directions such as online BCI-related activities that have become much more prominent during 2020 due to COVID concerns
Brain-computer interface research: A state-of-the-art summary 9 [Introduction]
No full textContains fulltext :
240288.pdf (Publisher’s version ) (Open Access)Brain-computer interface (BCI) systems can provide communication and control without any physical movement. The BCI Research Awards are annual events to select the best BCI projects that year. Groups from around the world submit projects that are scored by a jury of international experts that selects twelve nominees and three winners. We also produce books like this one that review that year’s nominees, awards ceremony, and winners. This introductory chapter briefly reviews BCIs and the 2019 awards process, including the jury, selection criteria, and nominees. We mention many chapters that might engage readers with different interests, including chapters with project descriptions or interviews with nominees. Many of the chapters here describe new approaches to BCIs that could be useful to patients and/or mainstream users. The final chapter of this book reviews the Awards Ceremony, announces the winners, and presents concluding comments
Das Scheitern der Doha-Runde - wie groß ist der Schaden?
No full textDie Staats- und Regierungschefs hatten beim G8-Gipfel am 16./17. Juli 2006 in St. Petersburg vereinbart, bis Mitte August die Einigung über die Eckpunkte der Marktöffnung im Agrar- und Industriegüterhandel nachzuholen. Dennoch wurden die Verhandlungen im Rahmen der "Doha Development Agenda" Ende Juli bis auf Weiteres unterbrochen. Stefan Tangermann, Director for Food and Agriculture bei der OECD, meint, es sei "nicht einmal gänzlich auszuschließen, dass die Doha-Runde am Ende gänzlich scheitert, weil sich im Agrarhandel keine Einigung erzielen lässt." Wenn eine schwere Krise, wie sie jetzt in Genf eingetreten sei, behoben werden solle, reiche es nicht aus, auf den Positionen von gestern zu bestehen, nur um vor den heimischen Lobbies und den Verhandlungspartnern in der WTO nicht das Gesicht zu verlieren. Erforderlich sei eine "politisch und sachlich überzeugende Begründung für das, was in den Verhandlungen erreicht werden soll". Bernhard Brümmer, Universität Göttingen, warnt vor einer wachsenden Bedeutung des Regionalismus. Als "weit überlegene Alternativen" bezeichnet er "eine zügige Rückkehr an die Genfer Verhandlungstische und eine vorsichtigere Anwendung von bilateralen und regionalen Freihandelsabkommen als in den letzten Jahren". Jürgen Matthes, Institut der Deutschen Wirtschaft, weist ebenfalls auf die Gefahr hin, dass die WTO durch immer mehr regionale und vor allem bilaterale Handelsabkommen geschwächt wird. Allerdings spreche manches dafür, dass sich der Bilateralismus von selbst als Irrweg erweise. Nach Ansicht von Michael Pfeiffer, Deutscher Industrie- und Handelskammertag, gibt es für den deutschen Mittelstand "keine vernünftige Alternative zum multilateralen Handelsabkommen". Andreas Schneider, Centre for European Policy Studies, befürchtet, dass der Abbruch der Gespräche eine Reform des globalen Handelssystems zum Vorteil der Entwicklungsländer verhindert, weist allerdings daraufhin, dass die "WTO keine Entwicklungsinstitution" sei
Extending a German Aphasia Rehabilitation BCI into Dutch and English Domains
No full textBrain-computer interfaces provide a way for brain-controlled devices, eliminating
the need for limb control. Auditory BCIs further give rise to possibility
for motor-impaired patients, specifically since some auditory BCIs do not
require eye-gaze control. German native post-stroke aphasic patients suffering
from damage in language-specific neural correlates have been shown to
regain a higher level of communicative capabilities and increased scores in
clinical aphasia assessments after training with a novel auditory ERP-based
BCI paradigm for aphasia rehabilitation. This BCI paradigm utilizes a spatial
speaker set-up, effectively introducing a spatial component to stimuli,
which was shown to increase classification on target versus nontarget stimuli
based on the classic oddball paradigm. Stimuli are comprised of a preceding
cueing sentence, with the last word missing. The present study aims
to gather apt stimuli sets in Dutch and English domains, and setting up
the experiment such that pilot studies can be held shortly after. Stimuli
sets were found in both Dutch and English, and recordings of these have
been made with native speakers. Furthermore, organizational requirements
to commence an EEG study have been fulfilled, and a detailed description
of future steps for conducting a pilot study in Dutch has been provided
Optimal Stimulus Conditions to Improve User Experience in Brain Computer Interfaces
No full textCurrent Brain Computer Interfaces (BCIs) used for spelling are quite fatiguing
and uncomfortable for the participant to use. It is proposed that
certain stimulus adaptions, such as implementing coloured stimuli, can inhibit
this. This thesis studied the effect of adapting the colour and structure
of checkerboard pattern stimuli on the user’s fatigue and comfort, using a
code-modulated visual evoked potential (c-VEP) based BCI speller interface.
The main focus was on improving this comfort while maintaining a
good system performance. Using five different conditions, it was found that
there is a trade-off between system performance and comfort, and that a
choice needs to be made according to the purpose of the system. It was concluded
that a black-white solid flashing condition was the best performing
stimulus in terms of accuracy of the system, while a violet-grey checkerboard
appeared to be the best condition in terms of user-comfort
How to Build a Faster Code-Modulated Visual Evoked Potential based Brain-Computer Interface: The Impact of Codes Bachelor’s Thesis in Artificial Intelligence
No full textBrain-computer interface (BCI) speller applications are a way for physically limited people to
communicate. The type of BCI in this research will use electroencephalography (EEG) to measure
the users’ visually evoked potentials (VEP) to decode what character the user is looking at. These
VEPs are induced by presenting flash patterns generated by pseudorandom patterns. To be able
to distinguish these flashing patterns from one another, they are required to have low correlation.
Generating these flash patterns is not trivial, which is why several types of code sets have been used
in earlier research, with m-sequences being used the most. This research aims to find alternatives
to this code set which achieve higher Information Transfer Rates (ITRs), or induce less eyestrain.
These alternatives are: de Bruijn, Gold and Golay code sets. Additionally, it aims to find the
impact modulation has on these metrics. Results are obtained by performing a 320 trial copyspelling
task in addition to an eyestrain rating across 10 conditions for 12 participants. Results are
analysed using reconvolution and canonical correlation analysis (CCA). Not modulating appeared
to perform significantly better than not modulating in ITR (122.0 bits per minute > 111.4 bits
per minute, p-value < 0.05). It also caused significantly less eyestrain (not modulated: 3.9 <
modulated: 5.5, p < 0.001). No significant differences were found in ITR or eyestrain across the
different code sets. The highest performing code set reached an ITR of 138.5 bits per minute
averaged over all participants. Higher performance was found by not modulated codes, whilst no
differences were found between different types of code sets. Variations in setup and decoding could
potentially be limitations and explain differences with other research
Incorporating spatial prior information to improve covariance matrix estimation and classification of EEG data
No full textPredicting c-VEP-based BCI performance to study BCI illiteracy
No full textA brain–computer interface (BCI) is a system that can deduce the intent of a user from recordings
of their brain activity. This allows users to control a computer application by brain activity,
which can be measured, e.g., by electroencephalography (EEG). After approximately 50 years of
BCI research, the success that BCI control can provide still greatly varies from subject-to-subject.
About 15% to 30% of sensorimotor rhythm-based (SMR) BCI users do not reach the criterion level
of 70% accuracy, which was determined to be the threshold for meaningful spelling. This is in the
literature known as the BCI illiteracy phenomenon. A myriad of variables are studied alongside
of subjects testing BCIs in order to determine if these variables correlate with BCI performance.
If they do, then they could potentially be used as predictors. The development of predictors of
BCI performance serves two purposes: it may lead to a better understanding of the BCI-illiteracy
phenomenon and these predictors could be used as a screening tool to inform users about poor
expected performance, among other things. An experiment was conducted in which six predictors
were analysed by means of a linear and a multivariate regression analysis alongside of the state-ofthe-
art paradigm code-modulated visual evoked potential-based (c-VEP) BCI performance. The
six predictors were: relative visual alpha power during resting state with eyes open and closed,
heart rate variability, attention span measured by the error rate of the Sustained Attention to
Response Task (SART) paradigm and flash-VEP latency and amplitude. There were no significant
(p > 0.008) Pearson’s correlation coefficients found for these predictors with N=16 and all subjects
were able to obtain sufficient BCI control when they were given enough time. It was concluded that
subject-to-subject variance is not in accuracy for c-VEP-based BCIs, but in time. The multivariate
regression model could be used as a screening tool given its root mean square error (RMSE) of
14.084% for a trial size of 1.05 seconds
The online optimization of brain-computer interface stimulus parameters, a simulation
No full textA brain-computer interface operates by presenting stimuli to elicit brain responses that can be
acted upon. As the discriminability of these responses is subject to the stimulus characteristics,
the presentation of the optimal stimuli could improve the performance of the interface.
Unfortunately, these optima are subject-specific. Furthermore, the optimization of said stimuli
is complicated by the potentially high level of non-independent and identically distributed
noise that comes with the recorded responses. The aim of this thesis is to demonstrate that
these challenges can be alleviated by introducing the optimization process with heteroskedatic
modelling and the replication of existing designs. To this end, various Bayesian optimization
algorithms are tested on simulations that are designed to resemble the aforementioned online
optimization objective. The compared optimization algorithms differ in the surrogate models,
replication schemes and selection strategies that have been used. As it is impossible to change
the stimulus characteristics that have been used to record existing datasets, the simulated objective
functions model the response discriminability as a function of the decoding parameters.
To assess the effectiveness of the optimization algorithms, the algorithms are tested on one, two
and seven-dimensional objective functions. The results suggest that a Bayesian optimization
algorithm that is enriched with heteroskedastic Gaussian process regression and the locationbased
evaluation of existing designs significantly outperforms the random sampling baseline for
all objective functions that have been considered. Even though the thesis is focused at braincomputer
interfaces, application of the results is widespread since heteroskedastic noise can also
be encountered with black-box optimization within other domains
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