FG Neurotechnologie

33 Items

Recent Submissions
Expanding the analysis of functional Near-Infrared Spectroscopy (fNIRS) data with multivariate techniques

Gemignani, Jessica (2019)

The use of functional Near-Infrared Spectroscopy is experiencing a rapid growth in use in every area of neuroscientific research, but one of the most prolific areas of application is the field of language development in children. If over the past decades there have been incredible advancements on the technology side, the same can not be said about data analysis techniques: for fNIRS, an ad-hoc ...

Real-time inference of word relevance from electroencephalogram and eye gaze

Wenzel, Markus A. ; Bogojeski, Mihail ; Blankertz, Benjamin (2017-08-16)

Objective. Brain-computer interfaces can potentially map the subjective relevance of the visual surroundings, based on neural activity and eye movements, in order to infer the interest of a person in real-time. Approach. Readers looked for words belonging to one out of five semantic categories, while a stream of words passed at different locations on the screen. It was estimated in real-time ...

Implicit relevance feedback from electroencephalography and eye tracking in image search

Golenia, Jan-Eike ; Wenzel, Markus A. ; Bogojeski, Mihail ; Blankertz, Benjamin (2018-01-24)

Objective. Methods from brain–computer interfacing (BCI) open a direct access to the mental processes of computer users, which offers particular benefits in comparison to standard methods for inferring user-related information. The signals can be recorded unobtrusively in the background, which circumvents the time-consuming and distracting need for the users to give explicit feedback to questio...

Unsupervised classification of operator workload from brain signals

Schultze-Kraft, Matthias ; Dähne, Sven ; Gugler, Manfred ; Curio, Gabriel ; Blankertz, Benjamin (2016-04-14)

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous ef...

Active visual search in non-stationary scenes: coping with temporal variability and uncertainty

Ušćumlić, Marija ; Blankertz, Benjamin (2016-01-04)

Objective. State-of-the-art experiments for studying neural processes underlying visual cognition often constrain sensory inputs (e.g., static images) and our behavior (e.g., fixed eye-gaze, long eye fixations), isolating or simplifying the interaction of neural processes. Motivated by the non-stationarity of our natural visual environment, we investigated the electroencephalography (EEG) corre...

EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)

Acqualagna, Laura ; Bosse, Sebastian ; Porbadnigk, Anne K. ; Curio, Gabriel ; Müller, Klaus-Robert ; Wiegand, Thomas ; Blankertz, Benjamin (2015-03-13)

Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. U...

Wyrm: A Brain-Computer Interface Toolbox in Python

Venthur, Bastian ; Dähne, Sven ; Höhne, Johannes ; Heller, Hendrik ; Blankertz, Benjamin (2015-05-24)

In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm (https://github.com/bbci/wyrm), an open source BCI ...

BNCI Horizon 2020: towards a roadmap for the BCI community

Brunner, Clemens ; Birbaumer, Niels ; Blankertz, Benjamin ; Guger, Christoph ; Kübler, Andrea ; Mattia, Donatella ; Millán, José del R. ; Miralles, Felip ; Nijholt, Anton ; Opisso, Eloy ; Ramsey, Nick ; Salomon, Patric ; Müller-Putz, Gernot R. (2015-02-10)

The brain-computer interface (BCI) field has grown dramatically over the past few years, but there are still no coordinated efforts to ensure efficient communication and collaboration among key stakeholders. The European Commission (EC) has recently renewed their efforts to establish such a coordination effort by funding a coordination and support action for the BCI community called ‘BNCI Horiz...

Integrating neurophysiologic relevance feedback in intent modeling for information retrieval

Jacucci, Giulio ; Barral, Oswald ; Daee, Pedram ; Wenzel, Markus A. ; Serim, Baris ; Ruotsalo, Tuukka ; Pluchino, Patrik ; Freeman, Jonathan ; Gamberini, Luciano ; Kaski, Samuel ; Blankertz, Benjamin (2019-03-12)

The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first‐of‐its‐kind, fully integrated information retrieval system that makes use ...

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

Wenzel, Markus A. ; Almeida, Inês ; Blankertz, Benjamin (2016-10-28)

Objective: Brain-computer interfaces (BCIs) that are based on event-related potentials (ERPs) can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli) in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG). Detecting ...

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

Sannelli, Claudia ; Vidaurre, Carmen ; Müller, Klaus-Robert ; Blankertz, Benjamin (2019-01-25)

Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session wi...

Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface

Acqualagna, Laura ; Botrel, Loic ; Vidaurre, Carmen ; Kübler, Andrea ; Blankertz, Benjamin (2016-02-18)

In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI pro...

Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli

Sturm, Irene ; Dähne, Sven ; Blankertz, Benjamin ; Curio, Gabriel (2015-10-28)

Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. ...

A gaze independent brain-computer interface based on visual stimulation through closed eyelids

Hwang, Han-Jeong ; Ferreria, Valeria Y. ; Ulrich, Daniel ; Kilic, Tayfun ; Chatziliadis, Xenofon ; Blankertz, Benjamin ; Treder, Matthias (2015-10-29)

A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented q...

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

Blankertz, Benjamin ; Acqualagna, Laura ; Dähne, Sven ; Haufe, Stefan ; Schultze-Kraft, Matthias ; Sturm, Irene ; Ušćumlic, Marija ; Wenzel, Markus ; Curio, Gabriel ; Müller, Klaus-Robert (2016-11-21)

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicabilit...

Classification of Eye Fixation Related Potentials for Variable Stimulus Saliency

Wenzel, Markus ; Golenia, Jan-Eike ; Blankertz, Benjamin (2016-02-15)

Objective: Electroencephalography (EEG) and eye tracking can possibly provide information about which items displayed on the screen are relevant for a person. Exploiting this implicit information promises to enhance various software applications. The specific problem addressed by the present study is that items shown in real applications are typically diverse. Accordingly, the saliency of infor...

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

Hebart, Martin N. ; Görgen, Kai ; Haynes, John-Dylan (2015-01-06)

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to...

Enhanced Classification Methods for the Depth of Cognitive Processing Depicted in Neural Signals

Nicolae, Irina-Emilia ; Acqualagna, Laura ; Neagu, Georgeta-Mihaela ; Strungaru, Rodica ; Blankertz, Benjamin (2018)

Analyzing brain states is a difficult problem due to high variability between subjects and trials, therefore improved techniques are requested to be developed for a better discrimination between the neural components. This paper investigates multiple enhanced classification methods for neurological feature selection and discrimination of the depth of cognitive processing. The aim is to detect t...

Presenting a Spatial-Geometric EEG Feature to Classify BMD and Schizophrenic Patients

Alimardani, Fatemeh ; Boostani, Reza ; Blankertz, Benjamin (2016)

Schizophrenia (SZ) and bipolar mood disorder (BMD) patients demonstrate some similar signs and symptoms; therefore, distinguishing those using qualitative criteria is not an easy task especially when these patients experience manic or hallucination phases. This study is aimed at classifying these patients by spatial analysis of their electroencephalogram (EEG) signals. In this way, 22-channels ...

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Sannelli, Claudia ; Vidaurre, Carmen ; Müller, Klaus-Robert ; Blankertz, Benjamin (2016-05-17)

Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spont...