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Gernot Müller-Putz

Researcher at Graz University of Technology

Publications -  382
Citations -  14836

Gernot Müller-Putz is an academic researcher from Graz University of Technology. The author has contributed to research in topics: Brain–computer interface & Motor imagery. The author has an hindex of 59, co-authored 366 publications receiving 12634 citations. Previous affiliations of Gernot Müller-Putz include University of Graz.

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Proceedings ArticleDOI

Kinect-based detection of self-paced hand movements: Enhancing functional brain mapping paradigms

TL;DR: The results of the study suggest that the Kinect device allows generation of trigger information that is comparable to the information that can be obtained from EMG.
Journal ArticleDOI

SAFE: An EEG dataset for stable affective feature selection

TL;DR: It is demonstrated that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during long-term usage, and a stable feature selection method is proposed to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run.
Journal ArticleDOI

The interplay of prefrontal and sensorimotor cortices during inhibitory control of learned motor behavior.

TL;DR: The results showed, that inhibition/execution of learned motor programs depends on an interplay of focal increases and decreases of neural activity in prefrontal and sensorimotor areas regardless of the effector.
Journal ArticleDOI

Correlation between EEG burst-to-burst intervals and HR acceleration in preterm infants.

TL;DR: The coupling between heart rate (HR) changes and electroencephalographic (EEG) bursts is confirmed in a larger group of preterm infants and semi-automatic detection of burst-to-burst intervals (BBI) is reported on and correlations between BBI and HR changes are found.
Journal ArticleDOI

Think2grasp - BCI-Controlled Neuroprosthesis for the Upper Extremity.

TL;DR: In the European Project TOBI (Tools for Brain-Computer Interfaces) a motor imagery based, hybrid BCI (hBCI) for control of an upper extremity hybrid neuroprosthesis was evaluated following a user-centered design.