@article{pmid39487237,

title = {Humans underestimate the movement range of their own hands},

author = {Artur Pilacinski and Antoine Vandenberghe and Gabriella Andrietta and Gilles Vannuscorps},

doi = {10.1038/s44271-024-00153-x},

issn = {2731-9121},

year  = {2024},

date = {2024-11-01},

journal = {Commun Psychol},

volume = {2},

number = {1},

pages = {104},

abstract = {Motor planning and motor imagery are assumed to use veridical internal representations of the biomechanical properties of our limbs. Here, we report that people underestimate their hands' range of motion. We used two tasks probing representations of own motion range, estimation and imagery, in which participants were supposed to judge their rotational hand movement ranges. In both tasks participants' judgments were underestimated in three out of four cardinal directions. We suggest that this representational bias provides an optimal balance between movement efficiency and safety in face of the inherently stochastic nature of movement execution.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pilacinski2024b,

title = {Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots},

author = {Artur Pilacinski and Lukas Christ and Marius Boshoff and Ioannis Iossifidis and Patrick Adler and Michael Miro and Bernd Kuhlenkötter and Christian Klaes},

doi = {10.3389/fnbot.2024.1383089},

issn = {1662-5218},

year  = {2024},

date = {2024-09-24},

journal = {Front. Neurorobot.},

volume = {18},

publisher = {Frontiers Media SA},

abstract = {Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method’s potential benefits and implications for HRC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dyck2024training,

title = {Training-related changes in neural beta oscillations associated with implicit and explicit motor sequence learning},

author = {Susanne Dyck and Christian Klaes},

year  = {2024},

date = {2024-01-01},

journal = {Scientific Reports},

volume = {14},

number = {1},

pages = {6781},

publisher = {Nature Publishing Group UK London},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PILACINSKI2024,

title = {Questionable evidence for prefrontal cortex as an alleged psi inhibitor},

author = {Artur Pilacinski and Christian Klaes and Jason Friedman and Michael Wiesing},

url = {https://www.sciencedirect.com/science/article/pii/S0010945224000017},

doi = {https://doi.org/10.1016/j.cortex.2024.01.001},

issn = {0010-9452},

year  = {2024},

date = {2024-01-01},

journal = {Cortex},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.7554/eLife.88654,

title = {Point of View: Beware ‘persuasive communication devices’ when writing and reading scientific articles},

author = {Olivier Corneille and Jo Havemann and Emma L Henderson and Hans IJzerman and Ian Hussey and Jean-Jacques Orban de Xivry and Lee Jussim and Nicholas P Holmes and Artur Pilacinski and Brice Beffara and Harriet Carroll and Nicholas Otieno Outa and Peter Lush and Leon D Lotter},

url = {https://doi.org/10.7554/eLife.88654},

doi = {10.7554/eLife.88654},

issn = {2050-084X},

year  = {2023},

date = {2023-05-01},

journal = {eLife},

volume = {12},

pages = {e88654},

publisher = {eLife Sciences Publications, Ltd},

abstract = {Authors rely on a range of devices and techniques to attract and maintain the interest of readers, and to convince them of the merits of the author’s point of view. However, when writing a scientific article, authors must use these ‘persuasive communication devices’ carefully. In particular, they must be explicit about the limitations of their work, avoid obfuscation, and resist the temptation to oversell their results. Here we discuss a list of persuasive communication devices and we encourage authors, as well as reviewers and editors, to think carefully about their use.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PILACINSKI2023e18164,

title = {The robot eyes don't have it. The presence of eyes on collaborative robots yields marginally higher user trust but lower performance},

author = {Artur Pilacinski and Ana Pinto and Soraia Oliveira and Eduardo Araújo and Carla Carvalho and Paula Alexandra Silva and Ricardo Matias and Paulo Menezes and Sonia Sousa},

url = {https://www.sciencedirect.com/science/article/pii/S2405844023053720},

doi = {https://doi.org/10.1016/j.heliyon.2023.e18164},

issn = {2405-8440},

year  = {2023},

date = {2023-01-01},

journal = {Heliyon},

volume = {9},

number = {8},

pages = {e18164},

abstract = {Eye gaze is a prominent feature of human social lives, but little is known on whether fitting eyes on machines makes humans trust them more. In this study we compared subjective and objective markers of human trust when collaborating with eyed and non-eyed robots of the same type. We used virtual reality scenes in which we manipulated distance and the presence of eyes on a robot's display during simple collaboration scenes. We found that while collaboration with eyed cobots resulted in slightly higher subjective trust ratings, the objective markers such as pupil size and task completion time indicated it was in fact less comfortable to collaborate with eyed robots. These findings are in line with recent suggestions that anthropomorphism may be actually a detrimental feature of collaborative robots. These findings also show the complex relationship between human objective and subjective markers of trust when collaborating with artificial agents.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Klaes2023.05.18.541297,

title = {Neural representations of economic decision variables in human posterior parietal cortex},

author = {Christian Klaes and Artur Pilacinski and Spencer Kellis and Tyson Aflalo and Charles Liu and Richard Andersen},

url = {https://www.biorxiv.org/content/early/2023/05/18/2023.05.18.541297},

doi = {10.1101/2023.05.18.541297},

year  = {2023},

date = {2023-01-01},

journal = {bioRxiv},

publisher = {Cold Spring Harbor Laboratory},

abstract = {Decision making has been intensively studied in the posterior parietal cortex in non-human primates on a single neuron level. In humans decision making has mainly been studied with psychophysical tools or with fMRI. Here, we investigated how single neurons from human posterior parietal cortex represent numeric values informing future decisions during a complex two-player game. The tetraplegic study participant was implanted with a Utah electrode array in the anterior intraparietal area (AIP). We played a simplified variant of Black Jack with the participant while neuronal data was recorded. During the game two players are presented with numbers which are added up. Each time a number is presented the player has to decide to proceed or to stop. Once the first player stops or the score reaches a limit the turn passes on to the second player who tries to beat the score of the first player. Whoever is closer to the limit (without overshooting) wins the game. We found that many AIP neurons selectively responded to the face value of the presented number. Other neurons tracked the cumulative score or were selectively active for the upcoming decision of the study participant. Interestingly, some cells also kept track of the opponent’s score. Our findings show that parietal regions engaged in hand action control also represent numbers and their complex transformations. This is also the first demonstration of complex economic decisions being possible to track in single neuron activity in human AIP. Our findings show how tight are the links between parietal neural circuits underlying hand control, numerical cognition and complex decision-making.Competing Interest StatementThe authors have declared no competing interest.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pilacinski2023phantom,

title = {Phantom touch illusion: Unexpected phenomenological effects of tactile gating in the absence of tactile stimulation},

author = {Artur Pilacinski and Marita Metzler and Christian Klaes},

year  = {2023},

date = {2023-01-01},

journal = {bioRxiv},

pages = {2023--02},

publisher = {Cold Spring Harbor Laboratory},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ali2022enhancing,

title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

year  = {2022},

date = {2022-01-01},

journal = {Scientific reports},

volume = {12},

number = {1},

pages = {1--19},

publisher = {Nature Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fidencio2022error,

title = {Error-related potentials in reinforcement learning-based brain-machine interfaces},

author = {Aline Xavier Fidêncio and Christian Klaes and Ioannis Iossifidis},

year  = {2022},

date = {2022-01-01},

journal = {Frontiers in Human Neuroscience},

volume = {16},

publisher = {Frontiers Media SA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ali2022contranet,

title = {ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

year  = {2022},

date = {2022-01-01},

journal = {arXiv preprint arXiv:2206.10677},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lienkamper2021quantifying,

title = {Quantifying the alignment error and the effect of incomplete somatosensory feedback on motor performance in a virtual brain--computer-interface setup},

author = {Robin Lienkämper and Susanne Dyck and Muhammad Saif-ur-Rehman and Marita Metzler and Omair Ali and Christian Klaes},

year  = {2021},

date = {2021-01-01},

journal = {Scientific reports},

volume = {11},

number = {1},

pages = {1--14},

publisher = {Nature Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saif2021spikedeep,

title = {SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm},

author = {Muhammad Saif-ur-Rehman and Omair Ali and Susanne Dyck and Robin Lienkämper and Marita Metzler and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and others},

year  = {2021},

date = {2021-01-01},

journal = {Journal of Neural Engineering},

volume = {18},

number = {1},

pages = {016009},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ali2020improving,

title = {Improving the performance of eeg decoding using anchored-stft in conjunction with gradient norm adversarial augmentation},

author = {Omair Ali and M Saif-ur Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2011.14694},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ali2020anchored,

title = {Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2011.14694},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saif2019spikedeeptector,

title = {SpikeDeeptector: A deep-learning based method for detection of neural spiking activity},

author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and others},

year  = {2019},

date = {2019-01-01},

journal = {Journal of neural engineering},

volume = {16},

number = {5},

pages = {056003},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lee2018engineering,

title = {Engineering artificial somatosensation through cortical stimulation in humans},

author = {Brian Lee and Daniel Kramer and Michelle Armenta Salas and Spencer Kellis and David Brown and Tatyana Dobreva and Christian Klaes and Christi Heck and Charles Liu and Richard A Andersen},

year  = {2018},

date = {2018-01-01},

journal = {Frontiers in systems neuroscience},

volume = {12},

pages = {24},

publisher = {Frontiers Media SA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{klaes2015hand,

title = {Hand shape representations in the human posterior parietal cortex},

author = {Christian Klaes and Spencer Kellis and Tyson Aflalo and Brian Lee and Kelsie Pejsa and Kathleen Shanfield and Stephanie Hayes-Jackson and Mindy Aisen and Christi Heck and Charles Liu and others},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Neuroscience},

volume = {35},

number = {46},

pages = {15466--15476},

publisher = {Society for Neuroscience},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aflalo2015decoding,

title = {Decoding motor imagery from the posterior parietal cortex of a tetraplegic human},

author = {Tyson Aflalo and Spencer Kellis and Christian Klaes and Brian Lee and Ying Shi and Kelsie Pejsa and Kathleen Shanfield and Stephanie Hayes-Jackson and Mindy Aisen and Christi Heck and others},

year  = {2015},

date = {2015-01-01},

journal = {Science},

volume = {348},

number = {6237},

pages = {906--910},

publisher = {American Association for the Advancement of Science},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{klaes2014cognitive,

title = {A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback},

author = {Christian Klaes and Ying Shi and Spencer Kellis and Juri Minxha and Boris Revechkis and Richard A Andersen},

year  = {2014},

date = {2014-01-01},

journal = {Journal of neural engineering},

volume = {11},

number = {5},

pages = {056024},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{andersen2014toward,

title = {Toward more versatile and intuitive cortical brain--machine interfaces},

author = {Richard A Andersen and Spencer Kellis and Christian Klaes and Tyson Aflalo},

year  = {2014},

date = {2014-01-01},

journal = {Current Biology},

volume = {24},

number = {18},

pages = {R885--R897},

publisher = {Cell Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{klaes2012sensorimotor,

title = {Sensorimotor learning biases choice behavior: a learning neural field model for decision making},

author = {Christian Klaes and Sebastian Schneegans and Gregor Schöner and Alexander Gail},

year  = {2012},

date = {2012-01-01},

journal = {PLoS computational biology},

volume = {8},

number = {11},

pages = {e1002774},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{klaes2011neural,

title = {A neural field model of decision making in the posterior parietal cortex},

author = {Christian Klaes and Sebastian Schneegans and Gregor Schöner and Alexander Gail},

year  = {2011},

date = {2011-01-01},

journal = {BMC Neuroscience},

volume = {12},

pages = {1--2},

publisher = {BioMed Central},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{klaes2011conditional,

title = {Conditional visuomotor behavior in the Parietal Reach Region and Dorsal Premotor Cortex},

author = {Christian Klaes},

year  = {2011},

date = {2011-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{klaes2011choosing,

title = {Choosing goals, not rules: deciding among rule-based action plans},

author = {Christian Klaes and Stephanie Westendorff and Shubhodeep Chakrabarti and Alexander Gail},

year  = {2011},

date = {2011-01-01},

journal = {Neuron},

volume = {70},

number = {3},

pages = {536--548},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{westendorff2010cortical,

title = {The cortical timeline for deciding on reach motor goals},

author = {Stephanie Westendorff and Christian Klaes and Alexander Gail},

year  = {2010},

date = {2010-01-01},

journal = {Journal of Neuroscience},

volume = {30},

number = {15},

pages = {5426--5436},

publisher = {Society for Neuroscience},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gail2009implementation,

title = {Implementation of spatial transformation rules for goal-directed reaching via gain modulation in monkey parietal and premotor cortex},

author = {Alexander Gail and Christian Klaes and Stephanie Westendorff},

year  = {2009},

date = {2009-01-01},

journal = {Journal of Neuroscience},

volume = {29},

number = {30},

pages = {9490--9499},

publisher = {Society for Neuroscience},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{schneider2004evolutionary,

title = {Evolutionary adaptation of nonlinear dynamical systems in computational neuroscience},

author = {Stefan Schneider and Christian Igel and Christian Klaes and Hubert R Dinse and Jan C Wiemer},

year  = {2004},

date = {2004-01-01},

journal = {Genetic Programming and Evolvable Machines},

volume = {5},

pages = {215--227},

publisher = {Kluwer Academic Publishers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dyck2024,

title = {Training-related changes in neural beta oscillations associated with implicit and explicit motor sequence learning},

author = {Susanne Dyck and Christian Klaes},

doi = {10.1038/s41598-024-57285-7},

issn = {2045-2322},

journal = {Sci Rep},

volume = {14},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractMany motor actions we perform have a sequential nature while learning a motor sequence involves both implicit and explicit processes. In this work, we developed a task design where participants concurrently learn an implicit and an explicit motor sequence across five training sessions, with EEG recordings at sessions 1 and 5. This intra-subject approach allowed us to study training-induced behavioral and neural changes specific to the explicit and implicit components. Based on previous reports of beta power modulations in sensorimotor networks related to sequence learning, we focused our analysis on beta oscillations at motor-cortical sites. On a behavioral level, substantial performance gains were evident early in learning in the explicit condition, plus slower performance gains across training sessions in both explicit and implicit sequence learning. Consistent with the behavioral trends, we observed a training-related increase in beta power in both sequence learning conditions, while the explicit condition displayed stronger beta power suppression during early learning. The initially stronger beta suppression and subsequent increase in beta power specific to the explicit component, correlated with enhanced behavioral performance, possibly reflecting higher cortical excitability. Our study suggests an involvement of motor-cortical beta oscillations in the explicit component of motor sequence learning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {[No title]},

doi = {https://doi.org/10.1038/s41598-024-57285-7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bertoni2024,

title = {Human–Robot Intimacy: Acceptance of Robots as Intimate Companions},

author = {Sophia Bertoni and Christian Klaes and Artur Pilacinski},

doi = {10.3390/biomimetics9090566},

issn = {2313-7673},

journal = {Biomimetics},

volume = {9},

number = {9},

publisher = {MDPI AG},

abstract = {Depictions of robots as romantic partners for humans are frequent in popular culture. As robots become part of human society, they will gradually assume the role of partners for humans whenever necessary, as assistants, collaborators, or companions. Companion robots are supposed to provide social contact to those who would not have it otherwise. These companion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human–robot intimacy remains a vastly unexplored territory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting the acceptance of robots as intimate partners, the public perception of robots in intimate roles, and the possible influence of cross-cultural differences in these domains. We also discuss the possible negative effects human–robot intimacy may have on human–human contact. Most importantly, we propose a new term “intimate companion robots” to reduce the negative connotations of the other terms that have been used so far and improve the social perception of research in this domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pilacinski2024c,

title = {Humans underestimate the movement range of their own hands},

author = {Artur Pilacinski and Antoine Vandenberghe and Gabriella Andrietta and Gilles Vannuscorps},

doi = {10.1038/s44271-024-00153-x},

issn = {2731-9121},

journal = {Commun Psychol},

volume = {2},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractMotor planning and motor imagery are assumed to use veridical internal representations of the biomechanical properties of our limbs. Here, we report that people underestimate their hands’ range of motion. We used two tasks probing representations of own motion range, estimation and imagery, in which participants were supposed to judge their rotational hand movement ranges. In both tasks participants’ judgments were underestimated in three out of four cardinal directions. We suggest that this representational bias provides an optimal balance between movement efficiency and safety in face of the inherently stochastic nature of movement execution.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pilacinski2024d,

title = {Humans underestimate the movement range of their own hands},

author = {Artur Pilacinski and Antoine Vandenberghe and Gabriella Andrietta and Gilles Vannuscorps},

doi = {10.1038/s44271-024-00153-x},

issn = {2731-9121},

journal = {Commun Psychol},

volume = {2},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractMotor planning and motor imagery are assumed to use veridical internal representations of the biomechanical properties of our limbs. Here, we report that people underestimate their hands’ range of motion. We used two tasks probing representations of own motion range, estimation and imagery, in which participants were supposed to judge their rotational hand movement ranges. In both tasks participants’ judgments were underestimated in three out of four cardinal directions. We suggest that this representational bias provides an optimal balance between movement efficiency and safety in face of the inherently stochastic nature of movement execution.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pilacinski2024e,

title = {Humans underestimate the movement range of their own hands},

author = {Artur Pilacinski and Antoine Vandenberghe and Gabriella Andrietta and Gilles Vannuscorps},

doi = {10.1038/s44271-024-00153-x},

issn = {2731-9121},

journal = {Commun Psychol},

volume = {2},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractMotor planning and motor imagery are assumed to use veridical internal representations of the biomechanical properties of our limbs. Here, we report that people underestimate their hands’ range of motion. We used two tasks probing representations of own motion range, estimation and imagery, in which participants were supposed to judge their rotational hand movement ranges. In both tasks participants’ judgments were underestimated in three out of four cardinal directions. We suggest that this representational bias provides an optimal balance between movement efficiency and safety in face of the inherently stochastic nature of movement execution.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}