Brain–computer interfacing: more than the sum of its parts

Scherer, Reinhold; Faller, Josef; Balderas, David; Friedrich, Elisabeth V. C.; Pröll, Markus; Allison, Brendan Z.; Müller-Putz, Gernot

doi:10.1007/s00500-012-0895-4

Cited by 46 publications

(36 citation statements)

References 44 publications

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“…This may be contrasted with the motor imagery tasks which were described by some users as "enjoyable". A proposed solution is the use of context aware BCIs, as proposed in (Zander et al, 2012;Scherer et al, 2012), in which the BCI is augmented by additional information relating to the subject and/or environment (e.g. measures of subject engagement).…”

Section: Discussionmentioning

confidence: 99%

On the control of brain-computer interfaces by users with cerebral palsy

Daly

Billinger

Laparra-Hernández³

et al. 2013

Clinical Neurophysiology

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Highlights1. We conduct a feasibility study with 14 individuals with cerebral palsy (CP) to evaluate their control of two online Brain-computer interfaces. 2. Eight of the individuals with CP were able to control at least one of the BCIs at a statistically significant level of accuracy. 3. Analysis of the results reveals that BCIs may be controlled by some individuals with CP. Abstract ObjectiveBrain-computer interfaces (BCIs) have been proposed as a potential assistive device for individuals with cerebral palsy (CP) to assist with their communication needs. However, it is unclear how well-suited BCIs are to individuals with CP. Therefore, this study aims to investigate to what extent these users are able to gain control of BCIs. MethodsThis study is conducted with 14 individuals with CP attempting to control two standard online BCIs (1) based upon sensorimotor rhythm modulations, and (2) based upon steady state visual evoked potentials.Email address: reinhold.scherer@tugraz.at (Reinhold Scherer*) Preprint submitted to Clinical NeurophysiologyMarch 27, 2013 ResultsOf the 14 users, 8 are able to use one or other of the BCIs, online, with a statistically significant level of accuracy, without prior training. Classification results are driven by neurophysiological activity and not seen to correlate with occurrences of artifacts. However, many of these users' accuracies, while statistically significant, would require either more training or more advanced methods before practical BCI control would be possible. ConclusionsThe results indicate that BCIs may be controlled by individuals with CP but that many issues need to be overcome before practical application use may be achieved. SignificanceThis is the first study to assess the ability of a large group of different individuals with CP to gain control of an online BCI system. The results indicate that six users could control a sensorimotor rhythm BCI and three a steady state visual evoked potential BCI at statistically significant levels of accuracy (SMR accuracies; mean ± STD, 0.821 ± 0.116, SSVEP accuracies; 0.422 ± 0.069).

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Section: Discussionmentioning

confidence: 99%

On the control of brain-computer interfaces by users with cerebral palsy

Daly

Billinger

Laparra-Hernández³

et al. 2013

Clinical Neurophysiology

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“…As demonstrated in Fig. 3, if the wheelchair moves in the direction ψ in (8) in the polar coordinate shown in Fig. 3, a much more efficient 2-D control can be generated.…”

Section: Bmi Control Strategymentioning

confidence: 96%

“…In this manner, the steering angle of the wheelchair is given by where ψ(k) denotes the wheelchair angular position at the kth sampling time, T is a fixed sampling interval, andψ is a constant rotation velocity, which is chosen according to individual subjects training performance and α(k) is chosen as either 1 or −1 according to the operator's intention. The polar radii r(ψ) can be obtained by (8), then the wheelchair forward speed υ(t) and rotational speed ω(t) can be calculated using (10) and (15). In addition, the limits of ψ(t) can be properly chosen in the BMI control mode such that the violation of dynamic constraints will not happen.…”

Section: Bmi Control Strategymentioning

confidence: 99%

“…However, the electroencephalogram (EEG) signals have low-spatial resolution and provide only a broad and very noisy overview of the ongoing brain activity [8]. Two typical ways to enhance noisy EEG activity patterns are decoder design and training method optimization, and a lot of brain-machine interface (BMI) studies focus on feature extraction and pattern classification.…”

Section: Introductionmentioning

confidence: 99%

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Human Cooperative Wheelchair With Brain–Machine Interaction Based on Shared Control Strategy

Zhao

Duan

et al. 2017

IEEE/ASME Trans. Mechatron.

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Abstract-In this paper, a human-machine shared control strategy is proposed for the navigation control of a wheelchair, employing both brain-machine control mode and autonomous control mode. In the brain-machine control mode, contrary to the traditional four-direction control signals, a novel brain-machine interface using steadystate visual evoked potentials is presented, which utilizes two brain signals to produce a polar polynomial trajectory. The produced trajectory is continuous in curvature without violating dynamic constraints of the wheelchair. In the autonomous control mode, the synthesis of angle-based potential field and vision-based simultaneous localization and mapping technique is proposed to guide the robot navigating among the obstacles. Extensive experiments have been conducted to test the developed shared control wheelchair in several scenarios with a number of volunteers, and the results have verified the effectiveness of the proposed shared control scheme.Index Terms-Angle potential field, brain-machine interface (BMI), shared control, vision-based simultaneous localization and mapping (SLAM).

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“…BCIs can provide people with a new nonmuscular channel for controlling games (Finke, Lenhardt and Ritter, 2009;Nijholt, Plass-Oude Bos and Reuderink, 2009;Scherer et al, 2013). BCI can be used as an extra modality added to game designs, so that gamers can consciously try to interact within the game environment.…”

Section: Eeg Applications In Game-based Learningmentioning

confidence: 99%

Neurophysiological methods for monitoring brain activity in serious games and virtual environments: a review

Ninaus

Kober

Friedrich

et al. 2014

IJTEL

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she works as Research Assistant at the Department of Psychology (Section Neuropsychology) at the University of Graz and is involved in national and international scientific projects. Her research topics are EEG/NIRS-based brain-computer communication and neurofeedback, interacting in virtual realities, virtual reality as rehabilitation tool and spatial cognition.Elisabeth V.C. Friedrich: Dr., Postdoctoral researcher Department of Psychology, Section Neuropsychology University of Graz Universitätsplatz 2/III 8010 Graz Austria elisabeth.friedrich@uni-graz.at Elisabeth V.C. Friedrich studied Psychology at the University of Graz, Austria, and conducted her master thesis at the Laboratory of Neural Injury and Repair, Wadsworth Center, Albany, NY. She has received her doctorate in natural science in 2012 at the University of Graz, Austria. Her main research interest is brain-computer interface (BCI). She explored different mental tasks to control an EEGbased BCI, the impact of distraction on user performance as well as improvements of BCI usability for severely motor impaired individuals. She is currently involved in the EU GALA Network of Excellence (www.galanoe.eu). Milos Kravcik has a diploma degree in computer science and a doctoral degree in applied informatics from the Comenius University in Slovakia. He has been dealing with Technology Enhanced Learning (TEL) since 1988 in various national and international projects, later also at the Fraunhofer Institute for Applied Information Technology in Germany and at the Open University in the Netherlands. Since 2010 he works as a Research Fellow at the RWTH Aachen University and his main research interests include personalized learning environments and self-regulated learning. He co-organized several TEL doctoral schools and serves also as executive peer-reviewer or editorial board member for several journals related to learning technologies. As both an academic and researcher with considerable industrial experience, he has been instrumental in the research, analysis and development of virtual engineering environments in a variety of product engineering domains and now focuses his work on the acquisition of engineering knowledge information management systems within all aspect of product engineering; with a particular emphasis on conceptual design. He has also implemented game-based learning methods in design and manufacturing taught courses. With over 40 international publications, a book and the successful commercialisation of his novel feature recognition algorithms, he is now applying his knowledge and expertise to the domain of serious games, game ware and computational biometrics for next generation engineering applications. AbstractThe use of serious games and virtual environments for learning is increasing worldwide. These technologies have the potential to collect live data from users through game-play and can be combined with neuroscientific methods such as EEG, fNIRS and fMRI. The several learning processes triggered by serious games are associated with spec...

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Brain–computer interfacing: more than the sum of its parts

Cited by 46 publications

References 44 publications

On the control of brain-computer interfaces by users with cerebral palsy

On the control of brain-computer interfaces by users with cerebral palsy

Human Cooperative Wheelchair With Brain–Machine Interaction Based on Shared Control Strategy

Neurophysiological methods for monitoring brain activity in serious games and virtual environments: a review

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