Related Projects

Ongoing EU projects


The long term goal of rehabilitation is resettlement back in the community away from institutional care. BackHome will conceive, research, design, implement and validate person-centred solutions to end users with functional diversity.

Knowing the person's needs will be a core part of the pro­ject focus. Social research techniques will be conducted in order to provide a continuous flow of user-based knowledge that will be crucial to ensure the alignment between the project outputs and the requirements of people.

BackHome will provide assistive technology (AT) solutions to research and develop systems for assisting people with severe disabilities. The AT embedded within BackHome will include BNCIs, environmental control systems and a range of other technologies which are usually classified as ambient intelligence and which can provide a considerable support to make BNCI solutions really work in environments with a lack of human support.

The main goal of BackHome is to help end users who want to use BNCI tools to accomplish goals that are otherwise impossible, difficult, or create dependence on a caregiver. BackHome will further help the end user support community, including doctors, nurses, family members, AT centers, and other people who want to provide the best possible tools for their patients or loved ones.

BackHome will also cover specific research on the potential applicability of the system in different scenarios and the benefits provided to additional target groups: quadriplegics, hemiplegics; people affected by speech disorders such as aphasia; people with dementia; people affected by visual and hearing functional diversity; people affected by cognitive functional diversity such as Alzheimer or Parkinson diseases; and minimum response people or locked-in persons.


In recent years, real time analysis of user state based on signals from the brain and peripheral physiology has made progress, typically within separate branches of research. However, the ability to predict user intention from these inferred states is still a grand challenge in real-world applications.

As a novel solution, MindSee proposes to fuse EEG – as the main sensor  with peripheral physiological sensors (EDR, fEMG, eye gaze and pupillometry) and contextual information for unobtrusive acquisition of implicit measures of perception, cognition and emotion. 

Real-time estimates of these implicit, or hidden, user states will be used to complement keyboard and gestural input in a real-world application of scientific literature search where the information exploration of the user is guided by co-adaptation with the computer. The proposed Symbiotic Information Seeking System will provide a wide range of visualization resources that adapt the information retrieved according to its relevance, cognitive ergonomic complexity, and aesthetic properties. For the target application of scientific literature search, MindSee builds upon a cutting-edge retrieval system that has access to 50 million documents from the main scientific databases.

MindSee will be developed using an iterative approach with three full cycles of implementation and evaluation of increasingly complex symbiotic interactions in information seeking. The iterative evaluation of MindSee technology and validation of underlying methods will be conducted in realistic experiments with user groups that vary in skill and content area.

The MindSee project will develop a new symbiotic information retrieval system capable of more than doubling the performance of information seeking in realistic tasks, compared to mainstream tools. MindSee symbiotic interaction will deliver solutions to increase productivity and creative potential. Several MindSee results are exploitable and applicable to other information seeking contexts beyond scientific literature search.


  • Coordinator: Graz University of Technology
  • Duration: 03/2015–02/2018

More than half of the persons with spinal cord injuries (SCI) are suffering from impairments of both hands, which results in a tremendous decrease of quality of life (QoL) and represents a major barrier for inclusion in society. Functional restoration is possible with neuroprostheses based on functional electrical stimulation (FES). However, current systems are nonintelligent, non-intuitive open loop systems without sensory feedback.

MoreGrasp aims at developing a multi-adaptive, multimodal user interface including brain-computer interfaces (BCIs) for intuitive control of a semi-autonomous motor and sensory grasp neuroprosthesis to support activities of daily living in individuals with SCI. With such a system a bilateral grasp restoration may become reality. The multimodal interfaces will be based on non-invasive BCIs for decoding of movements intentions with gel-less electrodes and wireless amplifiers. The neuroprosthesis will include FES electrode arrays and different sensors to allow for implementation of predefined or autonomously learned sequences. MoreGrasp will consequently follow the concept of the user-centered design by providing a scalable, modular, user-specific neuroprosthesis together with personalized EEG recording technology. Novel multimodal software architectures including interoperability standards will be defined to integrate neuroprostheses into the field of assistive technology.

Long-term end user studies will demonstrate the reliability, usefulness and impact on QoL of the MoreGrasp technology. A web-based service infrastructure including a discussion forum will be set up for assessing user priorities and screening of users’ status. The evaluation of the training and patterns of use will allow for user modeling to identify factors for successful use.

The highly interdisciplinary MoreGrasp consortium consists of members from universities, industry and rehabilitation centers, which have a long history of successful cooperation.


The NEBIAS (neurocontrolled bidirectional artificial upper limb and hand prosthesis) proposal aims at developing and clinically evaluating (in selected amputees) a neuro-controlled upper limb prosthesis intuitively controlled and felt by the amputee as the natural one. This will be possible by means of a novel neural interface able to provide a stable and very selective connection with the nervous system. This goal will be achieved by combining microtechnology and material science and will allow, on one side, recording of the motor-related signals governing the actions of the amputated hand/arm for the motion control of a mechanical prosthesis, and on the other providing sensory feedback from tactile and kinesthetic sensors through neuromorphic stimulation of the adequate afferent pathway within the residual limb.

The NEBIAS proposal is also aimed at finding out the language intrinsically linking the central nervous system with peripheral nerve signals in order to govern simple and complex hand or finger movements. To reach this goal, a variety of techniques exploring brain and nerve functions will be assembled and integrated; this includes the analysis of electromagnetic brain and nerve signals, as well as of movement-related changes in the blood flow/metabolism of the brain.


Brain Machine Interfaces (BMIs) are devices mediating communication between a brain and the external world, and hold the potential for a) restoring motor or sensory functions to people who lost them due to illness or injury, and b) understanding neural information processing through controlled interactions between neurons and external devices. However, the success of BMIs is hampered by the problem that neural responses to external correlates are highly variable because they depend on the internal state of the neural network. We propose to remove this obstacle by developing a radically new generation of "bidirectional BMIs" (which decode information from the recorded neural activity and provide information to the brain by stimulation) employing neural computational strategies and neuromorphic VLSI devices that i) understand how network states influence neural responses to stimuli; ii) use this know-how to discount variability induced by state changes in real time and thus operate with increased bandwidth and performance.

We gather a highly interdisciplinary team composed of both mathematical and experimental neuroscientists and of VLSI engineers. We will study the interplay between ongoing network states and stimulus-evoked responses in various nervous systems of different complexity. We will develop advanced algorithms and models of network dynamics to determine the network state variables best predicting and discounting neural variability, and to construct optimal state-dependent rules to decode neural activity. We will implement these algorithms in a new "state-dependent bidirectional BMI" prototype using low-power neuromorphic VLSI circuits that extract in real time network state information and use it to produce outputs optimally suited for both decoding of recorded signals and delivering electrical stimulation to a neural tissue in a given state. This BMI will be tested in a benchmark experiment in rats to guide an external device with closed loop control.

Past EU projects






Brain Bow



Future BNCI