European Doctoral Network for Neural Prostheses and Brain Research (DONUT)

DONUT is a project funded by the European Union. Its primary objective is to enhance the education and training of doctoral candidates, particularly in the fields of Brain-Computer Interface (BCI) and Electroencephalography (EEG) technologies. BCI detects brain signals, analyses them, and translates them into instructions that a computer can understand. BCIs vary in invasiveness, ranging from non-invasive methods such as EEG and Magnetic Resonance Imaging to partially invasive methods like Electrocorticography, and fully invasive methods like Microelectrode arrays. The project's mission is to establish a multidisciplinary and inter-sectoral Doctoral Network (DN) for talented researchers, specifically doctoral candidates (DCs), to equip them for research in EEG-based BCI applications. These BCI tools can serve various purposes, including communication, signal analysis, applications in healthcare (rehabilitation, neural prosthetics, diagnostics), industrial applications, and even in the entertainment sector (Virtual Reality, biometrics). Early scientific independence is one of key goals of PhD training programmes.
Project's web page
The European Union funds the project under Marie Skłodowska-Curie Actions (MSCA) program.

Advanced Physiological Estimation of Cognitive States in Neurorehabilitation Tasks using Brain-Computer Interfaces and Head-Mounted Displays (BCI-HMD) for Environment Modification

Impairment of attention, engagement or cognitive performance in many day-time processes poses a risk of procedural errors, disengagement on the task, or simply frustration ending the subject's interest to continue. To solve this problem, many methods have been developed and systems that automatically monitor the physiological and cognitive state of individual subjects tested. Although partial success has been achieved and reported in the literature, there is still a need to have robust, portable, easy-to-deploy system. This project focuses on monitoring subjects' mental states during tasks in a non-invasive brain-computer interface (BCI) environment. Non-invasive BCI represents a pathway between the brain's electrical activity and an external device, for example, a computer or robotic limb. The external environment can be expanded into virtual reality (VR) scenarios realized through head-mounted displays (HMDs). The combination produces a compact BCI-HMD system that is exceptionally flexible and rich for implementing various scenarios and tasks. An important factor in successful neurorehabilitation is the patient's full involvement in the task. Monitoring their cognitive state allows the therapist to modify/update the environment in real time, while the session is taking place, and thereby keep/motivate the patient’s interest and attention to training. This project aims on:
1. creating robust methods that combine AI/ML pipelines with neurophysiological interpretation for monitoring of the cognitive state of subjects based on brain activity through electroencephalography (EEG);
2. testing them in a context of neurorehabilitation task.
Project is funded by the MIT-Slovakia Seed Fund, MISTI Global Seed Funds.

Trustworthy human–robot and therapist–patient interaction in virtual reality (TInVR)

We aim to study specific forms of social interaction using state-of-the-art technology - virtual reality (VR) which is motivated by its known benefits. The project has two main parts, human–robot interaction (HRI) and therapist– patient interaction (TPI). The interactions are enabled using head-mounted displays and controllers allowing the human to act in VR. We propose two research avenues going beyond the state-of-the-art in respective contexts. In HRI, we will develop scenarios allowing the humanoid robot to learn, understand and imitate human motor actions using flexible feedback. Next, we develop scenarios for testing and validating human trust in robot behavior based on multimodal signals. We will also investigate physical interaction with a humanoid robot NICO. In TPI with stroke patients, we develop a series of VR-based occupational therapy procedures for motor and cognitive impairment neurorehabilitation using an active and passive brain-computer interface, and we will validate these procedures. We expect observations from HRI experiments to be exploited in TPI. The proposed project is highly multidisciplinary, combining knowledge and research methods from psychology, social cognition, robotics, machine learning and neuroscience. We expect to identify features and mechanisms leading to trustworthy processes with a human in the loop, as a precondition of success, be it a collaborative task or treatment in VR.
Project is funded by the Slovak Research and Development Agency, APVV-21-0105.

Towards an ecologically valid symbiosis of BCI and head-mounted VR displays: focus on collaborative post-stroke neurorehabilitation (ReHaB)

A growing body of evidence suggests that integrated technologies of brain-computer interfaces (BCI) and virtual reality (VR) environments provide a flexible platform for a series of neurorehabilitation therapies, including significant post-stroke motor recovery and cognitive-behavioural therapy. When immersed in such an environment, the subject's perceptual level of social interaction is often impaired due to the sub-optimal quality of the interface lacking the social aspect of human interactions. We propose a user-friendly wearable low-power smart BCI system with an ecologically valid VR environment in which both the patient and therapist collaboratively interact via their person-specific avatar representations. On the one hand, the patient voluntarily, and in a self-paced manner, manages their activity in the environment and interacts with the therapist via a BCI-driven mental imagery process. This process is computed and rendered in real-time on an energy efficient wearable device. On the other hand, the therapist's unlimited motor and communication skills allow him to fully control the environment. Thus, the VR environment may be flexibly modified by the therapist allowing for different occupational therapy scenarios to be created and selected following the patient's recovery needs, mental states, and instantaneous responses. Careful attention will be paid to balance known neurophysiological evidence of the process with artificial intelligence (AI) within the active BCI protocols to avoid running into conceptual pitfalls. Computed features of EEG signals will serve to monitor the patient's engagement, cognitive workload, or mental fatigue in real-time. These indicators will be combined with observable patient’s performance and behaviours to improve the accuracy of mental state estimation. Exceeding critical mental state levels will signal the therapist to activate appropriate countermeasures in the form of environmental and task changes. To challenge and overcome existing technologies, commercially available head-mounted VR displays (HMD) combined with miniaturized energy- efficient microcontroller units will be employed for EEG signal processing, BCI discrimination and on-board classification implementation, and a full-duplex communication with the HMD controllers. Advanced dry EEG sensors suitable to operate and be placed on the scalp without interfering with the HMD will be developed and tested. A novel patient-to-therapist multimodal collaborative environment augmented through VR immersion and by AI monitored patient’s brain activity will be created. By combining these pieces, a low-power wearable BCI-HMD system will be constructed. A series of clinical studies will validate the system.
Project is funded by national agencies (CH,IL,LV,PL,SK). See

Smart deep brain stimulation as a treatment strategy in treatment-resistant depression

Impaired connectivity between different brain areas underlines pathophysiology of multiple brain disorders. It is possible that impaired connectivity between prefrontal cortex and ventral pallidum is involved in depression. Smart deep brain simulation, combining real-time detection of the neuronal activity in in prefrontal cortex with the stimulation of ventral tegmental area, might be thus an effective in depression. We aim to examine the cortico-tegmental connectivity and to test the antidepressant-like effectiveness of the smart deep brain stimulation in an animal model of depression.
Project is funded by the VEGA 2/0057/22 grant.

Causal analysis of measured signals and time series

The project is focused on the causal analysis of measured time series and signals. It builds on previous results of the project regarding the generalization of the Granger test and proposals for new tests in the reconstructed state spaces. The aim is to develop new methods and algorithms for bivariate and multidimensional causal analysis. We will understand the investigated time series and signals as one-dimensional manifestations of more complex systems or subsystems. We will extend the detection of causality between the two systems to multivariate cases - dynamic networks with nodes characterized by time series. Such complex networks are widespread in the real world. Biomedical applications are among the best known. Brain activity, determined by multichannel electroencephalographic signals, is a crucial example. We will show that causality research is currently at a stage that allows for ambitious goals in studying effective connectivity (i.e., directed interactions, not structural or functional interconnections) in the brain.
Project is funded by the VEGA 2/0023/22 grant.

Enhancing cognition and motor rehabilitation using mixed reality (ECoReMiR)

Technological advancements based on mixed reality (MR) offer various challenges for research and medical treatment. The project focuses on two objectives related to healthy subjects and hemiparetic patients after stroke. First, we will test the hypothesis whether cognitive training using appropriately designed MR environment will enhance perceptual and cognitive performance in healthy subjects. This will be tested by computerized psychological experiments as well as by measuring event-related potentials or ERPs. Second, we will test the hypothesis whether experience with training in MR (in combination with motor-imagery based brain-computer interface developed by us) will enhance oscillatory sensory-motor rhythms. This will be tested by measuring subject's EEG activity before and after each training session, clinical testing, as well as by the questionnaires aiming to learn about human factors including mental fatigue, motivation, irritation or sleepiness due to training. In both objectives, we will design and implement a set of testing procedures, carry out a battery of dedicated experiments, and critically evaluate the results with the goal to validate MR designs.
Project was funded by the Slovak Research and Development Agency, APVV-16-0202.

Brain-computer interface with robot-assisted training for rehabilitation (BCI-RAS)

Within the project we developed an advanced intelligent system allowing the users to go through the process of self-controlled training of motor pathways. To meet this goal we combined the brain-computer interface (BCI) technology with a robotic arm system into a compact system that is primarily used as a robot-assisted neurorehabilitation tool. The BCI directly uses the signal of the brain electrical activity, which allows to users operating the environment without any muscular activation. As a part of this development we are addressed several critical issues ranging from signal acquisition and selection of the proper BCI paradigm to the evaluation of the affective state, cognitive load and system acceptability of the environment by users. We addressed these issues by using new signal processing and machine learning algorithms, training protocols and intelligent methods for the users' physiological state changes detection and monitoring. We used and validated the novel pre-training and model construction training protocols, including the mirror visual feedback and neurofeedback training. The system was tested in practice on selected patients with motor impairments caused by stroke, as well as on healthy volunteers.
Project was funded by the Slovak Research and Development Agency, APVV-0668-12.

Video: Neurorehabilitation training with RoboArm (for Slovak version click here)

Effects of sleep disturbances on day-time neurocognitive performance in patients with stroke (SleepCog)

Sleep deprivation, whether from disorder or lifestyle, whether acute or chronic, poses a significant risk in day-time cognitive performance, excessive somnolence, impaired attention or decreased level of motor abilities. Sleep deprivation is closely related to sleep fragmentation often associated with short several second long arousals. Although limited studies of partial sleep restriction and sleep fragmentation have revealed important sleep indices leading to cognitive deficits, a challenging question how a typical, good quality, structure of sleep should look like remains open. To improve these results the project investigated and evaluated a novel probabilistic sleep model. In a preliminary series of tests on healthy subjects it has been shown that the model contains significantly more objective information about external measures of the sleep quality than the traditional sleep staging. Patients with specific cerebral lesions were studied in the project. These patients are strongly vulnerable to sleep disturbances that often lead to deficits in their day-time cognitive and attentional performance.
Project was funded by the Ministry of Health of the Slovak Republic, MZ 2013/46-SAV-6.