Gernot R Müller-Putz

• Full Professor for Semantic Data Analysis
• Head of Department at Graz University of Technology

Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feature extraction, and the scarcity of high-quality gait EEG datasets. To address these issues, we propose EEG2GAIT, a novel hierarchical graph-based model that captures mul...

Introduction Movement-based brain-computer interfaces (BCIs) utilize brain activity generated during executed or attempted movement to provide control over applications. By relying on natural movement processes, these BCIs offer a more intuitive control compared to other BCI systems. However, non-invasive movement-based BCIs utilizing electroenceph...

Background/Objectives: Tactile gnosis derives from the interplay between the hand’s tactile input and the memory systems of the brain. It is the prerequisite for complex hand functions. Impaired sensation leads to profound disability. Various invasive and non-invasive sensory substitution strategies for providing feedback from prostheses have been...

Objective. The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored. Approach. We utilized electroencephalography (EEG) signals and hand position reco...

Background/Objectives: Tactile gnosis derives from the interplay between the hand’s tactile input and the memory systems of the brain. It is the prerequisite for complex hand functions. Impaired sensation leads to profound disability. Various invasive and non-invasive sensory substitution strategies for providing feedback from prostheses have been...

Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work char...

This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer...

Augmented reality (AR) technologies enhance a user’s physical environment by providing contextual information about their surroundings. This information might appear incongruent to users, either due to their current mental context or factual errors in the data. This paper explores the feasibility of incongruency decoding using electroencephalograph...

Objective. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events into correct or erroneous to the present day. Due to this pr...

Augmented reality (AR) allows users to display additional digital information about their physical environment. We present an interactive AR study, in which participants manipulated a Rubik's cube which served as a physical referent for presented digital information showing the current status of the cube. In 30% of the instances, the presented info...

We present a framework for applying time-varying autoregressive (TVAR)models to multi-trial, multi-channel data, validated usingelectroencephalography (EEG) from motor imagery tasks. Additionally, weintroduce a novel TVAR feature that enhances decoding accuracy in BCIs.

Monitoring the spectral characteristics of brain signals can provide insights into the underlying processes responsible for their generation. In brain-computer interface (BCI) applications, this is relatively important in decoding neural activity as it can provide a means to differentiate between various tasks or mental states. To capture spectral...

Decades of research thoroughly established various neural correlates of processing discrete errors, i.e., events that may be classified as either correct or wrong. However, despite many successful demonstra tions of brain-computer interfaces (BCIs) utilizing these discrete correlates, a range of everyday tasks (e.g., car driving) requires fine-tune...

Understanding how the brain plans reaching movements is crucial in designing a brain-computer interface (BCI) system for motor control. It is still unclear which referencing frame the brain uses to plan the movement. In this study, we investigated the global representation of a referencing frame during reaching planning via a low-frequency electroe...

Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG d...

Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple comput...

Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user’s mental context, can considerably impact user experience. This study cha...

Background: In electroencephalographic (EEG) or electrocorticographic (ECoG) experiments, visual cues are commonly used for timing synchronization but may inadvertently induce neural activity and cognitive processing, posing challenges when decoding self-initiated tasks. New method: To address this concern, we introduced four new visual cues (Fade...

Accident analyses repeatedly reported the considerable contribution of run-off-road incidents to fatalities in road traffic, and despite considerable advances in assistive technologies to mitigate devastating consequences, little insight into the drivers’ brain response during such accident scenarios has been gained. While various literature docume...

The classification of handwritten letters from invasive neural signals has lately been subject of research to restore communication abilities in people with limited movement capacities. This study explores the classification of ten letters (a,d,e,f,j,n,o,s,t,v) from non-invasive neural signals of 20 participants using two methods: the direct classi...

Background In electroencephalographic (EEG) or electrocorticographic (ECoG) experiments, visual cues are commonly used for timing synchronization but may inadvertently induce neural activity and cognitive processing, posing challenges when decoding self-initiated tasks. New Method To address this concern, we introduced four new visual cues (Fade,...

Technological advances in head-mounted displays (HMDs) facilitate the acquisition of physiological data of the user, such as gaze, pupil size, or heart rate. Still, interactions with such systems can be prone to errors, including unintended behavior or unexpected changes in the presented virtual environments. In this study, we investigated if multi...

Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of h...

Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG d...

Recent studies in the domain of invasive brain-computer interfaces (BCIs) have revealed that neural activity recorded during the observation of robotic movements in a reach-and-grasp task carries information that can be utilized to improve the active online decoding of motor intention. In the non-invasive domain, the spectral characteristics of hum...

Locked-in Syndrome (LIS) severely restricts the communication abilities of individuals due to extensive paralysis. The Intracranial Neuro Telemetry to Restore communication (INTRECOM) project aims to aid patients in overcoming these limitations by developing a fully implantable brain computer interface (BCI) system based on state-of-the-art technol...

Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain–computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech moveme...

In the recent past, many organizations and people have substituted face-to-face meetings with videoconferences. Among others, tools like Zoom, Teams, and Webex have become the “new normal” of human social interaction in many domains (e.g., business, education). However, this radical adoption and extensive use of videoconferencing tools also has a d...

Error perception is known to elicit distinct brain patterns, which can be used to improve the usability of systems facilitating human-computer interactions, such as brain-computer interfaces. This requires a high-accuracy detection of erroneous events, e.g., misinterpretations of the user’s intention by the interface, to allow for suitable reaction...

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscl...

Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to automatically learn features from lightly pre-processed signals. However, DNNs for kinematics decoding...

Performance monitoring and feedback processing - especially in the wake of erroneous outcomes - represent a crucial aspect of everyday life, allowing us to deal with imminent threats in the short term but also promoting necessary behavioral adjustments in the long term to avoid future conflicts. Over the last thirty years, research extensively anal...

Objective. The maintenance of balance is a complicated process in the human brain, which involves multisensory processing such as somatosensory and visual processing, motor planning and execution. It was shown that a specific cortical activity called perturbation-evoked potential (PEP) appears in the electroencephalogram (EEG) during balance pertur...

In this study, across-participant and across-session transfer learning was investigated to minimize the calibration time of the brain–computer interface (BCI) system in the context of continuous hand trajectory decoding. We reanalyzed data from a study with 10 able-bodied participants across three sessions. A leave-one-participant-out (LOPO) model...

In today’s fast-paced world, our brain spends almost half of our waking hours distracted from current environmental stimuli, often referred to as mind wandering in the scientific literature. At the same time, people frequently have several hours daily screen time, signifying the ubiquity of digital technologies. Here, we investigate mind wandering...

For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invas...

The usefulness of error-related potentials (ErrPs) for control in non-invasive Brain-Computer interface (BCI) research has been established over the last decades. To continuously correct for erroneous action of an end effector (e.g., robot arm) in a BCI however, these neural correlates relating only to the discrete perception of errors remain probl...

Bibian et al. show in their recent paper (Bibi\'an et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can generate artefacts that can cause an overoptimistic estimation of the classification accuracy. They speculate that such artefacts jeopardise the interpretation of the resul...

Objective. In people with a cervical spinal cord injury (SCI) or degenerative diseases leading to limited motor function, restoration of upper limb movement has been a goal of the brain-computer interface field for decades. Recently, research from our group investigated non-invasive and real-time decoding of continuous movement in able-bodied parti...

Neuroimaging studies have provided proof that loss of balance evokes specific neural transient wave complexes in electroencephalography (EEG), called perturbation evoked potentials (PEPs). Online decoding of balance perturbations from ongoing EEG signals can establish the possibility of implementing passive brain-computer interfaces (pBCIs) as a pa...

Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project “Feel Your Reach”. In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed peop...

Electroencephalographic (EEG) correlates of movement have been studied extensively over many years. In the present work, we focus on investigating neural correlates that originate from the spine and study their connectivity to corresponding signals from the sensorimotor cortex using multivariate autoregressive (MVAR) models. To study cortico-spinal...

Several studies showed evidence supporting the possibility of hand trajectory decoding from low-frequency electroencephalography (EEG). However, the decoding in the source space via source localization is scarcely investigated. In this study, we tried to tackle the problem of collinearity due to the higher number of signals in the source space by t...

[This corrects the article DOI: 10.3389/fnhum.2021.746081.].

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering the breadth of topics in BCI (also called brain-machine interface) research. Some workshops provided detailed examinations of methods, hardware, o...

Introduction: Advantageous effects of biological motion (BM) detection, a low-perceptual mechanism that allows the rapid recognition and understanding of spatiotemporal characteristics of movement via salient kinematics information, can be amplified when combined with motor imagery (MI), i.e., the mental simulation of motor acts. According to Jeann...

The goal of this study was to implement a Riemannian geometry (RG)-based algorithm to detect high mental workload (MWL) and mental fatigue (MF) using task-induced electroencephalogram (EEG) signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time...

Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-base...

There is an ongoing discussion in the NeuroIS (Neuro-Information-Systems) discipline on whether consumer-grade EEG instruments are as suitable for scientific research as research-grade instruments. Considering the increasing adoption of consumer-grade instruments along with the fact that many NeuroIS EEG papers used such tools, this debate is funda...

We investigated the cortical connectivity patterns that arise in subjects with spinal cord injury (SCI) during attempted hand and arm movements using multivariate autoregressive (MVAR) models and electroencephalographic (EEG) signals. The MVAR models were fitted using multiple trials from multiple subjects in order to capture general connectivity c...

Our minds tend to frequently drift away from present technology-related situations and tasks. Against this background, we seek to provide a better understanding of mind-wandering episodes while using information technology and its link to decisive variables of Information Systems research, such as performance, creativity and flow. Since the academi...

In this work, the hand trajectory decoding was investigated in the source space. A couple of di-mensionality reduction techniques were utilized to reduce the number of the source-space signals, namely, computing the mean, principle component analysis (PCA), locality preserving projection (LPP). The decoding performances from the source-space approa...

Recent research from our group has shown that non-invasive continuous online decoding of executed movement from non-invasive low-frequency brain signals is feasible. In order to cater the setup to actual end users, we proposed a new paradigm based on attempted movement and after conducting a pilot study, we hypothesize that user control in this set...

Motor imagery is a popular technique employed as a motor rehabilitation tool, or to control assistive devices to substitute lost motor function. In both said areas of application, artificial somatosensory input helps to mirror the sensorimotor loop by providing kinesthetic feedback or guidance in a more intuitive fashion than via visual input. In t...

Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-base...

Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain–computer interfaces (BCIs) for people with motor disabilities. Objective. The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG featu...

Grasping movements are known to activate the fronto-parietal brain networks both in human and non-human primates. However, it is unclear if these activations represent properties of the objects or hand postures or both at different stages of the movement. We manipulated the intrinsic properties of the objects and the grasping types in order to crea...

The classification of different fine hand movements from electroencephalogram (EEG) signals represents a relevant research challenge, e.g., in BCI applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar, and lateral grasp) were performed in a self-paced modality. We trained and...

Restoration of grasping has the highest priority for people with cervical spinal cord injury (SCI). This chapter describes the non-invasive brain–computer interface (BCI)-controlled grasp neuroprosthesis developed within the European Horizon 2020 project MoreGrasp. Based on former projects of the collaborators, several innovative technologies were...

Although motor imagery was used for the first BCI-controlled neuroprosthetic applications, it has turned out that it is a limited experimental strategy when it comes to control of more than two degrees of freedom. More natural ways for controlling the hand and ultimately the whole arm function have been identified in using attempted movement and ev...

This chapter gives an overview on the foundation of the electroencephalogram (EEG), describes its phenomena, and explains how EEG recordings can be performed. This includes types of electrodes, amplifiers, and known artifacts. Further on, the term brain–computer interface (BCI) is introduced and its components and useful experimental paradigms get...

With neuroscientific investigations of analyzing neuronal activity of motor areas directly in the cortex in the 1980s, the foundation of invasive brain–computer interfaces (BCIs) or brain–machine interfaces (BMIs) was laid. Since then, researchers were studying motor cortex activity with multielectrode arrays, first in primates and later in humans,...

For brain–computer interface (BCI) users, the awareness of an error is associated with a cortical signature known as an error-related potential (ErrP). The incorporation of ErrP detection into BCIs can improve their performance. Objective. This work has three main aims. First, we investigate whether an ErrP classifier is transferable from able-bodi...

This book aims to provide a comprehensive overview of the current state of the art of practical applications of neuroprosthesis based on functional electrical stimulation for restoration of motor functions lost by a spinal cord injury and brain-computer interfaces for their control. The book covers numerous topics starting with basics about spinal...

CYBATHLON is an international championship where people with severe physical disabilities compete with the aid of state-of-the-art assistive technology. In one of the disciplines, the BCI Race, tetraplegic pilots compete in a computer game race by controlling an avatar with a brain-computer interface (BCI). This competition offers a perfect opportu...

This book presents the proceedings of the NeuroIS Retreat 2021, June 1-3, virtual conference, reporting on topics at the intersection of information systems (IS) research, neurophysiology and the brain sciences. Readers will discover the latest findings from top scholars in the field of NeuroIS, which offer detailed insights on the neurobiology und...

A BCI user awareness of an error is associated with a cortical signature named error-related potential (ErrP). The incorporation of ErrPs' detection in BCIs can improve BCIs' performance. This work is three-folded. First, we investigate if an ErrP classifier is transferable from able-bodied participants to participants with spinal cord injury (SCI)...

Application of good methodological standards is critical in science because such standards constitute a precondition for high-quality research results. A fundamental question which has recently been raised in the NeuroIS literature is whether consumer-grade electroencephalography (EEG) instruments offer measurement quality that is comparable to res...

Passive brain-computer interfaces (pBCIs) can be used to inform humans about their current mental state or in human-machine interaction (HMI) scenario. We introduced the perturbation-evoked potential (PEP) in the context of pBCI and are further investigating how neural correlates in a HMI could interact. In the current study we investigate the neur...

Objective. An important part of restoring motor control via a brain-computer interface is to close the sensorimotor feedback loop. As part of our investigations into vibrotactile kinaesthetic feedback of arm movements, we studied electroencephalographic signals in the δ, µ and β bands obtained during a center-out movement task with four conditions:...

Objective. One of the main goals in brain–computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencep...

The aim of this work was to re-evaluate electrophysiological data from a previous study on motor imagery (MI) with a special focus on observed inter- and intra-individual differences. More concretely, we investigated event-related desynchronization/synchronization patterns during sports MI (playing tennis) compared with simple MI (squeezing a ball)...

Reaching and grasping is an essential part of everybody’s life, it allows meaningful interaction with the environment and is key to independent lifestyle. Recent electroencephalogram (EEG)-based studies have already shown that neural correlates of natural reach-and-grasp actions can be identified in the EEG. However, it is still in question whether...

Objective. Continuous decoding of voluntary movement is desirable for closed-loop, natural control of neuroprostheses. Recent studies showed the possibility to reconstruct the hand trajectories from low-frequency (LF) electroencephalographic (EEG) signals. So far this has only been performed offline. Here, we attempt for the first time continuous o...

Brain-computer interfaces (BCIs) provide more independence to people with severe motor disabilities but current BCIs' performance is still not optimal and often the user's intentions are misinterpreted. Error-related potentials (ErrPs) are the neurophysiological signature of error processing and their detection can help improving a BCI's performanc...

Decoding upper-limb movements in invasive recordings has become a reality, but neural tuning in non-invasive low-frequency recordings is still under discussion. Recent studies managed to decode movement positions and velocities using linear decoders, even developing an online system. The decoded signals, however, exhibited smaller amplitudes than a...

Eye movements and blinks contaminate electroencephalographic (EEG) and magnetoencephalographic (MEG) activity. As the eye moves, the corneo-retinal dipole (CRD) and eyelid introduce potential/field changes in the M/EEG activity. These eye artifacts can affect a brain-computer interface and thereby impinge on neurofeedback quality. Here, we introduc...

Movement preparation and initiation have been shown to involve large scale brain networks. Recent findings suggest that movement preparation and initiation are represented in functionally distinct cortical networks. In electroencephalographic (EEG) recordings, movement initiation is reflected as a strong negative potential at medial central channel...