Thorsten O. Zander

Brandenburg University of Technology Cottbus - Senftenberg | BTU · Chair for Neuroadaptive Human-Computer Interaction

An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-wor...

In heavily automated environments, such as modern-day cockpits, operators perform prolonged monitoring tasks, during which critical events may occur only rarely. A user model can help inform the automated environment about the operator's current mental state, allowing it to adapt accordingly and provide support. By using a passive brain-computer in...

Neuroadaptive technology uses a measure of brain activity to obtain implicit input, enabling forms of interaction that differ from traditional human-computer interaction in a number of important ways. These distinctions between traditional and neuroadaptive forms of interaction make it difficult to describe or define neuroadaptive technologies usin...

In this study, we investigated whether electrode shifts in the recording electroencephalogram (EEG) affect the performance of a pre-calibrated brain-computer interface (BCI). In particular, we considered single-electrode shifts, which occur after classifier training, but before classifier application. Our goal was to determine which aspects of elec...

We investigated whether a passive brain–computer interface that was trained to distinguish low and high mental workload in the electroencephalogram (EEG) can be used to identify (1) texts of different readability difficulties and (2) texts read at different presentation speeds. For twelve subjects we calibrated a subject-dependent, but task-indepen...

This study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots’ perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots’ perception and respo...

A model‐based approach for cognitive assistance is proposed to keep track of pilots' changing demands in dynamic situations. Based on model‐tracing with flight deck interactions and EEG recordings, the model is able to represent individual pilots' behavior in response to flight deck alerts. As a first application of the concept, an ACT‐R cognitive...

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics...

Objective: The interpretation of neurophysiological measurements has a decades-long history, culminating in current real-time brain-computer interfacing (BCI) applications for both patient and healthy populations. Over the course of this history, one focus has been on the investigation of cortical responses to specific stimuli. Such responses can...

In the context of brain-computer interfacing, it is important to investigate what regions of the brain a classifier focuses on. For one, this will clarify to what extent the classifier relies on brain activity, as opposed to undesirable non-cortical signals. More generally, the practice is informative as it allows conclusions to be drawn about the...

Background: Electroencephalography (EEG) is a popular method to monitor brain activity, but it is difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings. Simu...

Cognitive probing combines the ability of computers to interpret ongoing measures of arbitrary brain activity, with the ability of those same computers to actively elicit cognitive responses from their users. Purposefully elicited responses can be interpreted in order to learn about the user, enable symbiotic and implicit interaction, and support n...

Numerous methods have been developed to gain reliable real-time remote control over pilotless flying aircraft and to perform teleoperation. Recently, state-of-the-art brain–computer interface (BCI) research has provided an avant-garde approach to reach this goal. Due to its broad range of application, BCI has been the center of attention as a promi...

Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that...

We describe a live demonstration we performed at the Museum of Stillness, consisting of a closed-loop passive brain-computer interface focusing on a state of relaxation. This state was measured while participants were contemplating a painting in the Museum of Stillness, Berlin. Participants were provided with auditory feedback in the form of the so...

State of the art brain-computer interfaces (BCIs) largely focus on detecting single, specific, often experimentally induced or manipulated aspects of the user state. In a less controlled, more naturalistic environment, a larger variety of mental processes may be active and possibly interacting. When moving BCI applications from the lab to real-life...

This paper introduces a completely hands-free version of Tetris that uses eye tracking and passive brain-computer interfacing (a real-time measurement and interpretation of brain activity) to replace existing game elements, as well as introduce novel ones. In Meyendtris, dwell time-based eye tracking replaces the game's direct control elements, i.e...

Passive brain-computer interfaces have been formally introduced and defined almost a decade ago, and have gained considerable attention since then. Here, we provide a new perspective on this field. We refer to neuroadaptive systems, and identify a key aspect with regards to which various passive BCI-based systems differ from each other: interactivi...

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby intro...

The eBook of this Frontiers Research Topic is divided into four sections, defined by the primary research methods used to address a variety of neuroergonomic research questions. The scientific topics range from air traffic control and automation, over mental load detection and the use of brain activity to control a system (brain computer interfaces...

We examined physiological responses to behavior of an Adaptive Cruise Control (ACC) system during real driving. ACC is an example of automating a task that used to be performed by the user. In order to preserve the link between the user and an automated system such that they work together optimally, physiological signals reflecting mental state may...

Automatic detection of the current task load of a surgeon in the theatre in real time could provide helpful information, to be used in supportive systems. For example, such information may enable the system to automatically support the surgeon when critical or stressful periods are detected, or to communicate to others when a surgeon is engaged in...

We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considere...

The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through...

Brain-computer interfaces can provide an input channel from humans to computers that depends only on brain activity, bypassing traditional means of communication and interaction. This input channel can be used to send explicit commands, but also to provide implicit input to the computer. As such, the computer can obtain information about its user t...

Significance The human brain continuously and automatically processes information concerning its internal and external context. We demonstrate the elicitation and subsequent detection and decoding of such “automatic interpretations” by means of context-sensitive probes in an ongoing human–computer interaction. Through a sequence of such probe–inter...

please go to https://dx.doi.org/10.14279/depositonce-4887 to download the full conference proceedings

The central question of this Frontiers Research Topic is: What can we learn from brain and other physiological signals about an individual's cognitive and affective state and how can we use this information? This question reflects three important issues which are addressed by the 22 articles in this volume: (1) the combination of central and periph...

Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to...

According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presentin...

Brain computer interface (BCI) technology has been experiencing dynamic development over the past years. As a result, more and more possible applications of this technology are being investigated. One vision pursued in a European research project is brain controlled aircraft flight, where manual inceptors are replaced by electroencephalography (EEG...

Despite significant advances in context sensing and inference since its inception in the late 1990s, context-aware computing still doesn't implement a holistic view of all covert aspects of the user state. Here, the authors introduce the concept of cognitive context as an extension to the current notion of context with a cognitive dimension. They a...

In this chapter a specific aspect of Physiological Computing, that of implicit Human–Computer Interaction, is defined and discussed. Implicit Interaction aims at controlling a computer system by behavioural or psychophysiological aspects of user state, independently of any intentionally communicated command. This introduces a new type of Human–Comp...

Research on the neurophysiological correlates of visuomotor integration and learning (VMIL) has largely focused on identifying learning-induced activity changes in cortical areas during motor execution. While such studies have generated valuable insights into the neural basis of VMIL, little is known about the processes that represent the current s...

The precursor study presented here describes one further step towards investigating active BCIs in realistic scenarios. We invited six trained pilots to control horizontal flight in a flight simulator via an active BCI. Performance was tracked via standard BCI measures and performance in operational flight tasks. Results indicate that standard BCI...

We present a reliable reactive BCI based on steady-state somatosensory evoked potentials (SSSEPs). As the stimulation frequencies are higher than 35 Hz this system ensures no interference with BCIs relying on ERPs or SMR. Hence, the presented system can be combined with other BCIs broadening the bandwidth of communication.

Tracking eye movements to control technical systems is becoming increasingly popular; the use of eye movements to direct a cursor in human-computer interaction (HCI) is particularly convenient and caters for both healthy and disabled users alike. However, it is often difficult to find an appropriate substitute for the click operation, especially wi...

We provide a simple method, based on volume conduction models, to quantify the neurophysiological plausibility of independent components (ICs) reconstructed from EEG/MEG data. We evaluate the method on EEG data recorded from 19 subjects and compare the results with two established procedures for judging the quality of ICs. We argue that our procedu...

The application of brain-computer interfaces (BCI) shows promising results in stroke rehabilitation, but the underlying neural substrates and processes of successful BCI-based neurorehabilitation remain unclear. The goal of our present work was to identify the brain areas associated with successful visuomotor integration and motor learning (VMIL),...

Despite intensive efforts, no significant benefit of rehabilitation robotics in post-stroke motor-recovery has yet been demonstrated in large-scale clinical trials. The present work is based on the premise that future advances in rehabilitation robotics require an enhanced understanding of the neural processes involved in motor learning after strok...

Verbindet man ein menschliches Gehirn über eine Hirn-Rechner-Schnittstelle (BCI) mit einer Maschine, so kann das resultierende Mensch-Maschine-System (MMS) Informationen über die Hirnaktivität des Nutzers ableiten. Um diese Verbindung aufbauen zu können, werden Methoden aus dem Bereich des Maschinellen Lernens und Grundlagenwissen über die Interpre...

Brain-computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems...

The study presented here introduces a Passive BCI detecting responses of the subjects brain on the perception of correct and erroneous auditory signals. 10 experts in music theory who actively play an instrument listened to cadences, sequences of chords, that could have an unexpected, erroneous ending. In consistence with previous studies from the...

When using eye movements for cursor control in human-computer interaction (HCI), it may be difficult to find an appropriate substitute for the click operation. Most approaches make use of dwell times. However, in this context the so-called Midas-Touch-Problem occurs which means that the system wrongly interprets fixations due to long processing tim...

Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain-computer interfacing (BCI), EEG recently has become a tool to enhance human-machine interaction. EEG could be employed in a wider range o...

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modali...

Methods of statistical machine learning have recently proven to be very useful in contemporary brain-computer interface (BCI) research based on the discrimination of electroencephalogram (EEG) patterns. Because of this, many research groups develop new algorithms for both feature extraction and classification. However, until now, no large-scale com...

A Brain–Computer Interface (BCI) provides a new communication channel for severely disabled people who have completely or partially lost control over muscular activity. It is questionable whether a BCI is the best choice for controlling a device if partial muscular activity still is available. For example, gaze-based interfaces can be utilized for...

Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely o...

This chapter introduces a formal categorization of BCIs, according to their key characteristics within HCI scenarios. This comprises classical approaches, which we group into active and reactive BCIs, and the new group of passive BCIs. Passive BCIs provide easily applicable and yet efficient interaction channels carrying information on covert aspec...

We first discuss two MATLAB-centered solutions for real-time data streaming, the environments FieldTrip (Donders Institute, Nijmegen) and DataSuite (Data- River, Producer, MatRiver) (Swartz Center, La Jolla). We illustrate the relative simplicity of coding BCI feature extraction and classification under MATLAB (The Mathworks, Inc.) using a minimali...

Brain-computer interfaces (BCIs) provide insight into ongoing cognitive and affective processes and are commonly used for direct control of human-machine systems (Vidal, 1973). Recently, a different type of BCI has emerged (Cutrell and Tan, 2008; and Zander et al., 2008), which instead focuses solely on the non-intrusive recognition of mental state...

Recently, the use of brain-computer interfaces (BCIs) has been extended from active control to passive detection of cognitive user states. These passive BCI systems can be especially useful for automatic error detection in human-machine systems by recording EEG potentials related to human error processing. Up to now, these so-called error potential...

A Brain-Computer Interface (BCI) directly translates patterns of brain activity to input for controlling a machine. The introduction of methods from statistical machine learning [1] to the field of brain-computer interfacing (BCI) had a deep impact on classification accuracy. It also minimized the effort needed to build up the skill of controlling...

Gaze-based interfaces gained increasing importance in multimodal human-computer interaction research with the improvement of tracking technologies over the last few years. The activation of selected objects in most eye-controlled applications is based on dwell times. This interaction technique can easily lead to errors if the users do not pay very...

Imagine you click on a file on your computer by mistake. The computer processes the information and starts to open the corresponding application. But this takes some time. You immediately recognize your mistake and prepare to close the application right after it opens to continue your intended task. You feel distracted and helpless and your feeling...