Showing papers by "Gernot Müller-Putz published in 2012"

Review of the BCI Competition IV

Abstract: The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.

On the use of neurophysiological tools in is research: developing a research agenda for neurois

Abstract: This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social sciences in capturing presumably objective data directly from the human body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major strengths and weaknesses. It then discusses several promising application areas and research questions where IS researchers can benefit from the use of neurophysiological data. The proposed research topics are presented within three thematic areas: (1) development and use of systems, (2) IS strategy and business outcomes, and (3) group work and decision support. The article concludes with recommendations on how to use neurophysiological tools in IS research along with a set of practical suggestions for developing a research agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.

Toward smarter BCIs: extending BCIs through hybridization and intelligent control

Abstract: This paper summarizes two novel ways to extend brain-computer interface (BCI) systems. One way involves hybrid BCIs. A hybrid BCI is a system that combines a BCI with another device to help people send information. Different types of hybrid BCIs are discussed, along with challenges and issues. BCIs are also being extended through intelligent systems. Software that allows high-level control, incorporates context and the environment and/or uses virtual reality can substantially improve BCI systems. Throughout the paper, we critically address the real benefits of these improvements relative to existing technology and practices. We also present new challenges that are likely to emerge as these novel BCI directions become more widespread.

Relationship Between Electrical Brain Responses to Motor Imagery and Motor Impairment in Stroke

Abstract: Background and Purpose—New strategies like motor imagery based brain–computer interfaces, which use brain signals such as event-related desynchronization (ERD) or event-related synchronization (ERS) for motor rehabilitation after a stroke, are undergoing investigation. However, little is known about the relationship between ERD and ERS patterns and the degree of stroke impairment. The aim of this work was to clarify this relationship. Methods—EEG during motor imagery and execution were measured in 29 patients with first-ever monolateral stroke causing any degree of motor deficit in the upper limb. The strength and laterality of the ERD or ERS patterns were correlated with the scores of the European Stroke Scale, the Medical Research Council, and the Modified Ashworth Scale. Results—Mean age of the patients was 58±15 years; mean time from the incident was 4±4 months. Stroke lesions were cortical (n=8), subcortical (n=11), or mixed (n=10), attributable to either an ischemic event (n=26) or a hemorrhage (n=3...

Is It Significant? Guidelines for Reporting BCI Performance

Abstract: Recent growth in brain-computer interface (BCI) research has increased pressure to report improved performance. However, different research groups report performance in different ways. Hence, it is essential that evaluation procedures are valid and reported in sufficient detail. In this chapter we give an overview of available performance measures such as classification accuracy, cohen’s kappa, information transfer rate (ITR), and written symbol rate. We show how to distinguish results from chance level using confidence intervals for accuracy or kappa. Furthermore, we point out common pitfalls when moving from offline to online analysis and provide a guide on how to conduct statistical tests on (BCI) results.

Decoding of velocities and positions of 3D arm movement from EEG

Abstract: A brain-computer interface (BCI) can be used to control a limb neuroprosthesis with motor imaginations (MI) to restore limb functionality of paralyzed persons. However, existing BCIs lack a natural control and need a considerable amount of training time or use invasively recorded biosignals. We show that it is possible to decode velocities and positions of executed arm movements from electroencephalography signals using a new paradigm without external targets. This is a step towards a non-invasive BCI which uses natural MI. Furthermore, training time will be reduced, because it is not necessary to learn new mental strategies.

What does clean EEG look like

Abstract: Lack of a clear analytical metric for identifying artifact free, clean electroencephalographic (EEG) signals inhibits robust comparison of different artifact removal methods and lowers confidence in the results of EEG analysis. An algorithm is presented for identifying clean EEG epochs by thresholding statistical properties of the EEG. Thresholds are trained on EEG datasets from both healthy subjects and stroke / spinal cord injury patient populations via differential evolution (DE).

Stability and distribution of steady-state somatosensory evoked potentials elicited by vibro-tactile stimulation.

Abstract: Steady-state somatosensory evoked potentials (SSSEPs) have been elicited applying vibro-tactile stimulation to all fingertips of the right hand. Nine healthy subjects participated in two sessions within this study. All fingers were stimulated 40 times with a 200-Hz carrier frequency modulated with a rectangular signal. The frequencies of the rectangular signal ranged between 17 and 35 Hz in 2 Hz steps. Relative band power tuning curves were calculated, introducing two different methods. Person-specific resonance-like frequencies were selected based on the data from the first session. The selected resonance-like frequencies were compared with the second session using an ANOVA for repeated measures to investigate the stability of SSSEPs over time. To determine, if SSSEPs can be classified with a classifier based on unseen data, an LDA classifier was trained with data from the first and applied to data from the second session. Person-specific resonance-like frequencies within a range from 19 to 29 Hz were found. The relative band power of the resonance-like frequencies did not differ significantly between the two sessions. Significant differences were found for the two methods and the used channels. SSSEPs were classified with a hit rate from 51 to 96 %.

Coupling between Intrinsic Prefrontal HbO2 and Central EEG Beta Power Oscillations in the Resting Brain

Abstract: There is increasing interest in the intrinsic activity in the resting brain, especially that of ultraslow and slow oscillations. Using near-infrared spectroscopy (NIRS), electroencephalography (EEG), blood pressure (BP), respiration and heart rate recordings during 5 minutes of rest, combined with cross spectral and sliding cross correlation calculations, we identified a short-lasting coupling (duration s) between prefrontal oxyhemoglobin (HbO2) in the frequency band between 0.07 and 0.13 Hz and central EEG alpha and/or beta power oscillations in 8 of the 9 subjects investigated. The HbO2 peaks preceded the EEG band power peaks by 3.7 s in 6 subjects, with moderate or no coupling between BP and HbO2 oscillations. HbO2 and EEG band power oscillations were approximately in phase with BP oscillations in the 2 subjects with an extremely high coupling (squared coherence ) between BP and HbO2 oscillation. No coupling was identified in one subject. These results indicate that slow precentral (de)oxyhemoglobin concentration oscillations during awake rest can be temporarily coupled with EEG fluctuations in sensorimotor areas and modulate the excitability level in the brains’ motor areas, respectively. Therefore, this provides support for the idea that resting state networks fluctuate with frequencies of between 0.01 and 0.1 Hz (Mantini et.al. PNAS 2007).

Error potential detection during continuous movement of an artificial arm controlled by brain–computer interface

Abstract: Patients who benefit from Brain-Computer Interfaces (BCIs) may have difficulties to generate more than one distinct brain pattern which can be used to control applications. Other BCI issues are low performance, accu- racy, and, depending on the type of BCI, a long preparation and/or training time. This study aims to show possible solu- tions. First, we used time-coded motor imagery (MI) with only one pattern. Second, we reduced the training time by recording only 20 trials of active MI to set up a BCI classifier. Third, we investigated a way to record error potentials (ErrPs) during continuous feedback. Ten subjects controlled an arti- ficial arm by performing MI over target time periods between 1 and 4 s. The subsequent movement of this arm served as continuous feedback. Discrete events, which are required to elicit ErrPs, were added by mounting blinking LEDs on top of the continuously moving arm to indicate the future move- ments. Time epochs after these events were used to evaluate ErrPs offline. The achieved error rate for the arm movement was on average 26.9%. Obtained ErrPs looked similar to results from the previous studies dealing with error detection and the detection rate was above chance level which is a positive outcome and encourages further investigation.

BCI Software Platforms

Abstract: In this chapter, we provide an overview of publicly available software platforms for brain–computer interfaces. We have identified seven major BCI platforms and one platform specifically targeted towards feedback and stimulus presentation. We describe the intended target user group (which includes researchers, programmers, and end users), the most important features of each platform such as availability on different operating systems, licences, programming languages involved, supported devices, and so on. These seven platforms are: (1) BCI2000, (2) OpenViBE, (3) TOBI Common Implementation Platform (CIP), (4) BCILAB, (5) BCI++, (6) xBCI, and (7) BF++. The feedback framework is called Pyff. Our conclusion discusses possible synergies and future developments, such as combining different components of different platforms. With this overview, we hope to identify the strengths and weaknesses of each available platform, which should help anyone in the BCI research field in their decision which platform to use for their specific purposes.

Proposing a Standardized Protocol for Raw Biosignal Transmission

Abstract: In this paper, we propose a standardized interface called TiA (TOBI interface A) to transmit raw biosignals, supporting multirate and block-oriented transmission of different kinds of signals from various acquisition devices (e.g., EEG, electrooculogram, near-infrared spectroscopy signals, etc.) at the same time. To facilitate a distinction between those kinds of signals, so-called signal types are introduced. TiA is a single-server, multiple-client system, whereby clients can connect to the server at runtime. Information transfer between client and server is divided into control and data connections. The control connections use transmission control protocol (TCP) and transmit extensible-markup-language (XML)-encoded meta information. The data transmission utilizes a user datagram protocol (UDP) or TCP with a binary data stream. A standardized handshaking procedure for the connection setup and a standardized binary data packet has been defined. Thus, a standardized layer, abstracting used hardware devices and facilitating distributed raw data transmission in a standardized way, has been evolved. A cross-platform library, implemented in C++, is available for download.

Switching between Manual Control and Brain-Computer Interface Using Long Term and Short Term Quality Measures.

Abstract: Assistive devices for persons with limited motor control translate or amplify remaining functions to allow otherwise impossible actions. These assistive devices usually rely on just one type of input signal which can be derived from residual muscle functions or any other kind of biosignal. When only one signal is used, the functionality of the assistive device can be reduced as soon as the quality of the provided signal is impaired. The quality can decrease in case of fatigue, lack of concentration, high noise, spasms, tremors, depending on the type of signal. To overcome this dependency on one input signal, a combination of more inputs should be feasible. This work presents a hybrid Brain-Computer Interface (hBCI) approach where two different input signals (joystick and BCI) were monitored and only one of them was chosen as a control signal at a time. Users could move a car in a game-like feedback application to collect coins and avoid obstacles via either joystick or BCI control. Both control types were constantly monitored with four different long term quality measures to evaluate the current state of the signals. As soon as the quality dropped below a certain threshold, a monitoring system would switch to the other control mode and vice versa. Additionally, short term quality measures were applied to check for strong artifacts that could render voluntary control impossible. These measures were used to prohibit actions carried out during times when highly uncertain signals were recorded. The switching possibility allowed more functionality for the users. Moving the car was still possible even after one control mode was not working any more. The proposed system serves as a basis that shows how BCI can be used as an assistive device, especially in combination with other assistive technology.

Development of a non-invasive, multifunctional grasp neuroprosthesis and its evaluation in an individual with a high spinal cord injury

Abstract: Over the last decade the improvement of a missing hand function by application of neuroprostheses in particular the implantable Freehand system has been successfully shown in high spinal cord injured individuals. The clinically proven advantages of the Freehand system is its ease of use, the reproducible generation of two distinct functional grasp patterns and an analog control scheme based on movements of the contralateral shoulder. However, after the Freehand system is not commercially available for more than ten years, alternative grasp neuroprosthesis with a comparable functionality are still missing. Therefore, the aim of this study was to develop a non-invasive neuroprosthesis and to show that a degree of functional restoration can be provided to end users comparable to implanted devices. By introduction of an easy to handle forearm electrode sleeve the reproducible generation of two grasp patterns has been achieved. Generated grasp forces of the palmar grasp are in the range of the implanted system. Though pinch force of the lateral grasp is significantly lower, it can effectively used by a tetraplegic subject to perform functional tasks. The non-invasive grasp neuroprosthesis developed in this work may serve as an easy to apply and inexpensive way to restore a missing hand and finger function at any time after spinal cord injury.

Computational Sensemaking on Examples of Knowledge Discovery from Neuroscience Data: Towards Enhancing Stroke Rehabilitation

Abstract: Strokes are often associated with persistent impairment of a lower limb. Functional brain mapping is a set of techniques from neuroscience for mapping biological quantities (computational maps) into spatial representations of the human brain as functional cortical tomography, generating massive data. Our goal is to understand cortical reorganization after a stroke and to develop models for optimizing rehabilitation with non-invasive electroencephalography. The challenge is to obtain insight into brain functioning, in order to develop predictive computational models to increase patient outcome. There are many EEG features that still need to be explored with respect to cortical reorganization. In the present work we use independent component analysis, and data visualization mapping as tools for sensemaking. Our results show activity patterns over the sensorimotor cortex, involved in the execution and association of movements; our results further supports the usefulness of inverse mapping methods and generative models for functional brain mapping in the context of non-invasive monitoring of brain activity.

The auditory p300-based SSBCI: A door to minimally conscious patients?

Abstract: In this study we report on the evaluation of a novel auditory single-switch BCI in nine patients diagnosed with MCS. The task included a simple and a complex oddball paradigm, the latter uses the tone stream segregation phenomenon. In all patients a significant difference between deviant and frequent tones could be observed in EEG. However, in some cases the deviant tones produce a significant negative peak and in some a very late positive peak. These preliminary findings are relevant in order to address future customization of this auditory ssBCI-based paradigm for unresponsive patients.

Kinect-based detection of self-paced hand movements: Enhancing functional brain mapping paradigms

Abstract: Monitoring and interpreting (sub)cortical reorganization after stroke may be useful for selecting therapies and improving rehabilitation outcome. To develop computational models that predict behavioral motor improvement from changing brain activation pattern, we are currently working on the implementation of a clinically feasible experimental set-up, which enables recording high quality electroencephalography (EEG) signals during inpatient rehabilitation of upper and lower limbs. The major drawback of current experimental paradigms is the cue-guided repetitive design and the lack of functional movements. In this paper, we assess the usability of the Kinect device (Microsoft Inc., Redmond, WA, USA) for tracking self-paced hand opening and closing movements. Three able-bodied volunteers performed self-paced right hand open-close movement sequences while EEG was recorded from sensorimotor areas and electromyography (EMG) from the right arm from extensor carpi radialis and flexor carpi radialis muscles. The results of the study suggest that the Kinect device allows generation of trigger information that is comparable to the information that can be obtained from EMG.

The interplay of prefrontal and sensorimotor cortices during inhibitory control of learned motor behavior.

Abstract: In the present study inhibitory cortical mechanisms have been investigated during execution and inhibition of learned motor programs by means of multi-channel functional near infrared spectroscopy (fNIRS). fNIRS is an emerging non-invasive optical technique for the in-vivo assessment of cerebral oxygenation, concretely changes of oxygenated [oxy-Hb] and deoxygenated [deoxy-Hb] hemoglobin. Eleven healthy subjects executed or inhibited previous learned finger and foot movements indicated by a visual cue. The execution of finger/foot movements caused a typical activation pattern namely an increase of [oxy-Hb] and a decrease of [deoxy-Hb] whereas the inhibition of finger/foot movements caused a decrease of [oxy-Hb] and an increase of [deoxy-Hb] in the hand or foot representation area (left or medial somatosensory and primary motor cortex). Additionally an increase of [oxy-Hb] and a decrease of [deoxy-Hb] in the medial area of the anterior prefrontal cortex (APFC) during the inhibition of finger/foot movements were found. The results showed, that inhibition/execution of learned motor programs depends on an interplay of focal increases and decreases of neural activity in prefrontal and sensorimotor areas regardless of the effector. As far as we know, this is the first study investigating inhibitory processes of finger/foot movements by means of multi-channel fNIRS.

Principles of Hybrid Brain–Computer Interfaces

Abstract: Brain–Computer Interface (BCI) research has developed in the last decade so that BCIs are ready to be used with users outside the research labs Although a wide range of assistive devices (ADs) exist, the additional usage of a BCI could improve the overall performance or applicability of such a combined system and is called hybrid BCI (hBCI) In this chapter the development of hBCIs starting from specific BCI combinations to very general hBCI based on EEG, biosignals and ADs is presented

New input modalities for modern game design and virtual embodiment

Abstract: Brain-computer interface (BCI) systems are not often used as input devices for modern games, due largely to their low bandwidth. However, BCIs can become a useful input modality when adapting the dynamics of the brain-game interaction, as well as combining them with devices based on other physiological signal to make BCIs more powerful and flexible. We introduce the Graz BCI Game Controller (GBGC) and describe how techniques such as context dependence, dwell timers and other intelligent software tools were implemented in a new system to control the Massive Multiplayer Online Role Playing Game World of Warcraft (WoW).

Prototype of an auto-calibrating, context-aware, hybrid brain-computer interface

Abstract: We present the prototype of a context-aware framework that allows users to control smart home devices and to access internet services via a Hybrid BCI system of an auto-calibrating sensorimotor rhythm (SMR) based BCI and another assistive device (Integra Mouse mouth joystick). While there is extensive literature that describes the merit of Hybrid BCIs, auto-calibrating and co-adaptive ERD BCI training paradigms, specialized BCI user interfaces, context-awareness and smart home control, there is up to now, no system that includes all these concepts in one integrated easy-to-use framework that can truly benefit individuals with severe functional disabilities by increasing independence and social inclusion. Here we integrate all these technologies in a prototype framework that does not require expert knowledge or excess time for calibration. In a first pilot-study, 3 healthy volunteers successfully operated the system using input signals from an ERD BCI and an Integra Mouse and reached average positive predictive values (PPV) of 72 and 98% respectively. Based on what we learned here we are planning to improve the system for a test with a larger number of healthy volunteers so we can soon bring the system to benefit individuals with severe functional disability.

Towards a framework based on single trial connectivity for enhancing knowledge discovery in BCI

Abstract: We developed a framework for systematic evaluation of BCI systems. This framework is intended to compare features extracted from a variety of spectral measures related to functional connectivity, effective connectivity, or instantaneous power. Different measures are treated in a consistent manner, allowing fair comparison within a repeated measures design. We applied the framework to BCI data from 14 subjects recorded on two days each, and demonstrated the framework's feasibility by confirming results from the literature. Furthermore, we could show that electrode selection becomes more focal in the second BCI session, but classification accuracy stays unchanged.

iScope: viewing biosignals on mobile devices

Abstract: We developed an iOS based application called iScope to monitor biosignals online. iScope is able to receive different signal types via a wireless network connection and is able to present them in the time or the frequency domain. Thus it is possible to inspect recorded data immediately during the recording process and detect potential artifacts early without the need to carry around heavy equipment like laptops or complete PC workstations. The iScope app has been tested during various measurements on the iPhone 3GS as well as on the iPad 1 and is fully functional.