The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface (BCI) for control of a grasp neuroprostheses in individuals with high spinal cord injury (SCI). The first results of the ongoing MoreGrasp clinical feasibility study involving end users with SCI are presented. This includes BCI screening sessions, in which we investigate the electroencephalography (EEG) patterns associated with single, natural movements of the upper limb. These patterns will later be used to control the neuroprosthesis. Additionally, the MoreGrasp grasp neuroprosthesis consisting of electrode arrays embedded in an individualized textile forearm sleeve is presented. The general feasibility of this electrode array in terms of corrections of misalignments during donning is shown together with the functional results in end users of the electrode forearm sleeve.

Spinal cord injury (SCI) can disrupt the communication pathways between the brain and the rest of the body, restricting the ability to perform volitional movements. Neuroprostheses or robotic arms can enable individuals with SCI to move independently, improving their quality of life. The control of restorative or assistive devices is facilitated by brain-computer interfaces (BCIs), which convert brain activity into control commands. In this paper, we summarize the recent findings of our research towards the main aim to provide reliable and intuitive control. We propose a framework that encompasses the detection of goal-directed movement intention, movement classification and decoding, error-related potentials detection and delivery of kinesthetic feedback. Finally, we discuss future directions that could be promising to translate the proposed framework to individuals with SCI.

Restoring the ability to reach and grasp can dramatically improve quality of life for people with cervical spinal cord injury (SCI). The main challenge in restoring independent reaching and grasping in patients is to develop assistive technologies with intuitive and non-invasive user interfaces. We believe that this challenge can be met by directly translating movement-related brain activity into control signals. During the last decade, we have conducted research on EEG-based brain-computer interfaces (BCIs) for the decoding of movement parameters, such as trajectories and targets. Although our findings are promising, the control is still unnatural. Therefore, we surmise that natural and intuitive control of neuroprostheses could be achieved by developing a novel control framework that incorporates detection of goal directed movement intention, movement decoding, identifying the type of grasp, error potentials detection and delivery of feedback.

The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition device, the P300-based BCI, and the music composing software. Seventeen musical participants and one professional composer performed a copy-spelling, a copy-composing, and a free-composing task with the system. According to the USD guidelines, we investigated the efficiency, the effectiveness and subjective criteria in terms of satisfaction, enjoyment, frustration, and attractiveness. The musical participants group achieved high average accuracies: 88.24% (copy-spelling), 88.58% (copy-composing), and 76.51% (free-composing). The professional composer achieved also high accuracies: 100% (copy-spelling), 93.62% (copy-composing), and 98.20% (free-composing). General results regarding the subjective criteria evaluation were that the participants enjoyed the usage of the Brain Composing system and were highly satisfied with the system. Showing very positive results with healthy people in this study, this was the first step towards a music composing system for severely disabled people.

One of the main goals of modern braincomputer interfaces (BCIs) is that they should be simple and intuitive to use. Long-lasting training and learning periods are demotivating for the intended user. Therefore, the training should be reduced to a minimum. This particularly applies to P300-based BCIs, which are known as highly accurate and robust. In this paper, we evaluated an approach that uses a generic classifier for P300 spelling instead of the usual personalized classifier, which users have to train before they can use the P300-based BCI. The generic classifier was calculated using the training data of 18 persons and evaluated with the data of 7 persons. Results were compared to the results achieved with personalized classifiers. We found that the generic classifier achieved comparable results regarding the effectiveness and efficiency. Therefore, our approach seems to be an appropriate, zero training alternative to personalized classifiers.

One important aspect in non-invasive brain-computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective, it means with maximum comfort and without any extra inconveniences (e.g., washing the hair), whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently. In this work, we evaluated three different commercially available EEG acquisition systems that differ in the type of electrodes (gel-, water-, and dry-based), the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control), and user satisfaction (comfort). We found that water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode-based system caused the least inconveniences. Therefore, building a reliable BCI is possible with all the evaluated systems, and it is on the user to decide which system meets the given requirements best.

In the past years many NeuroIS studies have been published using different neuroimaging tools like electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). In general most of the EEG studies have been performed in the lab, where participants are mounted with EEG sensors sitting in front of the computer and following the presented instructions. There are several mobile EEG systems on the market which could be used to investigate brain activity of human behaviour in the field, like during sports or social activities. In this paper we will present a novel system for EEG-based NeuroIS studies out of the lab, named mobile NeuroIS. The system consists of a wireless EEG system and a smart-phone, serving as a monitor. Beside the system architecture we will present first evaluation data of three participants using it as mobile Brain-Computer Interface (BCI) application.

Brain-Computer Interfaces (BCI) are systems that can be controlled by the user through harnessing their brain signals. Extensive research has been undertaken within a laboratory setting with healthy users to illustrate the usability of such systems. To bring these systems to users with severe disabilities it is necessary to develop simple, easy to use systems that can be operated b y non-experts outside of the lab and are evaluated by real end users preferably through a user centered design approach. This paper presents a user centered evaluation of a P300 BCI operated by non-expert users in a rehabilitation center with a control group of five healthy participants without acquired brain injury (ABI) and five end users with ABI. Each participant aimed to complete the 30-step protocol three separate times and rate his or her satisfaction from 0 to 10 on the Visual Analogue Scale after each session. Participants then rated their satisfaction with the BCI on the extended QUEST 2.0 and a customized usability questionnaire. The results indicated that end-users were able to achieve an average accuracy of 55% compared to the control group that reported an average of 78%. The findings indicated that participants were satisfied with the BCI but felt frustrated when it did not respond to their commands. This work was phase one of three to move the BCI system into end users homes. Key recommendations for advancing the P300 BCI towards an easy to use, home-based system were identified, including reducing the complexity of the setup, ensuring the system becomes more responsive and increasing the overall functionality.

Providing brain-computer interface (BCI) users engaging applications should be one of the main targets in BCI research. A painting application, a web browser and other applications can already be controlled via BCI. Another engaging application would be a music composer for self-expression. In this work, we describe Brain Composing: A BCI controlled music composing software. We tested and evaluated the implemented brain composing system with five volunteers. Using a tap water-based electrode biosignal amplifier further improved the usability of the system. Three participants reached accuracies above 77% and were able to copy-compose a given melody. Results of questionnaires support that our brain composing system is an attractive and easy way to compose music via a BCI.

Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) were proven to be a reliable synchronous communication method. For everyday life situations, however, this synchronous mode is impractical because the system will deliver a selection even if the user is not paying attention to the stimulation. So far, research into attention-aware visual ERP-BCIs (i.e., asynchronous ERP-BCIs) has led to variable success. In this study, we investigate new approaches for detection of user engagement. Approach. Classifier output and frequency-domain features of electroencephalogram signals as well as the hybridization of them were used to detect the user' s state. We tested their capabilities for state detection in different control scenarios on offline data from 21 healthy volunteers. Main results. The hybridization of classifier output and frequency-domain features outperformed the results of the single methods, and allowed building an asynchronous P300-based BCI with an average correct state detection accuracy of more than 95%. Significance. Our results show that all introduced approaches for state detection in an asynchronous P300-based BCI can effectively avoid involuntary selections, and that the hybrid method is the most effective approach.

We tested seven different EEG electrode systems according user centered, operator centered and technical aspects. In the initial testing phase we focused on technical aspects and more simple experimental tasks. The results of these first tests were used to select the best three systems in an advanced testing phase. In this second testing phase a P300 based BCI was used to navigate through a multimedia player, selecting music and video clips. The results showed that each of the systems has its advantages and disadvantages which should be considered when planning future NeuroIS studies using EEG.

The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users' home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.

Objectives: Access to the world wide web and multimedia content is an important aspect of life. We present a web browser and a multimedia user interface adapted for control with a brain-computer interface (BCI) which can be used by severely motor impaired persons.Methods: The web browser dynamically determines the most efficient P300 BCI matrix size to select the links on the current website. This enables control of the web browser with fewer commands and smaller matrices. The multimedia player was based on an existing software. Both applications were evaluated with a sample of ten healthy participants and three end-users. All participants used a visual P300 BCI with face-stimuli for control.Results: The healthy participants completed the multimedia player task with 90% accuracy and the web browsing task with 85% accuracy. The end-users completed the tasks with 62% and 58% accuracy. All healthy participants and two out of three end-users reported that they felt to be in control of the system.Conclusions: In this study we presented a multimedia application and an efficient web browser implemented for control with a BCI.Significance: Both applications provide access to important areas of modern information retrieval and entertainment. (C) 2014 Elsevier B.V. All rights reserved.

Brain-computer interface (BCI) users can control very complex applications such as multimedia players or even web browsers. Therefore, different biosignal acquisition systems are available to noninvasively measure the electrical activity of the brain, the electroencephalogram (EEG). To make BCIs more practical, hardware and software are nowadays designed more user centered and user friendly. In this paper we evaluated one of the latest innovations in the area of BCI: A wireless EEG amplifier with dry electrode technology combined with a web browser which enables BCI users to use standard webmail. With this system ten volunteers performed a daily life task: Write, read and answer an email. Experimental results of this study demonstrate the power of the introduced BCI system.

We developed a new P300-based BCI communication system. The design is tripartite: One part operates as a universal data acquisition unit, which allows to easily use different data acquisition devices. The second part is a rapid prototyping platform based on Matlab/Simulink (R) for data processing, which can be modified in an easy way. The last part is a graphical user interface, which also acts as main controller. Every single part is state-of-the-art designed and implemented. Connected together they are a very powerful tool not only for scientists and research issues, but also for non-expert users.