BNCI Horizon 2020 - Towards a Roadmap for Brain/Neural Computer Interaction

In this paper, we present BNCI Horizon 2020, an EU Coordination and Support Action (CSA) that will provide a roadmap for brain-computer interaction research for the next years, starting in 2013, and aiming at research efforts until 2020 and beyond. The project is a successor of the earlier EU-funded Future BNCI CSA that started in 2010 and produced a roadmap for a shorter time period. We present how we, a consortium of the main European BCI research groups as well as companies and end user representatives, expect to tackle the problem of designing a roadmap for BCI research. In this paper, we define the field with its recent developments, in particular by considering publications and EU-funded research projects, and we discuss how we plan to involve research groups, companies, and user groups in our effort to pave the way for useful and fruitful EU-funded BCI research for the next ten years

Cognitive and affective probing: A tutorial and review of active learning for neuroadaptive technology

Contains fulltext : 216075.pdf (Publisher’s version ) (Closed access)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 be informative with respect to the human user's mental state at the time of presentation. An ability to decode neurophysiological responses to stimuli in real time becomes particularly powerful when combined with a simultaneous ability to autonomously produce such stimuli. This allows a computer to gather stimulus-response samples and iteratively produce new stimuli based on the information gathered from previous samples, thus acquiring more, and more specific, information. This information can even be obtained without the explicit, voluntary involvement of the user. Cognitive and affective probing refers to this application of active learning where repeated sampling is done by eliciting implicit brain responses. In this tutorial, we provide a definition of this method that unifies different past and current implementations based on common aspects. We argue that a key element is the user model, which serves as both information storage and basis for subsequent probes. Cognitive probing can be used to continuously and autonomously update this user model, refining the probes, and obtaining increasingly detailed or accurate information from the resulting brain activity. Based on the method as presented here, we discuss aspects that differentiate various technical implementations of cognitive probing. We furthermore note that, in contrast to a number of potential advantages of the method, cognitive probing may also pose a threat to informed consent, our privacy of thought, and our ability to assign responsibility to actions mediated by the system. As such, this tutorial provides guidelines to both implement, and critically discuss potential ethical implications of, novel cognitive probing applications and research endeavours.15 p

Erratum: Cognitive and affective probing: A tutorial and review of active learning for neuroadaptive technology (vol 17, 012001)

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Error-related EEG patterns during tactile human-machine interaction

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 potentials have only been observed in the visual and auditory modality. However, new interfaces making use of the tactile sensory modality for conveying information to the user are on the rise. The present study aims at investigating the feasibility of BCI error detection during tactile human-machine interaction. Therefore, an experiment was conducted where EEG was measured while participants interacted with a tactile interface. During this interaction, errors of the user as well as of the interface were induced. It was shown that EEG patterns after erroneous behavior - either of the user or of the interface - significantly differed from patterns after correct responses. ©2009 IEEE

What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI

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 times or spontaneous dwellings of the user as command. Lately it has been shown that brain-computer interface (BCI) input bears good prospects to overcome this problem using imagined hand movements to elicit a selection. The current approach tries to develop this idea further by exploring potential signals for the use in a passive BCI, which would have the advantage that the brain signals used as input are generated automatically without conscious effort of the user. To explore event-related potentials (ERPs) giving information about the user’s intention to select an object, 32-channel electroencephalography (EEG) was recorded from ten participants interacting with a dwell-time-based system. Comparing ERP signals during the dwell time with those occurring during fixations on a neutral cross hair, a sustained negative slow cortical potential at central electrode sites was revealed. This negativity might be a contingent negative variation (CNV) reflecting the participants’ anticipation of the upcoming selection. Offline classification suggests that the CNV is detectable in single trial (mean accuracy 74.9 %). In future, research on the CNV should be accomplished to ensure its stable occurence in human-computer interaction and render possible its use as a potential substitue for the click operation

Evaluating User Experience in a Selection Based Brain-Computer Interface Game: A Comparative Study

In human-computer interaction, it is important to offer the users correct modalities for particular tasks and situations. Unless the user has the suitable modality for a task, neither task performance nor user experience can be optimised. The aim of this study is to assess the appropriateness of using a steady-state visually evoked potential based brain-computer interface (BCI) for selection tasks in a computer game. In an experiment participants evaluated a BCI control and a comparable automatic speech recogniser (ASR) control in terms of workload, usability and engagement. The results showed that although BCI was a satisfactory modality in completing selection tasks, its use in our game was not engaging for the player. In our particular setup, ASR control appeared to be a better alternative to BCI control