Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity

The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator'smindset. Neuroadaptive technology significantlywidens the communication bottleneck and has the potential to fundamentally change the way we interact with technology

Affective aspects of perceived loss of control and potential implications for brain-computer interfaces

© 2017 Grissmann, Zander, Faller, Brönstrup, Kelava, Gramann and Gerjets. 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 introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios

Evaluation of a dry EEG system for application of passive brain-computer interfaces in autonomous driving

© 2017 Zander, Andreessen, Berg, Bleuel, Pawlitzki, Zawallich, Krol and Gramann. 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 considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. Fromthese results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset’s usability and wearing comfort

Automated task load detection with electroencephalography: towards passive brain–computer interfacing in robotic surgery

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 a complex maneuver and should not be disturbed. Passive brain–computer interfaces (BCI) infer changes in cognitive and affective state by monitoring and interpreting ongoing brain activity recorded via an electroencephalogram. The resulting information can then be used to automatically adapt a technological system to the human user. So far, passive BCI have mostly been investigated in laboratory settings, even though they are intended to be applied in real-world settings. In this study, a passive BCI was used to assess changes in task load of skilled surgeons performing both simple and complex surgical training tasks. Results indicate that the introduced methodology can reliably and continuously detect changes in task load in this realistic environment

Towards a conceptual framework for cognitive probing

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 neuroadaptive technology. We propose a working definition of cognitive probing that allows it to be generalised across different applications and disciplines

A task-independent workload classifier for neuroadaptive technology: Preliminary data

© 2016 IEEE. Passive brain-computer interfacing allows computer systems direct access to aspects of their user's cognition. In essence, a computer system can gain information about its user without this user needing to explicitly communicate it. Based on this information, human-computer interaction can be made more symmetrical, solving an age-old but still fundamental problem of present-day interaction techniques. For practical real-world application of this technology, it is important that cognitive states can be identified accurately and efficiently. Here we present preliminary data demonstrating it is possible to calibrate a task-independent classifier to identify when a user is under heavy workload across different activities. We used different types of mental arithmetic and even a semantic task. Task-independent classification is an important step towards real-world practical application of this technology