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's mindset. Neuroadaptive technology significantly widens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.human-computer interaction | passive brain-computer interfaces | electroencephalogram | predictive coding | neuroadaptive technology I n the European Union, 96% of enterprises rely on computers for their productivity (1). Advances in human-computer interaction (HCI), concerning the effective, efficient, and satisfying use of computer systems, may thus carry great societal benefits, e.g., in terms of productivity. However, although interaction techniques have become increasingly user-friendly-e.g., from punch cards to touch screens-they still depend on the user (operator) to translate their original thought or intention into a sequence of small, explicit commands (2). This translational step, where the human operator must ultimately obey the machine's logic, presents both a communication bottleneck and a source of potential error (3). At the same time, the computer has practically no limitation to the amount of information it can communicate, and is not as adaptable as its user. In these aspects, present-day HCI is asymmetrical (4). Comparing this to human-human interaction, Fischer (5) emphasizes the importance of a shared understanding of the situation and an understanding of the communication partner. In this sense, for a computer system to "understand" its user, it needs a model of that user-a source of informa...

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. From these 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.

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SEREEGA is a general-purpose toolbox to simulate ground-truth EEG data.

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