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

Neuroadaptive modelling for generating images matching perceptual categories

Brain-computer interfaces enable active communication and execution of a pre-defined set of commands, such as typing a letter or moving a cursor. However, they have thus far not been able to infer more complex intentions or adapt more complex output based on brain signals. Here, we present neuroadaptive generative modelling, which uses a participant's brain signals as feedback to adapt a boundless generative model and generate new information matching the participant's intentions. We report an experiment validating the paradigm in generating images of human faces. In the experiment, participants were asked to specifically focus on perceptual categories, such as old or young people, while being presented with computer-generated, photorealistic faces with varying visual features. Their EEG signals associated with the images were then used as a feedback signal to update a model of the user's intentions, from which new images were generated using a generative adversarial network. A double-blind follow-up with the participant evaluating the output shows that neuroadaptive modelling can be utilised to produce images matching the perceptual category features. The approach demonstrates brain-based creative augmentation between computers and humans for producing new information matching the human operator's perceptual categories.Peer reviewe

Neuroadaptive Technology and the Self: a Postphenomenological Perspective

Neuroadaptive technology (NAT) is a closed-loop neurotechnology designed to enhance human–computer interaction. NAT works by collecting neurophysiological data, which are analysed via autonomous algorithms to create actions and adaptations at the user interface. This paper concerns how interaction with NAT can mediate self-related processing (SRP), such as self-awareness, self-knowledge, and agency. We begin with a postphenomenological analysis of the NAT closed loop to highlight the built-in selectivities of machine hermeneutics, i.e., autonomous chains of algorithms that convert data into an assessment of psychological states/intentions. We argue that these algorithms produce an assessment of lived experience that is quantitative, reductive, and highly simplistic. This reductive assessment of lived experience is presented to the user via feedback at the NAT interface and subsequently mediates SRP. It is argued that congruence between system feedback and SRP determines the precise character of the alterity relation between human user and system. If feedback confirms SRP, the technology is regarded as a quasi-self. If there is a disagreement between SRP and feedback from the system, NAT is perceived to be a quasi-other. We argue that the design of the user interface shapes the precise ways in which NAT can mediate SRP

THE DEVELOPMENT OF A MULTIMODAL NEUROADAPTIVE GAMING TECHNOLOGY TO DISTRACT FROM PAINFUL EXPERIENCES.

Painful experiences can be mitigated by distraction techniques such as video game distraction, due to limited available attentional resources. There are many benefits to using video games as a non-pharmacological intervention, including their cost-effectiveness and absence of side effects or withdrawal symptoms. However, video games cannot provide a distraction which is sufficient for pain management if they are not engaging. This work aims to discuss how and why video games capture attention and explore how modulating game factors can affect the response to pain. The aim of this work in its entirety is to develop a neuroadaptive game which is tailored to reorient attention away from a painful experience, and towards the distraction technique. The neuroadaptive element of this technology will enable a balance of challenge and skill which make a unique and playable game for each participant. The development of the neuroadaptive game was supported by two studies. Study One focused on the determination of optimal game difficulty level for pain distraction, and Study Two furthered this research, alongside determining optimal neurological sites for the monitoring of attention and attentional reorientation. Study 3 explored the use of a neuroadaptive gaming technology to distract from pain – a bespoke, real-time data processing pipeline was developed for this purpose. The limitations of the neuroadaptive game are discussed in detail with considerations for future work and development. The results of the three studies carried out during the course of this work indicate that real-time pre-processing and classification of fNIRS data to a good standard is possible. The studies also revealed that the montage for data collection and features used for data collection are crucial considerations for classification accuracy. This thesis also has implications for further work into neuroadaptive technologies and how these systems can be tested and verified. Statistical significance between a non-neuroadaptive game and a neuroadaptive game was not found throughout the course of this work, although the potential explanations and future considerations are discussed in detail. Overall, we were able to confirm that pain tolerance can be improved with the use of a distraction task, but that the balance of task difficulty and skill level is delicate and requires further exploration

Feedback information and the reward positivity

The reward positivity is a component of the event-related brain potential (ERP) sensitive to neural mechanisms of reward processing. Multiple studies have demonstrated that reward positivity amplitude indices a reward prediction error signal that is fundamental to theories of reinforcement learning. However, whether this ERP component is also sensitive to richer forms of performance information important for supervised learning is less clear. To investigate this question, we recorded the electroencephalogram from participants engaged in a time estimation task in which the type of error information conveyed by feedback stimuli was systematically varied across conditions. Consistent with our predictions, we found that reward positivity amplitude decreased in relation to increasing information content of the feedback, and that reward positivity amplitude was unrelated to trial-to-trial behavioral adjustments in task performance. By contrast, a series of exploratory analyses revealed frontal-central and posterior ERP components immediately following the reward positivity that related to these processes. Taken in the context of the wider literature, these results suggest that the reward positivity is produced by a neural mechanism that motivates task performance, whereas the later ERP components apply the feedback information according to principles of supervised learning

Cortical Topography of Error-Related High-Frequency Potentials During Erroneous Control in a Continuous Control Brain–Computer Interface

Brain–computer interfaces (BCIs) benefit greatly from performance feedback, but current systems lack automatic, task-independent feedback. Cortical responses elicited from user error have the potential to serve as state-based feedback to BCI decoders. To gain a better understanding of local error potentials, we investigate responsive cortical power underlying error-related potentials (ErrPs) from the human cortex during a one-dimensional center-out BCI task, tracking the topography of high-gamma (70–100 Hz) band power (HBP) specific to BCI error. We measured electrocorticography (ECoG) in three human subjects during dynamic, continuous control over BCI cursor velocity. Subjects used motor imagery and rest to move the cursor toward and subsequently dwell within a target region. We then identified and labeled epochs where the BCI decoder incorrectly moved the cursor in the direction opposite of the subject’s expectations (i.e., BCI error). We found increased HBP in various cortical areas 100–500 ms following BCI error with respect to epochs of correct, intended control. Significant responses were noted in primary somatosensory, motor, premotor, and parietal areas and generally regardless of whether the subject was using motor imagery or rest to move the cursor toward the target. Parts of somatosensory, temporal, and parietal areas exclusively had increased HBP when subjects were using motor imagery. In contrast, only part of the parietal cortex near the angular gyrus exclusively had an increase in HBP during rest. This investigation is, to our knowledge, the first to explore cortical fields changes in the context of continuous control in ECoG BCI. We present topographical changes in HBP characteristic specific to the generation of error. By focusing on continuous control, instead of on discrete control for simple selection, we investigate a more naturalistic setting and provide high ecological validity for characterizing error potentials. Such potentials could be considered as design elements for co-adaptive BCIs in the future as task-independent feedback to the decoder, allowing for more robust and individualized BCIs

MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Neurophysiological studies are typically conducted in laboratories with limited ecological validity, scalability, and generalizability of findings. This is a significant challenge for the development of brain-computer interfaces (BCIs), which ultimately need to function in unsupervised settings on consumer-grade hardware. We introduce MYND: A framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies. Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires. As a use case, we evaluate two BCI control strategies ("Positive memories" and "Music imagery") in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG). Thirty subjects recorded 70.4 hours of EEG data with the system at home. The median headset fitting time was 25.9 seconds, and a median signal quality of 90.2% was retained during recordings.Neural activity in both control strategies could be decoded with an average offline accuracy of 68.5% and 64.0% across all days. The repeated unsupervised execution of the same strategy affected performance, which could be tackled by implementing feedback to let subjects switch between strategies or devise new strategies with the platform.Comment: 9 pages, 5 figures. Submitted to PNAS. Minor revisio

BCI to Potentially Enhance Streaming Images to a VR Headset by Predicting Head Rotation

While numerous studies show that brain signals contain information about an individual’s current state that are potentially valuable for smoothing man–machine interfaces, this has not yet lead to the use of brain computer interfaces (BCI) in daily life. One of the main challenges is the common requirement of personal data that is correctly labeled concerning the state of interest in order to train a model, where this trained model is not guaranteed to generalize across time and context. Another challenge is the requirement to wear electrodes on the head. We here propose a BCI that can tackle these issues and may be a promising case for BCI research and application in everyday life. The BCI uses EEG signals to predict head rotation in order to improve images presented in a virtual reality (VR) headset. When presenting a 360° video to a headset, field-of-view approaches only stream the content that is in the current field of view and leave out the rest. When the user rotates the head, other content parts need to be made available soon enough to go unnoticed by the user, which is problematic given the available bandwidth. By predicting head rotation, the content parts adjacent to the currently viewed part could be retrieved in time for display when the rotation actually takes place. We here studied whether head rotations can be predicted on the basis of EEG sensor data and if so, whether application of such predictions could be applied to improve display of streaming images. Eleven participants generated left- and rightward head rotations while head movements were recorded using the headsets motion sensing system and EEG. We trained neural network models to distinguish EEG epochs preceding rightward, leftward, and no rotation. Applying these models to streaming EEG data that was withheld from the training showed that 400 ms before rotation onset, the probability “no rotation” started to decrease and the probabilities of an upcoming right- or leftward rotation started to diverge in the correct direction. In the proposed BCI scenario, users already wear a device on their head allowing for integrated EEG sensors. Moreover, it is possible to acquire accurately labeled training data on the fly, and continuously monitor and improve the model’s performance. The BCI can be harnessed if it will improve imagery and therewith enhance immersive experience