Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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An Introduction To The Event Related Potential Technique

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

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The Oxford Handbook Of Event Related Potential Components

Moving from a set of independent virtual worlds to an integrated network of 3D virtual worlds or Metaverse rests on progress in four areas: immersive realism, ubiquity of access and identity, interoperability, and scalability. For each area, the current status and needed developments in order to achieve a functional Metaverse are described. Factors that support the formation of a viable Metaverse, such as institutional and popular interest and ongoing improvements in hardware performance, and factors that constrain the achievement of this goal, including limits in computational methods and unrealized collaboration among virtual world stakeholders and developers, are also considered.

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3D Virtual worlds and the metaverse: Current status and future possibilities

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.

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

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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Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving.

In this chapter a specific aspect of Physiological Computing, that of implicit Human–Computer Interaction, is defined and discussed. Implicit Interaction aims at controlling a computer system by behavioural or psychophysiological aspects of user state, independently of any intentionally communicated command. This introduces a new type of Human–Computer Interaction, which in contrast to most forms of interaction implemented nowadays, does not require the user to explicitly communicate with the machine. Users can focus on understanding the current state of the system and developing strategies for optimally reaching the goal of the given interaction. For example, the system can assess the user state by means of passive Brain-Computer Interfaces, which the user needs not even be aware of. Based on this information and the given context the system can adapt automatically to the current strategies of the user. In a first study, a proof of principle is given, by implementing an Implicit Interaction to guide simple cursor movements in a 2D grid to a target. The results of this study clearly indicate the high potential of Implicit Interaction and introduce a new bandwidth of applications for passive Brain-Computer Interfaces.

Towards BCI-Based Implicit Control in Human–Computer Interaction

https://depositonce.tu-berlin.de/bitstream/11303/12350/2/Krol_etal_SEREEGA_2018.pdf

SEREEGA: Simulating event-related EEG activity

Objective 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. Approach We define cognitive and affective probing, referring to an 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 then discuss a number of aspects that differentiate various possible implementations of cognitive probing. Main results 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 model, refining the probes, and obtaining increasingly detailed or accurate information from the resulting brain activity. 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. Significance This tutorial provides guidelines to both implement, and critically discuss potential ethical implications of, novel cognitive probing applications and research endeavours.

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Laurens R. Krol

Papers

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

Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving.

Towards BCI-Based Implicit Control in Human–Computer Interaction

SEREEGA: Simulating event-related EEG activity

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