I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

An Introduction to MultiAgent Systems.

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

/pdf/brain-computer-interfaces-a-review-2z42gnpdfy.pdf

Brain Computer Interfaces, a Review

Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.

/pdf/the-steady-state-visual-evoked-potential-in-vision-research-123m7llkdr.pdf

The steady-state visual evoked potential in vision research: A review.

Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

/pdf/a-survey-of-stimulation-methods-used-in-ssvep-based-bcis-38omyw8mqs.pdf

A survey of stimulation methods used in SSVEP-based BCIs

Brain-Computer Interfaces (BCIs) enable people to control appliances without involving the normal output pathways of peripheral nervesand muscles. A particularly promising type of BCI is based on the Steady-State Visual Evoked Potential (SSVEP). Users can selectcommands by focusing their attention on repetitive visual stimuli(RVSi) that change one of their properties (e.g. color or pattern) with a certain frequency. These properties as well as the devicethe RVSi are rendered on, can greatly affect the performance, applicability, comfort and safety of the BCI. Despite this fact, stimulation properties have received fairly little attention in the BCI literature to this date. Furthermore, a heavy emphasis is placedon BCI performance to the detriment of other important factors suchas comfort and safety. The research reported in this document aimsat studying the effects of stimulation properties on performance aswell as comfort of SSVEP-based BCIs. Research was performed in bothoffline and online settings, using a custom made high-performance BCI. Comfort was measured using a custom questionnaire. A largevariability across subjects was found, but the results confirm that stimulation properties have a considerable impact on performance and comfort of SSVEP based BCIs. In general, a large difference between stimulation states is beneficial for BCI performance, but detrimen-tal to user comfort. A couple of configurations were found that provide a good compromise between comfort and performance.

Light Stimulation Properties to Influence Brain Activity: A Brain-CoMputer Interface application

The concept of “task” is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane’s performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial general intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A task theory would enable addressing tasks at the class level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.

Why Artificial Intelligence Needs a Task Theory

We report on a series of new platforms and events dealing with AI evaluation that may change the way in which AI systems are compared and their progress is measured. The introduction of a more diverse and challenging set of tasks in these platforms can feed AI research in the years to come, shaping the notion of success and the directions of the field. However, the playground of tasks and challenges presented there may misdirect the field without some meaningful structure and systematic guidelines for its organization and use. Anticipating this issue, we also report on several initiatives and workshops that are putting the focus on analyzing the similarity and dependencies between tasks, their difficulty, what capabilities they really measure and ultimately on elaborating new concepts and tools that can arrange tasks and benchmarks into a meaningful taxonomy.

/pdf/a-new-ai-evaluation-cosmos-ready-to-play-the-game-4b140dcgac.pdf

A new AI evaluation cosmos: Ready to play the game?

Brain-Computer Interfaces (BCIs) enable people to control appliances without involving the normal output pathways of peripheral nerves and muscles. A particularly promising type of BCI is based on the Steady-State Visual Evoked Potential (SSVEP). Users can select commands by focusing on visual stimuli that alternate appearance with a certain frequency. The properties of these stimuli, such as size and color, as well as the device they are rendered on, can significantly affect the performance, comfort and safety of the system. However, the choice of stimulation properties is often ad-hoc or copied. In this paper we report our findings about the effects of rendering device, refresh rate, environmental illumination, contrast, color, spatial frequency and size of visual stimuli. In order to investigate these effects online, a high-performance BCI was developed. User comfort was measured using a questionnaire. The results suggest that high contrast stimulation works the best, while also being the least comfortable. However, maximum black/white contrast is often not needed and other stimuli (e.g. blue/green stimulation) are shown to work almost as well, while being far more comfortable. Knowledge of these effects can help to improve SSVEP-based BCIs. I. INTRODUCTION The steady state visual evoked potential (SSVEP) refers to the response of the cerebral cortex to repetitive visual stimuli (RVSi) oscillating at a constant frequency. The SSVEP man- ifests as an oscillatory component in the electroencephalo- gram (EEG) having the same frequency (and/or harmonics) as the RVS (1). Because of their proximity to the visual cortex, the occipital sites exhibit a higher SSVEP response. The SSVEP is an effective electrophysiological source that can be used as input for brain-computer interfaces (BCIs). An SSVEP-based presents the subject with a set of RVSi that in general oscillate at different frequencies from each other. The SSVEP corresponding to the RVS on which the subject focuses their attention is more prominent and can be detected in the ongoing EEG. Each RVS is associated with an action which is executed by the BCI system when the corresponding SSVEP is detected. SSVEP-based BCIs offer two main advantages over BCIs based on other electrophysiological sources (e.g. P300, ERD/ERS): 1) they have higher information transfer rate, and 2) they require shorter calibration time. Unfortunately, the constant flicker can induce visual fatigue and even epileptic seizures in those that are susceptible. The functional model of a BCI system is depicted in Fig. 1. The visual stimulation plays a key role in the system and has

Jordi Bieger

Papers

A survey of stimulation methods used in SSVEP-based BCIs

Light Stimulation Properties to Influence Brain Activity: A Brain-CoMputer Interface application

Why Artificial Intelligence Needs a Task Theory

A new AI evaluation cosmos: Ready to play the game?

Effects of Stimulation Properties in Steady-State Visual Evoked Potential Based Brain-Computer Interfaces