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Über dieses Buch

Brain-computer interfaces (BCIs) are rapidly developing into a mainstream, worldwide research endeavor. With so many new groups and projects, it can be difficult to identify the best ones. This book summarizes ten leading projects from around the world. About 60 submissions were received in 2011 for the highly competitive BCI Research Award, and an international jury selected the top ten. This Brief gives a concise but carefully illustrated and fully up-to-date description of each of these projects, together with an introduction and concluding chapter by the editors.

Inhaltsverzeichnis

Frontmatter

State of the Art in BCI Research: BCI Award 2011

Without Abstract
Christoph Guger, Brendan Allison, Günter Edlinger

An Auditory Output Brain–Computer Interface for Speech Communication

Abstract
Understanding the neural mechanisms underlying speech production can aid the design and implementation of brain–computer interfaces for speech communication. Specifically, the act of speech production is unequivocally a motor behavior; speech arises from the precise activation of all of the muscles of the respiratory and vocal mechanisms. Speech also preferentially relies on auditory output to communicate information between conversation partners. However, self-perception of one’s own speech is also important for maintaining error-free speech and proper production of intended utterances. This chapter discusses our efforts to use motor cortical neural output during attempted speech production for control of a communication BCI device by an individual with locked-in syndrome while taking advantage of neural circuits used for learning and maintaining speech. The end result is a BCI capable of producing instantaneously vocalized output within a framework of motor-based brain-computer interfacing that provides appropriate auditory feedback to the user.
Jonathan S. Brumberg, Frank H. Guenther, Philip R. Kennedy

User-Appropriate and Robust Control Strategies to Enhance Brain−Computer Interface Performance and Usability

Abstract
This project aimed to enhance performance and usability of mental imagery-based BCIs by evaluating (1) user-appropriate and robust control strategies, (2) whether mental imagery-based BCIs are robust and stable enough for real-world applications and (3) user-comfort in able-bodied and disabled individuals. Three studies were conducted to address these issues. The results showed that alternatives to motor imagery can provide a great benefit especially to severely motor impaired users. Individually chosen control strategies from a broad range of reliable and stable mental tasks can improve BCI usability and performance substantially. Furthermore, participants could operate the BCI while simultaneously perceiving or reacting to deviant auditory stimuli and could attain stable long-time BCI control despite longer breaks without any BCI use. This project paid special attention to practical issues and helped to pave the way out of the laboratory into real-world application for mental imagery-based BCIs.
E. V. C. Friedrich, R. Scherer, C. Neuper

What’s Your Next Move? Detecting Movement Intention for Stroke Rehabilitation

Abstract
BCIs have recently been identified as a method to promote restorative neuroplastic changes in patients with severe motor impairment, such as after a stroke. In this chapter, we describe a novel therapeutic strategy for hand rehabilitation making use of this method. The approach consists of recording brain activity in cortical motor areas by means of near-infrared spectroscopy, and complementing the cortical signals with physiological data acquired simultaneously. By combining these signals, we aim at detecting the intention to move using a multi-modal classification algorithm. The classifier output then triggers assistance from a robotic device, in order to execute the movement and provide sensory stimulation at the level of the hand as response to the detected motor intention. Furthermore, the cortical data can be used to control audiovisual feedback, which provides a context and a motivating training environment. It is expected that closing the sensorimotor loop with such a brain-body-robot interface will promote neuroplasticity in sensorimotor networks and support the recovery process.
R. Zimmermann, L. Marchal-Crespo, O. Lambercy, M. -C. Fluet, J. -C. Metzger, J. Edelmann, J. Brand, K. Eng, R. Riener, M. Wolf, R. Gassert

A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs)

Abstract
The ability to operate a brain-computer interface (BCI) varies not only across subjects but also across time within each individual subject. In this article, we review recent progress in understanding the origins of such variations for BCIs based on the sensorimotor-rhythm (SMR). We propose a classification of studies according to four categories, and argue that an investigation of the neuro-physiological correlates of within-subject variations is likely to have a large impact on the design of future BCIs. We place a special emphasis on our own work on the neuro-physiological causes of performance variations, and argue that attentional networks in the gamma-range (\({>}40\) Hz) are likely to play a critical role in this context. We conclude the review with a discussion of outstanding problems.
Moritz Grosse-Wentrup, Bernhard Schölkopf

Exploring the Cortical Dynamics of Learning by Leveraging BCI Paradigms

Abstract
Brain-computer interfaces (BCIs)—systems that can record neural activity and translate them into commands for computer systems—are sufficiently advanced to allow users to volitionally guide them through simple tasks. Contemporary BCI research focuses on squeezing additional functionality out of standardized paradigms, be it achieving more bits per second, increased degrees of freedom, or increasing accuracy. While these studies have shown marginal advancements in recent years, our lack of understanding concerning the underlying neurophysiology continues to be the limiting factor in BCI development. In this chapter, we propose turning the way research is done on BCI systems on its head; instead of using our understanding of neural signals to incrementally advance the state of brain-machine interfaces, we apply a BCI system as a form of experimental control to study changes in neural activity. By using current BCI systems as a tool for neuroscientific study, we can probe the underlying neuroanatomy in novel, behaviorally controlled ways.
Tim Blakely, Kai Miller, Jeffrey Ojemann, Rajesh Rao

An Affective BCI Using Multiple ERP Components Associated to Facial Emotion Processing

Abstract
P300-based brain computer interfaces (BCIs) have successfully demonstrated that attention to an oddball stimulus can enhance the P300 component of the event-related potential (ERP) phase-locked to the event. However, it was unclear whether the more sophisticated face-evoked potentials can also be modulated by related mental tasks under the oddball paradigm. This study investigated ERP responses to image stimuli of objects, neutral faces, and emotional faces when subjects perform attention, face recognition and discrimination of emotional facial expressions respectively under the oddball paradigm. The results revealed the significant difference between target and non-target ERPs for each mental task. In addition, significant differences among the three mental tasks were observed for vertex-positive potential (VPP) over the fronto-central sites, late positive potential (LPP)/P3b over the centro-parietal sites and N250 over the occipito-temporal sites. These findings indicate that a novel affective BCI paradigm can be developed based on detection of multiple ERP components reflecting human face encoding and emotion processing. The high classification performance for single-trial emotional face-related ERPs demonstrated the effectiveness of the affective BCI.
Qibin Zhao, Yu Zhang, Akinari Onishi, Andrzej Cichocki

Seven Degree of Freedom Cortical Control of a Robotic Arm

Abstract
We have recently established simultaneous 7 degree-of-freedom (DoF) brain-computer interface (BCI) control of a robotic arm. Using signals recorded from single units of monkeys with implanted chronic microelectrode arrays, we can now demonstrate brain control of a prosthetic arm that exhibits the following features: (1) simultaneous 7-degree of freedom (DoF) brain control over 3-D robot hand translation, 3-D rotation, and finger aperture, (2) integrated kinematic (movement) and dynamic (force) control of a brain-controlled prosthetic robot through a novel impedance-based movement controller, (3) simplified methods for constructing cortical extraction models based only on observation of the moving robot, and (4) a generalized method for training subjects to use complex prosthetic robot devices using a novel form of operator-machine shared control.
Samuel T. Clanton, Angus J. C. McMorland, Zohny Zohny, S Morgan Jeffries, Robert G Rasmussen, Sharlene N Flesher, Meel Velliste

Utilizing High Gamma (HG) Band Power Changes as a Control Signal for Non-Invasive BCI

Abstract
Current electroencephalography (EEG) Brain-Computer Interface (BCI) methods typically use control signals (P300, modulated slow cortical potentials, mu or beta rhythm) that suffer from a slow time scale, low signal to noise ratio, and/or low spatial resolution. High gamma oscillations (70–150 Hz; HG) are rapidly evolving, spatially localized signals and recent studies have shown that EEG can reliably detect task-related HG power changes. In this chapter, we discuss how we capitalize on EEG resolved HG as a control signal for BCI. We use functional magnetic resonance imaging (fMRI) to impose spatial constraints in an effort to improve the signal to noise ratio across the HG band. The overall combination lends itself to a fast-responding, dynamic BCI.
M. Smith, K. Weaver, T. Grabowski, F. Darvas

Towards a Speech BCI Using ECoG

Abstract
Electrocorticography (ECoG) has emerged as a new signal platform for brain–computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from sensorimotor cortex. More recently, speech networks have emerged as a new neurophysiological substrate that could be used to both further improve on or complement existing motor-based control paradigms as well as expand BCI techniques to new clinical populations. We review the emerging literature associated with the scientific, clinical, and technical findings that provide the motivation and capability for speech-based BCIs.
Eric C. Leuthardt, John Cunningham, Dennis Barbour

Towards Communication in the Completely Locked-In State: Neuroelectric Semantic Conditioning BCI

Abstract
We introduced a Pavlovian semantic conditioning paradigm to enable basic communication in completely locked-in state (CLIS). Patients in CLIS have no means of communication and they have represented the target population for brain–computer interface (BCI) research in the last 15 years. Although different paradigms have been tested as well as different physiological signals have been used, to date no documented CLIS patient was able to control a BCI over an extended time period. We designed a novel paradigm based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) ‘yes’ and ‘no’ responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli and only affirmative statements were paired with electrical stimulation. A CLIS patient diagnosed with amyotrophic lateral sclerosis (ALS) participated in the study and underwent 37 daily sessions. The online classification accuracies of the slow cortical potentials, identified as the electroencephalographic (EEG) signature differentiating between covert ‘yes’ and ‘no’ responses, were around chance level on average, with peaks of high communication accuracy in some sessions.
Daniele De Massari, Carolin A. Ruf, Adrian Furdea, Sebastian Halder, Tamara Matuz, Niels Birbaumer

Backmatter

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