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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Brain–computer interfaces (BCI) are devices which measure brain activity and translate it into messages or commands, thereby opening up many possibilities for investigation and application. This book provides keys for understanding and designing these multi-disciplinary interfaces, which require many fields of expertise such as neuroscience, statistics, informatics and psychology.
This second volume, Technology and Applications, is focused on the field of BCI from the perspective of its end users, such as those with disabilities to practitioners. Covering clinical applications and the field of video games, the book then goes on to explore user needs which drive the design and development of BCI. The software used for their design, primarily OpenViBE, is explained step by step, before a discussion on the use of BCI from ethical, philosophical and social perspectives.
The basic notions developed in this reference book are intended to be accessible to all readers interested in BCI, whatever their background. More advanced material is also offered, for readers who want to expand their knowledge in disciplinary fields underlying BCI.
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Table of Contents
Cover
Title
Copyright
Foreword
Introduction
I.1. History
I.2. Introduction to BCIs
I.3. Book presentation
I.4. Acknowledgments
I.5. Bibliography
Part 1: Fields of Application
1 Brain–Computer Interfaces in Disorders of Consciousness
1.1. Introduction
1.2. Altered states of consciousness: etiologies and clinical features
1.3. Functional assessment of patients with altered states of consciousness (passive paradigms)
1.4. Advanced approaches to assessing consciousness (active paradigms)
1.5. Toward the real-time use of functional markers
1.6. Conclusion and future outlook
1.7. Bibliography
2 Medical Applications: Neuroprostheses and Neurorehabilitation
2.1. Motor deficiencies
2.2. Compensating for motor deficiency
2.3. Conclusions
2.4. Bibliography
3 Medical Applications of BCIs for Patient Communication
3.1. Introduction
3.2. Reactive interfaces for communication
3.3. Active interfaces for communication
3.4. Conclusions
3.5. Bibliography
4 BrainTV: Revealing the Neural Bases of Human Cognition in Real Time
4.1. Introduction and motivation
4.2. Toward first person data accounting
4.3. Bringing subjective and objective data into the same space: conscious experience of the subject
4.4. Technical aspects: the contribution of brain–computer interfaces
4.5. The BrainTV system and its applications
4.6. BrainTV limitations
4.7. Extension to other types of recordings
4.8. Conclusions
4.9. Bibliography
5 BCIs and Video Games: State of the Art with the OpenViBE2 Project
5.1. Introduction
5.2. Video game prototypes controlled by BCI
5.3. Industrial prototypes: the potential for very different kinds of games
5.4. Discussion
5.5. Conclusion
5.6. Bibliography
Part 2: Practical Aspects of BCI Implementation
6 Analysis of Patient Need for Brain–Computer Interfaces
6.1. Introduction
6.2. Types of users
6.3. Interpretation of needs in BCI usage contexts
6.4. Conclusions
6.5. Bibliography
7 Sensors: Theory and Innovation
7.1. EEG electrodes
7.2. Invasive recording
7.3. Latest generation sensors
7.4. Magnetoencephalography
7.5. Conclusions
7.6. Bibliography
8 Technical Requirements for High-quality EEG Acquisition
8.1. Electrodes
8.2. Montages
8.3. Amplifiers
8.4. Analog filters
8.5. Analog-to-digital conversion
8.6. Event synchronization with the EEG
8.7. Conclusions
8.8. Bibliography
9 Practical Guide to Performing an EEG Experiment
9.1. Study planning
9.2. Equipment
9.3. Experiment procedure
9.4. Bibliography
Part 3: Step by Step Guide to BCI Design with OpenViBE
10 OpenViBE and Other BCI Software Platforms
10.1. Introduction
10.2. Using BCI for control
10.3. BCI processing stages
10.4. Exploring BCI
10.5. Comparison of platforms
10.6. Choosing a platform
10.7. Conclusion
10.8. Bibliography
11 Illustration of Electrophysiological Phenomena with OpenViBE
11.1. Visualization of raw EEG signals and artifacts
11.2. Visualization of alpha oscillations
11.3. Visualization of the beta rebound
11.4. Visualization of the SSVEP
11.5. Conclusions
11.6. Bibliography
12 Classification of Brain Signals with OpenViBE
12.1. Introduction
12.2. Classification
12.3. Evaluation
12.4. Conclusions
12.5. Bibliography
13 OpenViBE Illustration of a P300 Virtual Keyboard
13.1. Target/non-target classification
13.2. Illustration of a P300 virtual keyboard
13.3. Bibliography
14 Recreational Applications of OpenViBE: Brain Invaders and Use-the-Force
14.1. Brain Invaders
14.2. Implementation
14.3. Use-The-Force!
14.4. Conclusions
14.5. Bibliography
Part 4: Societal Challenges and Perspectives
15 Ethical Reflections on Brain–Computer Interfaces
15.1. Introduction
15.2. The animal
15.3. Human beings
15.4. The human species
15.5. Conclusions
15.6. Bibliography
16 Acceptance of Brain–machine Hybrids: How is Their Brain Perceived In Vivo?
16.1. The ethical problem
16.2. The method
16.3. Ethics of experimentation: Matthew Nagle, the first patient
16.4. Body language in performance
16.5. Ethics of autonomous (re)socialization
16.6. Conclusions
16.7. Bibliography
16.8. Appendix (verbatim video retranscriptions)
7
17 Conclusion and Perspectives
17.1. Introduction
17.2. Reinforcing the scientific basis of BCIs
17.3. Using BCI in practice
17.4. Opening up BCI technologies to new applications and fields
17.5. Concern about ethical issues
17.6. Conclusions
17.7. Bibliography
List of Authors
Index
Contents of Volume 1
End User License Agreement
Guide
Cover
Table of Contents
Begin Reading
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Series Editor
Maureen Clerc
Brain–Computer Interfaces 2
Technology and Applications
Edited by
Maureen Clerc
Laurent Bougrain
Fabien Lotte
First published 2016 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2016
The rights of Maureen Clerc, Laurent Bougrain and Fabien Lotte to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2016945318
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-84821-963-2
Foreword
A Brain–Computer Interface (BCI) records brain signals, translates them into commands that operate a variety of devices, and provides feedback to the user about how intentions are transformed into actions. These three essential components, forming a closed-loop system, define the core components of a BCI. Their natural target population has traditionally been people with motor disabilities that have lost control of their body but have preserved cognitive functions, and BCIs have been intended to act as alternative assistive devices for them. However, in recent years the scope of a BCI has widened to include restoration or rehabilitation of motor and even cognitive functions for patients after some kind of central nervous system injury, brain state monitoring for healthy subjects, and new tools for studying human brain functions.
An anecdotal, even fringe, field of research at the confines with science fiction when it appeared, BCIs have grown over the last 40 years from early prototypes in a handful of locations to more than 3,000 research labs and nearly 150 companies working in BCI-related areas nowadays. The complexity of today’s BCI systems, which are moving beyond constrained laboratory conditions, calls for truly multidisciplinary efforts spanning clinical research to computer science and human–computer interfaces, from neuroscience to biomedical and neuroengineering, from rehabilitation to robotics and virtual reality, and from human psychology to material and electrical engineering.
This wide range of fields that contribute to BCI makes it difficult, if not impossible, to have a unified view covering all the facets of this fascinating scientific and translational enterprise. Thus, a certain bias is always present and openly acknowledged in our research. This book is no exception. It is edited by signal processing and machine learning specialists. Yet, aiming to become a reference for the French-speaking research community, it gathers a collective body of expertise from all the fields involved in BCI research and practice. We consider this challenge that the editors have successfully tackled, as the book covers state-of-the-art research and results in a way that all other communities can relate to. Furthermore, the curious layperson – I hope you are if you want to live long with a healthy brain! – can also profit from a significant number of chapters that do not require any specific background.
The book is organized into seven parts, distributed between two volumes.
Following on from the first volume (Foundations and Methods), this second volume (Technology and Applications) clearly exposes the field of BCI from the standpoint of end users (mainly people with physical disabilities) and practitioners. Part 1 presents a large variety of domains, clinical and non-clinical. Academic and clinical researchers, but also BCI enthusiasts, will find Part 2 a precious resource for setting up a BCI platform, both at the hardware and at the software level. Special emphasis is placed on the OpenViBE software platform in Part 3. Finally, Part 4 concludes the second volume of this reference book by addressing key societal issues regarding ethics and acceptability, which should concern any informed citizen – from the researcher to the experimental subject, from the clinician to the end user and from the philosopher to the policy maker.
Brain–Computer Interfaces by Clerc, Bougrain and Lotte is the first BCI book for and by the French-speaking community. Here, it is also translated in English as it has important lessons for all BCI researchers and practitioners worldwide. I am certain that this book will appeal to each of them as it has done to me. Enjoy it.
José DEL R. MILLÁNGenevaSwitzerlandMay 2016
Introduction
A Brain–Computer Interface (BCI) is a system that translates a user’s brain activity into messages or commands for an interactive application. BCIs represent a relatively recent technology that is experiencing a rapid growth. The objective of this introductory chapter is to briefly present an overview of the history of BCIs, the technology behind them, the terms and classifications used to describe them and their possible applications. The book’s content is presented, and a reading guide is provided so that you, the reader, can easily find and use whatever you are searching for in this book.
I.1. History
The idea of being able to control a device through mere thought is not new. In the scientific world, this idea was proposed by Jacques Vidal in 1973 in an article entitled “Toward Direct Brain–Computer Communications” [VID 73]. In this article, the Belgian scientist, who had studied in Paris and taught at the University of California, Los Angeles, describes the hardware architecture and the processing he sought to implement in order to produce a BCI through electroencephalographic signals. In 1971, Eberhard Fetz had already shown that it was possible to train a monkey to voluntarily control cortical motor activity by providing visual information according to discharge rate [FET 71]. These two references show that since that time, BCIs could be implemented in the form of invasive or non-invasive brain activity measurements, that is, measurements of brain activity at the neural or scalp levels. For a more comprehensive history of BCIs, the reader may refer to the following articles: [LEB 06, VAA 09].
Although BCIs have been present in the field of research for over 40 years, they have only recently come to the media’s attention, often described in catchy headlines such as “writing through thought is possible” or “a man controls a robot arm by thinking”. Beyond announcements motivated by journalists’ love for novelty or by scientists and developers’ hopes of attracting the attention of the public and of potential funding sources, what are the real possibilities for BCIs within and outside research labs?
This book seeks to pinpoint these technologies somewhere between reality and fiction, and between super-human fantasies and real scientific challenges. It also describes the scientific tools that make it possible to infer certain aspects of a person’s mental state by surveying brain activity in real time, such as a person’s interest in a given element of a computer screen or the will to make a certain gesture. This book also details the material and software elements involved in the process and explores patients’ expectations and feedback and the actual number of people using BCIs.
I.2. Introduction to BCIs
Designing a BCI is a complex and difficult task that requires knowledge of several disciplines such as computer science, electrical engineering, signal processing, neuroscience and psychology. BCIs, whose architecture is summarized in Figure 1.1, are closed loop systems usually composed of six main stages: brain activity recording, preprocessing, feature extraction, classification, translation into a command and feedback:
–
Brain activity recording
makes it possible to acquire raw signals that reflect the user’s brain activity [WOL 06]. Different kinds of measuring devices can be used, but the most common one is electroencephalography (EEG) as shown in
Figure I.1
;
–
Preprocessing
consists of cleaning up and removing noise from measured signals in order to keep only the relevant information they contain [BAS 07];
–
Feature extraction
consists of describing signals in terms of a small number of relevant variables called “features” [BAS 07]; for example, an EEG signal’s strength on some sensors and on certain frequencies may count as a feature;
–
Classification
associates a class to a set of features drawn from the signals within a certain time window [LOT 07]. This class corresponds to a type of identified brain activity pattern (for example the imagined movement of the left or right hand). A classification algorithm is known as a “classifier”;
–
Translation into a command
associates a command with a given brain activity pattern identified in the user’s brain signals. For example, when imagined movement of the left hand is identified, it can be translated into the command: “move the cursor on the screen toward the left”. This command can then be used to control a given application, such as a text editor or a robot [KÜB 06];
–
Feedback
is then provided to the user in order to inform him or her about the brain activity pattern that was observed and/or recognized. The objective is to help the user learn to modulate brain activity and thereby improve his or her control of the BCI. Indeed, controlling a BCI is a skill that must often be learned [NEU 10].
Figure I.1.Architecture of a BCI working in real time, with some examples of applications
Two stages are usually necessary in order to use a BCI: (1) an offline calibration stage, during which the system settings are determined, and (2) an online operational stage, during which the system recognizes the user’s brain activity patterns and translates them into application commands. The BCI research community is currently searching for solutions to help avoid the costly offline calibration stage (see, for example, [KIN 14, LOT 15]).
I.2.1. Taxonomy of BCIs
BCIs can often be classified into different categories according to their properties. In particular, they can be classified as active, reactive or passive; as synchronous or asynchronous; as dependent or independent; and as invasive, non-invasive or hybrid. We will review the definition of those categories, which can be combined when describing a BCI (for example a BCI can be active, asynchronous and non-invasive at the same time):
–
Active/reactive/passive
[ZAN 11b]: an active BCI is a BCI whose user is actively employed by carrying out voluntary mental tasks. For example, a BCI that uses imagined hand movement as mental command is an active BCI. A reactive BCI employs the user’s brain reactions to given stimuli. BCIs based on evoked potentials are considered reactive BCIs. Finally, a BCI that is not used to voluntarily control an application through mental commands, but that instead passively analyzes the user’s mental state in real time, is considered a passive BCI. An application monitoring a user’s mental load in real time to adapt a given interface is also a passive BCI;
–
Synchronous/asynchronous
[MAS 06]: user–system interaction phases may be determined by the system. In such a case, the user can only control a BCI at specific times. That kind of system is considered a synchronous BCI. If interaction is allowed at any time, the interface is considered asynchronous;
–
Dependent/independent
[ALL 08]: a BCI is considered independent if it does not depend on motor control. It is considered dependent in the opposite case. For example, if the user has to move his or her eyes in order to observe stimuli in a reactive BCI, then BCI is dependent (it depends on the user’s ocular montricity). If the user can control a BCI without any movement at all, even ocular, the BCI is independent;
–
Invasive/non-invasive
: as specified above, invasive interfaces use data measured from within the body (most commonly from the cortex), whereas non-invasive interfaces acquire surface data, that is, data gathered on or around the head;
–
Hybrid
[PFU 10]: different neurophysiological markers may be used to pilot a BCI. When markers of varied natures are combined in the same BCI, it is considered hybrid. For example, a BCI that uses both imagined hand movement and brain responses to stimuli is considered hybrid. A system that combines BCI commands and non-cerebral commands (e.g. muscular signals) or more traditional interaction mechanisms (for example a mouse) is also considered hybrid. In sum, a hybrid BCI is a BCI that combines brain signals with other signals (that may or may not emanate from the brain).
I.2.2. BCI applications
Throughout the last decade, BCIs have proven to be extremely promising, especially for handicapped people (in particular for quadriplegic people suffering from locked-in syndrome and stroke patients), since several international scientific results have shown that it is possible to produce written text or to control prosthetics and wheelchairs with brain activity. More recently, BCIs have also proven to be interesting for people in good health, with, for example, applications in video games, and more generally for interaction with any automated system (robotics, home automation, etc.). Finally, researchers have shown that it is also possible to use BCIs passively in order to measure a user’s mental state (for example stress, concentration or tiredness) in real time and regulate or adapt their environment in response to that state.
I.2.3. Other BCI systems
Let us now examine some systems that are generally related to BCIs. Neuroprostheses are systems that link an artificial device to the nervous system. Upper limb myoelectric prostheses analyze electric neuromuscular signals to identify movements that the robotic limb will carry out. Neuroprostheses are not BCIs if they do not employ brain activity, but rather, the peripheral nervous system activity. Exoskeletons also make it possible to bring life to a limb by equipping it with mechanical reinforcement, but to date they are very seldom activated by brain activity1. Cochlear implants and artificial retinas can be compared to neuroprostheses since they connect a device that replaces a defective organ with the central nervous system. However, these kinds of implants differ from BCIs in their directionality, since they do not measure neural activity, but rather stimulate it artificially.
I.2.4. Terminology
Several other terms are employed to refer to BCIs. In this regard, the term “brain–machine interface” refers to the same idea, although the term is more often used when the brain measurements are invasive. Although more rarely, the term “direct neural interface” is also sometimes used to designate BCIs. In this book, the term “brain–computer interface” will be employed because it underscores the idea that the processing chain is not fixed; this is to say that the system may adapt to evolutions in brain signals and the user’s preferences through learning. The acronym BCI will also largely be used throughout the book, since it is the most commonly employed.
I.3. Book presentation
This book seeks to give an account of the current state of advances in BCIs by describing in detail the most common methods for designing and using them. Each chapter is written by specialists in the field and is presented in the most accessible way possible in order to address as large an audience as possible. This book, Volume 2 (Technology and Applications) follows Volume 1 (Foundation and Methods).
I.3.1. Volume 2: Technology and Applications
BCI applications are abundant. Part 1 of the book, entitled “Fields of Application”, focuses on applications in the clinical and video-game fields. The scope of clinical applications includes consciousness disorders, motor rehabilitation, verbal communication and presurgical diagnosis of epilepsy. Examining these applications makes it possible to better understand the future and current limits of BCIs.
The Part 2, entitled “Practical Aspects of BCI Implementation” explains the inherent difficulties of this task. This section’s first chapter (Chapter 6) studies the expression of patients’ needs, which drives the development of BCIs devoted to them. Next, we move on to study platforms that employ EEG. The role that sensors play in this technology is explained, as well as the material and software requirements they involve. Finally, some recommendations for developing EEG experiments are presented.
The Part 3, entitled “Step by Step Guide to BCI Design with OpenViBE” explores the software implementation and execution of the methods proposed in this book. It first presents the main existing software platforms, placing special emphasis on OpenViBE, an open source software that makes it possible to quickly design BCIs and to perform real-time processing for neuroscience2. The following chapters illustrate several practical applications through OpenViBE, allowing readers to experiment with the concepts presented in the book by downloading scenarios and signals, or even to begin their own research in the field.
The book ends with Part 4 entitled “Societal Challenges and Perspectives”, which opens up the debate about the use of BCIs from ethical, philosophical and societal perspectives. The insights that the humanities and societal sciences can bring to the field of BCIs are extremely important as not to lose sight of the fact that new technologies must always be developed with the greatest possible respect for animals, humans and society at large.
I.3.2. Reading guide
This book is intended for anyone seeking to understand BCIs, their origins, how they work, how they are used and the challenges they face. It may prove useful for people approaching the field in order to carry out research (researchers, engineers, PhD students, postdoctoral fellows) but also for present and future users (patients, medical practitioners, video game developers and artists), as well as for decision makers (investors, insurance experts and legal experts).
In order to facilitate the reading of this multidisciplinary book, we have provided an icon signaling the scope of each chapter’s content. Chapters that are essential for understanding how BCIs work are denoted with . Those chapters compose a common core of indispensable knowledge, which can be complemented by more specialized notions in:
– neuroscience
– math and computer science
– clinical fields
– technological fields
– societal fields
We suggest the following reading combinations according to readers’ profile or to their field of specialization:
– general public:
– patients:
– medical/clinical practitioners:
– neuropsychologists, cognitive neuroscientists:
– mathematicians, computer scientists:
– electrical engineers, mechatronic engineers:
– investors, insurance experts and legal practitioners:
I.4. Acknowledgments
This book is the collective work of a very large number of colleagues from very different disciplines, which would not have been possible without their contributions. We would like, therefore, to thank all the authors, and to all the colleagues and friends who have helped us in writing this book. We are indebted to Flora Lotte for creating the cover illustration.
I.5. Bibliography
[ALL 08] Allison B., MCFarland D., Schalk G. et al., “Towards an independent Brain–Computer Interface using steady state visual evoked potentials”, Clinical Neurophysiology, vol. 119, no. 2, pp. 399–408, 2008.
[BAS 07] Bashashati A., Fatourechi M., Ward R.K. et al., “A survey of signal processing algorithms in Brain–Computer Interfaces based on electrical brain signals”, Journal of Neural Engineering, vol. 4, no. 2, pp. R35–57, 2007.
[FET 71] Fetz E.E., Finocchio D.V., “Operant conditioning of specific patterns of neural and muscular activity”, Science, vol. 174, 1971.
[KÜB 06] Kübler A., Mushahwar V., Hochberg L. et al., “BCI meeting 2005-workshop on clinical issues and applications”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 131–134, 2006.
[KIN 14] Kindermans P.-J., Tangermann M., Müller K.-R. et al., “Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller”, Journal of Neural Engineering, vol. 11, no. 3, 2014.
[LEB 06] Lebedev M., Nicolelis M., “Brain-machine interfaces: past, present and future”, Trends in Neurosciences, vol. 29, no. 9, pp. 536–546, 2006.
[LOT 07] Lotte F., Congedo M., Lécuyer A. et al., “A review of classification algorithms for EEG-based Brain–Computer Interfaces”, Journal of Neural Engineering, vol. 4, pp. R1–R13, 2007.
[LOT 15] Lotte F., “Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based Brain–Computer Interfaces”, Proceedings of the IEEE, vol. 103, no. 6, pp. 871–890, 2015.
[MAS 06] Mason S., Kronegg J., Huggins J. et al., “Evaluating the performance of self-paced BCI technology”, report, Neil Squire Society, 2006.
[NEU 10] Neuper C., Pfurtscheller G., “Neurofeedback Training for BCI Control”, in Graimann B., Pfurtscheller G., Allison B. (eds), Brain–Computer Interfaces, Springer, 2010.
[PFU 10] Pfurtscheller G., Allison B.Z., Bauernfeind G. et al., “The hybrid BCI”, Frontiers in Neuroscience, vol. 4, p. 3, 2010.
[VAA 09] Vaadia E., Birbaumer N., “Grand challenges of Brain–Computer Interfaces in the years to come”, Frontiers in Neuroscience, vol. 3, no. 2, pp. 151–154, 2009.
[VID 73] Vidal J.J., “Toward direct brain–computer communication”, Annual Review of Biophysics and Bioengineering, vol. 2, no. 1, pp. 157–180, 1973.
[WOL 06] Wolpaw J., Loeb G., Allison B. et al., “BCI Meeting 2005 – workshop on signals and recording methods”, IEEE Transaction on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 138–141, 2006.
[ZAN 11] Zander T., Kothe C., “Towards passive Brain–Computer Interfaces: applying Brain–Computer Interface technology to human-machine systems in general”, Journal of Neural Engineering, vol. 8, 2011.
1
However, the MindWalker project has started research in that direction; see
https://mindwalker-project.eu/
.
2
http://openvibe.inria.fr
.
Introduction written by Maureen CLERC, Laurent BOUGRAIN and Fabien LOTTE.
Part 1Fields of Application
1Brain–Computer Interfaces in Disorders of Consciousness
1.1. Introduction
The notion of an altered state of consciousness describes a large spectrum of pathological states in which the patient is not able to interact with his or her environment by means of speech or gesture. Even if there remain active residual cognitive processes, or if the patient retains some degree of self or environmental awareness, he or she is unable to communicate. These minimal fragments of consciousness can pass unobserved clinically due to motor deficits, sensory disorders, fatigability, cognitive disorders or fluctuations in wakefulness. Caring for patients with altered states of consciousness presents challenges at multiple different levels, not just ethical, but practical, human, and economical, affecting everything from diagnosis to treatment-related decision making. Today, research on altered states of consciousness is a dynamic field of neuroscience with the two-part objective of shedding light on poorly understood neural mechanisms of consciousness, and finding ways to assess patients’ levels of consciousness or even restore basic communication whenever possible.
The purpose of this chapter is to provide a brief overview of studies in electrophysiology and neuroimaging that represent advances in terms of both care for patients with altered states of consciousness and the way that we view these patients, followed by a presentation of the most recent works performed in this field together with a future outlook based on brain–computer interface (BCI) techniques. In the first section, we show how resting brain signals and passive responses to stimuli can provide the basis for a hierarchical approach to the functional assessment of patients, from the prognosis of coma awakening to the differential diagnosis between different levels of consciousness. In the second section, we present paradigms for eliciting voluntary participation from the patient. The goal of these so-called “active” paradigms is to determine the patient’s level of consciousness, as well as his or her capacity to cooperate. Finally, in the third section, we show how the ability to measure brain function in real time could soon make it possible to monitor patients’ cognitive functions for diagnostic purposes, restore some form of communication with certain patients and perhaps even assist in their rehabilitation.
1.2. Altered states of consciousness: etiologies and clinical features
1.2.1. From coma to awakening
Coma is defined as a severe disorder of consciousness with full loss of awareness and the incapacity to respond to external prompts. Coma occurs following significant brain lesions that are usually the result of traumatic brain injury or cerebral anoxia, but may also arise from other origins such as metabolic, infectious or toxic disorders. Coma is characterized by functional alterations to attention and wakefulness mechanisms in the ascending reticular formation [ZEM 97]. Both the ability to wake (arousal) and the substance of consciousness (awareness) are lost. This acute phase of coma may last from several days to several weeks, during which the future progression of the coma is difficult to predict. If a patient survives the acute phase of the coma, he/she enters a phase in which the eyes are open with the appearance of wakefulness, but with no communication. If no objective signs of a reaction to stimuli are observed, the patient is said to be in a vegetative state (VS, or “unresponsive wakefulness syndrome”, see [LAU 10]). In some cases, this VS can last for months or even years – this state is given the name of permanent vegetative state (see [LAU 04]). If the patient is able to follow simple commands or shows purposeful non-reflex behavior, the state is described as a minimally conscious state (MCS, see [GIA 02]). MCS can refer to a wide range of situations, depending on the nature and the extent of the observed responses. For this reason, some authors distinguish between MCS+ for patients with responses said to be high level (e.g. intelligible verbalization) and MCS- for patients with only low-level responses (e.g. localization of a pain stimulus) [BRU 11].
In the best-case scenario after awakening from coma, the patient recovers to a fully conscious state, with variable degrees of long-term functional consequences. The potential to improve relational capabilities and the timescales of these improvements vary strongly from patient to patient. They are in particular strongly linked to the etiology of the coma. The clinical progression of traumatic comas is generally more favorable than that of anoxic comas, with a faster and stronger recovery of consciousness. One very specific and infrequent postcoma state is locked-in syndrome (LIS, see [BAU 79]), which arises as a result of lesions on the brainstem. This is not a consciousness disorder, as patients awaken from coma fully conscious, but with full paralysis in all voluntary muscles except for the eyelids. Patients’ cognitive abilities generally remain intact, but their means of communication are extremely limited, or even non-existent (complete locked-in). Degenerative neurological diseases such as amyotrophic lateral sclerosis (ALS) can also lead to a LIS after paralysis develops progressively in the patient’s muscles without affecting his/her consciousness [HAY 03].
1.2.2. The importance of differential diagnosis
In a postcoma state, wakefulness is restored but the capacity to interact with others, self-awareness and awareness of surroundings might remain affected to a greater or lesser extent. The patient’s state can be thought of as part of a continuum of states of consciousness ranging from fully non-responsive (VS) to a regular state of consciousness (LIS), with MCSs in between.
Establishing a differential diagnosis between VS and MCS, or in other words discerning the presence of some conscious awareness in patients unable to communicate, is particularly crucial. The right diagnosis is the first step toward the right course of treatment. An optimal regime of care should involve interacting with the patient whenever possible, for both basic everyday life situations and consequence-heavy decision making. Clinical assessment involves observing the patient’s spontaneous behavior as well as behavioral changes in response to various stimuli. The goal is to detect a motor response consistent with the given command, an oriented response to sound or pain, an intelligible verbalization or visual tracking or fixation, all of which are signs of emerging consciousness. The differential diagnosis between VS and MCS is somewhat unreliable, as it is difficult to distinguish subtle indications of consciousness from purely automatic responses, and because of the limitations posed by the patient’s state. It has been estimated that up to 43% of patients with disorders of consciousness may have been incorrectly diagnosed as vegetative [SCH 09]. Indeed, these patients can suffer from peripheral or cortical sensory deficits, neuromuscular deficits and other pathological conditions that disguise their state of consciousness. Assessment is repeated multiple times to account for possible fluctuations in wakefulness. Carefully structured scoring tests have been validated in an attempt to standardize this clinical evaluation process. One commonly used test is the Coma Recovery Scale Revised [GIA 04, SEE 10], which explores the patient’s auditive, visual, motor and oromotor/verbal capacities in depth, as well as communication and wakefulness. The application of these kinds of test should improve the precision of the diagnostic process, but due to the difficulties described above, a standard measure of consciousness does not exist.
1.3. Functional assessment of patients with altered states of consciousness (passive paradigms)
1.3.1. Prognosis of coma outcome
For therapeutic, ethical and economical reasons, establishing a prognosis for both survival and awakening during the acute phase of coma is the most urgent medical objective. The prognosis depends on the etiology of the coma, the severity and extent of brain lesions, and the patient’s clinical functional state. The criterion used to evaluate prognostic techniques is the clinical outcome of the patient 6 months or 1 year after coma onset. Classically, patient states after coma are evaluated according to the Glasgow Outcome Scale [JEN 75], which originally comprised three levels of awakening (no disability, moderate or severe disability), VS and death. This system has the benefit of being simple, but although it may be used to describe the functional state and overall level of dependency of the patient, it does not provide further information about potential intermediate disorders of consciousness such as MCSs [GIA 02].
