Brain-Computer Interfaces 1 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 519

Veröffentlichungsjahr: 2016

Beschreibung

Brain–computer interfaces (BCI) are devices which measure brain activity and translate it into messages or commands, thereby opening up many investigation and application possibilities. 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 first volume, Methods and Perspectives, presents all the basic knowledge underlying the working principles of BCI. It opens with the anatomical and physiological organization of the brain, followed by the brain activity involved in BCI, and following with information extraction, which involves signal processing and machine learning methods. BCI usage is then described, from the angle of human learning and human-machine interfaces.

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.

This first volume will be followed by a second volume, entitled Technology and Applications.

Leseprobe

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: Anatomy and Physiology

1 Anatomy of the Nervous System

1.1. General description of the nervous system

1.2. The central nervous system

1.3. The cerebellum

1.4. The spinal cord and its roots

1.5. The peripheral nervous system

1.6. Some syndromes and pathologies targeted by Brain–Computer Interfaces

1.7. Conclusions

1.8. Bibliography

2 Functional Neuroimaging

2.1. Functional MRI

2.2. Electrophysiology: EEG and MEG

2.3. Simultaneous EEG-fMRI

2.4. Discussion and outlook for the future

2.5. Bibliography

3 Cerebral Electrogenesis

3.1. Electrical neuronal activity detected in EEG

3.2. Dipolar and quadrupole fields

3.3. The importance of geometry

3.4. The influence of conductive media

3.5. Conclusions

3.6. Bibliography

4 Physiological Markers for Controlling Active and Reactive BCIs

4.1. Introduction

4.2. Markers that enable active interface control

4.3. Markers that make it possible to control reactive interfaces

4.4. Conclusions

4.5. Bibliography

5 Neurophysiological Markers for Passive Brain–Computer Interfaces

5.1. Passive BCI and mental states

5.2. Cognitive load

5.3. Mental fatigue and vigilance

5.4. Attention

5.5. Error detection

5.6. Emotions

5.7. Conclusions

5.8. Bibliography

PART 2: Signal Processing and Machine Learning

6 Electroencephalography Data Preprocessing

6.1. Introduction

6.2. Principles of EEG acquisition

6.3. Temporal representation and segmentation

6.4. Frequency representation

6.5. Time–frequency representations

6.6. Spatial representations

6.7. Statistical representations

6.8. Conclusions

6.9. Bibliography

7 EEG Feature Extraction

7.1. Introduction

7.2. Feature extraction

7.3. Feature extraction for BCIs employing oscillatory activity

7.4. Feature extraction for the BCIs employing EPs

7.5. Alternative methods and the Riemannian geometry approach

7.6. Conclusions

7.7. Bibliography

8 Analysis of Extracellular Recordings

8.1. Introduction

8.2. The origin of the signal and its consequences

8.3. Spike sorting: a chronological presentation

8.4. Recommendations

8.5. Bibliography

9 Statistical Learning for BCIs

9.1. Supervised statistical learning

9.2. Specific training methods

9.3. Performance metrics

9.4. Validation and model selection

9.5. Conclusions

9.6. Bibliography

PART 3: Human Learning and Human-Machine Interaction

10 Adaptive Methods in Machine Learning

10.1. The primary sources of variability

10.2. Adaptation framework for BCIs

10.3. Adaptive statistical decoding

10.4. Generative model and adaptation

10.5. Conclusions

10.6. Bibliography

11 Human Learning for Brain–Computer Interfaces

11.1. Introduction

11.2. Illustration: two historical BCI protocols

11.3. Limitations of standard protocols used for BCIs

11.4. State-of-the-art in BCI learning protocols

11.5. Perspectives: toward user-

adapted

and user-

adaptable

learning protocols

11.6. Conclusions

11.7. Bibliography

12 Brain–Computer Interfaces for Human–Computer Interaction

12.1. A brief introduction to human–computer interaction

12.2. Properties of BCIs from the perspective of HCI

12.3. Which pattern for which task?

12.4. Paradigms of interaction for BCIs

12.5. Conclusions

12.6. Bibliography

13 Brain Training with Neurofeedback

13.1. Introduction

13.2. How does it work?

13.3. Fifty years of history

13.4. Where NF meets BCI

13.5. Applications

13.6. Conclusions

13.7. Bibliography

List of Authors

Index

Contents of Volume 2

End User License Agreement

Guide

Cover

Table of Contents

Begin Reading

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Series Editor

Maureen Clerc

Brain–Computer Interfaces 1

Foundations and Methods

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 author of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2016942416

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-84821-826-0

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 a 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 in two volumes. In the first volume (Foundations and Methods) , readers walk along the path covering the main principles of BCI, with all its subtle meanders which they may decide to jump over or to explore in more details. This is a volume that we may well need to read in several iterations as we go into detail into the field and its different components. Part 1 provides all the necessary background in anatomy and physiology of the brain and nervous system to understand BCI from a neuroscience perspective. Part 2 covers the signal processing and machine learning sides of BCI, while Part 3 deals with human learning, and the interplay between the human and the machine.

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. MILLÁN

Geneva

Switzerland

May 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 teach a monkey to voluntarily control motor cortex brain 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 his or her environment or the will to make a certain gesture. This book also explores patients’ expectations and feedback, the actual number of people using BCIs and details the material and software elements involved in the process.

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 extract 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 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 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’s 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.Classification 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 11]: 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 commands is an active BCI. A reactive BCI is a BCI that 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 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 employ 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), 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 neuroprostheses 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 1 (Foundations and Methods), is followed by a second book, Volume 2 (Technology and Applications).

I.3.1.Foundations and methods

This first volume introduces the basic notions necessary to understand how a BCI works.

The brain stands at the core of a BCI. It is an organ whose functioning still remains largely beyond our understanding, although its basic principles are known. The first part of the book, entitled “Anatomy and Physiology”, explains the anatomical and physiological foundations of BCIs, as well as the pathologies to which they can be applied. This part also explores devices that make it possible to measure brain activity. Finally, it studies the neurophysiological markers used in active or reactive BCIs, and in passive interfaces.

The second part, which is entitled “Signal Processing and Learning”, focuses on brain activity analysis. This preparatory phase that precedes the implementation of a BCI consists of a processing chain. Preprocessing makes it possible to increase the percentage of useful signals. In turn, it becomes necessary to represent those signals in a simplified manner in terms of characteristics that are potentially useful to the BCI. According to the type of BCI, relevant characteristics will vary greatly, and two chapters will study those issues for EEG recordings, as well as for intracerebral recordings. The last crucial stage is that of machine learning, which makes it possible to define appropriate classifiers adjusted to and optimized for each user. Learning proceeds in two stages: the calibration stage generally takes place offline and operates on data gathered when the user repeatedly performs mental tasks that are relevant to the BCI, following instructions provided to him or her. Those recorded brain signals will serve as examples in order to find the best calibration settings for that particular user. Next, the online, closed loop, usage stage applies the classifier to new data.

The third part, entitled “Human Learning and Human–Machine Interaction”, analyzes the BCI use phase. Machine learning must adapt to changes that can take place over a long period of time. To that end, BCIs use adaptive learning methods. Using a BCI is not a self-evident task, and we will examine the human learning that is necessary in order to attain the skills necessary to do so. Concepts of Human–Machine interaction must also be taken into account in order to best use the commands emitted by a BCI and to ensure an optimal user experience, that is, a usable, effective and efficient interaction. Finally, we will explore the concept of neurofeedback, or the perceptive feedback provided to users about their brain activity, and we will also study the relation between this approach and BCIs.

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

– fields concerning societal issues

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 co-workers 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/

.

Introduction written by Maureen CLERC, Laurent BOUGRAIN and Fabien LOTTE.

PART 1Anatomy and Physiology

1Anatomy of the Nervous System

This chapter’s objective is not to describe the nervous system in detail, which would be impossible to do in just a few pages, but rather to provide readers who are interested in Brain–Computer Interfaces but who are not an experts in anatomy, with some basics of neuroanatomy and functional anatomy as well as the vocabulary used to talk about them. Readers looking for greater depth and precision in the description of anatomical structures may consult reference books in neuroanatomy (we can cite for their clarity and exhaustiveness [KAM 13, CHE 98, DUU 98])

This description seeks to provide a general understanding of the structure of the adult nervous system, its main constituents and their principal functions, and to thereby better understand the pathologies associated with it.

This chapter will first provide a general description of the nervous system, and it will then focus on a description of the central nervous system (CNS), as well as that of the peripheral nervous system (PNS). In the last section, we will succinctly describe the main pathologies that can be addressed through the use of Brain–Computer Interfaces.

1.1. General description of the nervous system

A neuron is composed of a cell body and an axon, which terminates in a synaptic area. The information that travels through it is an electric signal that corresponds to a depolarization of the axonal membrane: the action potential. In this way, the axon transmits the action potential up to the synapse, the area of communication between neurons. Molecules emitted at the synapses under the influence of action potentials are called neurotransmitters. These neurotransmitters may either be excitatory or inhibitory and thus determine the response obtained.

Neurons are organized in pathways, tracts or networks whose connections determine their roles. Traditionally, a distinction is made between the CNS and the PNS. It is common to talk about efferent neurons, which transmit information from the CNS to the PNS, and afferent neurons, which transmit information from the PNS to the CNS.

The CNS includes the encephalon, which is enclosed in the skull, and the spinal cord in the spinal canal. The encephalon is itself composed of the brain stem, the cerebellum and the two hemispheres of the brain. The brain stem, located in the most caudal part of the encephalon, gives way to 12 pairs of nerves that are known as cranial nerves. The cerebellum is located in the back of the brain stem. Each hemisphere is composed of several lobes (frontal, parietal, temporal, occipital and the insular cortex). From a functional perspective, each hemisphere has its own specific functions, especially for the most complex functions (for example language in the frontal and temporal areas of the dominant hemisphere, spatial orientation in the right parietal lobe, the organization of complex gestures in frontal lobe, etc.).

The cortex, which is located on the surface of the hemispheres, is composed of gray matter that contains neuron cell bodies and is organized into six layers. The basal ganglia are located at the base of the hemispheres. These are also composed of gray matter. White matter contains myelinated axons from CNS neurons and it makes it possible to establish connections between different parts of the CNS through associative fibers (connecting parts of the cortex to each other or to the basal ganglia) and through fibers that stretch out toward the spinal cord.

The spinal cord, which contains ascending fibers and descending fibers, transmits all motor, sensitive and vegetative information between the encephalon and the PNS. It is also composed of gray matter and is the regulation center for a certain number of reflex actions.

The roots that give way to the PNS arise from the spinal cord. These roots form, passing through the (brachial and lumbosacral) plexuses, the entire set of nerve trunks that make it possible to innervate the skeletal muscles (efferent motor fibers) to transmit sensory (sensitive afferent fibers) and vegetative (efferent and afferent vegetative fibers) information.

Different systems (motor, somatosensory, sensory) may have either ascending or descending pathways, going from the peripheral receptor to the area of the brain involved in interpreting the signal, or going from the cortex all the way to the effector (for example the muscle). We may cite, for example, the descending motor tracts distributed in a (corticospinal and corticobulbar) pyramidal pathway, which is the pathway for voluntary motion. We may also cite extrapyramidal pathways, which include other motor pathways. Other pathways include sensitive, visual, auditory, vestibular and olfactory tracts.

1.2. The central nervous system

The CNS includes the encephalon, which is located in the skull, and the spinal cord, which is located in the spinal canal.

Figure 1.1.General view of the human encephalon (http://lecerveau.mcgill.ca)

The encephalon (Figure 1.1) is usually composed of the following structures:

– the telencephalon;

– the diencephalon;

– the brain stem itself comprising the midbrain, the pons and the medulla oblongata. The cerebellum is located in the back of the pons, which is connected to the pons through the cerebellar peduncle.

It is also possible to describe the encephalon from its formation at the embryonic stage. In such a case, we can distinguish between the hindbrain, which will become the medulla oblongata and the metencephalon (pons and cerebellum), the midbrain and the prosencephalon, which will turn into the diencephalon and the telencephalon.

1.2.1.The telencephalon

The cerebrum is composed of two hemispheres (right and left) that are connected to one another through white matter tracts (especially by the corpus callosum). The surface of each hemisphere has a folded aspect, which makes it possible to individualize the lobes (Figure 1.2