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...