Introduction

Verbal or non-verbal information exchange is the basis of human communication. However, some people lose this fundamental ability of communication through accidents or inherited neuromuscular disorders. In the absence of methods for repairing or restoring function due to disease or damage, various alternatives in the form of assistive devices to enable individuals to communicate with and control their environment have been developed. The brain-computer interface (BCI), i.e., electroencephalography (EEG)-based communication, is a new way of controlling devices that does not require eye movement or muscle activity. This chapter introduces the various components of a typical BCI system and explains each component's importance and function within the complete BCI system.

Keywords

Brain-computer interface; electroencephalography; graphical user interface; motor imagery; signal filtering

1.1 Introduction

Verbal or non-verbal information exchange is the basis of human communication. However, some people lose this fundamental ability of communication because of accidents or inherited neuromuscular disorders. The purpose of the work presented in this book is to contribute to the development of novel methods to allow people to regain freedom of movement/communication by way of controlling devices directly with their brain, bypassing the normal communication channels.

The human brain is estimated to contain about 100 billion neurons [1-4]. The spinal cord acts as an intermediate cable that carries information to and from our brain to control various body parts and their movements. People with an injury to the spinal cord are still able to generate the output signals from the brain, but these signals do not reach the specific body parts because the intermediate spinal cable is damaged. Several technologies using a joystick, head movement, eye gazing and many more may help a physically challenged person to control a robotic device or a wheelchair [5-9]. However, these techniques require the use of partial movement control through the hand, head or eyes etc., and therefore make the control issue less complicated. The issue becomes more challenging when people with complete loss of control over their voluntary muscles are involved, a condition generally known as locked-in syndrome [10,11], in which people are unable to speak and move but are conscious and can think and reason. A number of neurological diseases such as stroke1, severe cerebral palsy2, motor neuron disease (MND)3, amyotrophic lateral sclerosis (ALS), and encephalitis4 can result in such severe motor paralysis [12]. Many of these diseases can lead to restrictions in communication capacity. A brain-computer interface (BCI) can enable such physically challenged people to achieve greater independence by making technology accessible. BCI technology provides an alternative communication channel between the human brain (that does not depend on the brain's normal output channels of the peripheral nerves and muscles) and a computer [13-21]. The three most commonly discussed diseases/injuries cited in the BCI literature as being a case of locked-in syndrome are ALS, high spinal cord injury and brain stem stroke [16,22-24].

 Patients suffering from ALS can undergo severe physical impairment due to the degeneration of nerve cells that control the voluntary muscles. In the later stages of ALS, the most basic human actions are affected, including speech, swallowing and breathing [25].

 Spinal cord injury (SCI) can result in damage to myelinated fiber tracts or the nerve roots that carry the signals to and from the brain [25]. In complete SCI, most of the motor functions and sensation below the neurological level are affected or completely lost [26]. SCI has a global annual incidence of 15-40 cases per million population [27] and less than 5% of people suffering from SCI recover locomotion [26].

 Brain stem stroke can be fatal, as the brain stem controls many of the basic and fundamental activities for life, such as breathing, heart rate, blood pressure, swallowing and eye movement [28]. People with severe brain stem stroke may also enter into a locked-in state and lose motor functions [29].

BCI (i.e., electroencephalography [EEG])-based communication produces new channels for controlling devices which would not be possible through the modes of communication that require eye movement or some muscle activity. Hans Berger performed a systematic study of the electrical activity of the human brain, and developed the EEG5. The first scientific literature referring to communication between the brain and the computer dates back to the early 1970s, and is due to Vidal [18], who suggested the feasibility of direct brain communication. To achieve this, the intent of the user must be extracted from the brain via the EEG or brain waves.

A typical BCI scheme generally consists of a data acquisition system, preprocessing of the acquired signals, the feature extraction process (FEP), classification of the features and finally the control interface and device controller, as shown in Figure 1.1. The EEG signals are acquired by mounting electrodes on the scalp of the user. These raw EEG signals have very low amplitude [30], very low signal-to-noise (SNR) ratio and considerable noise contamination. Preprocessing is carried out to obtain cleaner EEG signals by removing the unwanted components embedded in the EEG, which can considerably reduce the computational load on the rest of the BCI components.

The work presented in this book has focused on the preprocessing stage for signal enhancement and extracting more motor imagery (MI)6 (mental imagination of movement) [31] related information from the acquired noisy EEG. These raw EEG signals are considered as a realization of a random or stochastic process [32]. When an accurate description of the signal is not available, a stochastic filter can be designed based on probabilistic measures. Therefore, the approach undertaken in this book is to use the concepts from quantum mechanics (QM) and the Schrodinger wave equation (SWE). A recurrent quantum neural network (RQNN) is constructed by using a layer of neurons within the neural network framework by computing a time-varying probability density function (pdf) of the noisy input signal (cf. Chapter 3). This pdf evolves recurrently under the influence of the SWE and helps to enhance the EEG (cf. Chapter 5).

Features are extracted from the filtered EEG. The classifier interprets these extracted features to categorize the input signal into a designated output class. The control interface or the graphical user interface (GUI) further interprets the classifier output in the form of a command to be sent to the controlled device. The GUI also provides appropriate feedback information to the BCI user [7], and can quicken the issuance of the command from the BCI user to the device that is controlled. A two-class BCI system has two output classes in the form of a left hand MI or a right hand/foot MI. But the task of maneuvering a mobile robot requires commands in the form of forward, left, right, backward and start/stop, using just the two-class information; i.e., there is very limited communication bandwidth. However, given the inherent higher accuracy compared to multi-class BCIs, this book focuses on utilizing a two-class BCI. This book proposes an intelligent, adaptive and user-centric interface design that plays a major role in compensating for the low bandwidth of a two-class BCI and simultaneously capitalizes on the intrinsic higher accuracy characteristic that is typical of a two-class BCI system (cf. Chapter 4 and Chapter 6).

In summary, this book outlines the recent developments in MI-based BCI, specifically focusing on reviewing the existing signal processing and classification methodologies, as well as different interface designs for a BCI system. It proposes an alternative nature-inspired information processing approach based on the concepts from QM, which is referred to as the RQNN model and is utilized for EEG signal enhancement (cf. Chapter 3 and Chapter 5). It also proposes an intelligent user interface design (cf. Chapter 4) which is customized to provide effective control of a wheelchair/mobile robot and a robot arm for a quicker communication process (cf. Chapter 6).

1.2 Rationale

BCI technology has not yet reached a critical level of acceptance even forty years after its inception. The challenges in this domain begin right at the stage of acquiring the EEG signals from the brain. An EEG is recorded non-invasively, so it is a mixture of the signal of interest from the activity of the underlying neural networks and an unknown amount of noise. Therefore, the raw signals need to be filtered in order to obtain cleaner EEG signals. Several groups work in the field of EEG filtering [19,33-39]. Most of their approaches involve subject-specific parameters, which, if tuned properly, can enhance the performance of an individual subject in terms of the classification accuracy (CA) [35,40]. However, these frequency-selective techniques lead to an unknown amount of loss of information from the...