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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5276_Библиотеки_им_академика_М_И_Перельмана
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To clarify the ability of such sensors, first, the P300 brain wave has to be guaranteed to exist
among signals that are acquired from the human brain using these sensors. That requires efficient
visual paradigms that keep the user engaged and attended for such a purpose. Second, adequate
data processing and classification algorithms have to be used to efficiently detect the P300 wave
among these noisy signals.
(a) (b)
Figure 1.1: Some commercially available BCI headsets. a) NeuroSky Mindset [59]. b) Emotiv Epoc [26]
1.2 Objectives
The main objective of the study in this thesis is to develop an affordable P300-based
BCI system that uses an Emotiv Epoc headset, one of the recently commercially developed EEG
headsets for consumers, to control different computer based processes. The study first aims to
review general BCI systems and different aspects and approaches of P300-based BCI systems
proposed in the literature. The study also aims to review available low-cost EEG acquisition
systems and selected examples of their use in BCI related research. Then, the next goal is to
review specific P300-based BCI data processing and classification algorithms, test, and compare
them using well-known benchmark P300 datasets from the BCI Competition III, 2004 [11, 12].
Finally, to achieve the main goal mentioned above, the compared algorithms and approaches are
used to develop a real-time P300-based BCI system. The feasibility of the developed inexpensive
BCI system in simple control applications, such as cursor movement will be tested using real-
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time experiments. In the future, more complex applications, such as mobile robot control, can be
tested with the system. The future performance of these control applications will depend on the
performance of the currently proposed system in these simple applications. This thesis analyses
current system performance.
1.3 Outlines
The rest of this thesis is organized into five chapters.
Chapter two presents a general introduction to BCI. The chapter first discusses different
methods for measuring brain activity and the types of neurophysiological signals used in
BCI systems. Second, the chapter reviews in detail BCI systems that use a specific type
of neurophysiological signals, i.e. the P300 signal. A neuroscientific point of view
describes the P300. The same point of view is applied to a discussion about conditions
under which the P300 can be evoked as well as factors that influence its characteristics.
The literature review introduces the basic idea underlying P300-based BCIs. The chapter
presents several systems implementing this idea. Finally, a brief review of available low-
cost EEG sensors and BCIs is given. The chapter also presents a detailed description of
the commercial EEG sensor used in this work, i.e. the Emotiv Epoc headset.
Chapter three discusses algorithmic aspects of P300-based BCI. It addresses different
EEG raw data preprocessing techniques, methods for extracting the distinct and useful
features from P300 neurophysiological signal, and machine learning algorithms. Finally,
this chapter describes criteria for evaluating the different P300-based BCI systems and
algorithms, other issues relevant to the system development, and design of a P300-based
BCI system.
In Chapter four, the P300-based BCI approaches discussed in chapter three are compared
to each other using BCI competition III data sets and based on the results of this
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comparison, an inexpensive P300-based BCI system uses these approaches and a
commercial EEG sensor for control applications is introduced. The first part of the
chapter describes some related research works that have developed P300-based BCIs for
different control applications. It also describes some works that use the Emotiv Epoc
headset in the BCI research field. The second part describes experiments using data sets
from one of the widely used benchmarks. These experiments test and compare the
performance of the selected P300-based BCI algorithms presented in Chapter three.
Finally, the chapter ends with a description of the proposed BCI system. This description
includes the experimental setup, procedure, offline system training, and real-time system
testing.
Chapter five presents and discusses the results of both experiments.
Chapter six presents the conclusion of this thesis and an outlook on possible extensions
and future work of the presented research.
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CHAPTER 2
INTORDUCTION TO BRAIN-COMPUTER INTERFACES
2.1 Introduction
Generally, a BCI is a communication tool that translates brain activities into actions or commands
for computerized devices or other devices. In other words, a BCI enables users to act on their
environment by using only their brain signals without using nerves or muscles. The main goal of
BCI researches is to develop systems that allow disabled users to communicate and interact with
other persons, to control prosthetic limbs, or to control different tasks in their environment. Also,
such developed systems can be used to improve the performance of able-bodied persons in
controlling sophisticated multi-task operations such as security monitoring. To achieve these aims,
many aspects and approaches of BCI systems are currently being investigated. Research areas
may include comparison of invasive and noninvasive technologies to measure brain activities,
assessment of different control signals (i.e. brain activities that can be used as communication
tool), development of algorithms that translate brain signals into computer commands, and the
evolution of new BCI applications.
In this chapter we review some of the general views of BCI researches mentioned above,
highlight recent developments and open problems, and focus on a specific type of BCI that is
related to the work in this thesis, i.e. the P300-based BCI. The review is ordered by the steps that
are needed for brain computer communication (see figure 2.1). We start with methods for
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measuring brain activity in Section 2.2, then in Section 2.3, we give a description of some of the
neurophysiological signals that can be used in BCI systems. A functionality and cost review of
different types of available commercial BCIs in current markets is briefly described in Section 2.4.
In Section 2.5 we present in details the P300-based BCI where we focus on the characteristics of
P300 brain wave and the conditions and factors that affect these characteristics. Some of the
existing and widely used P300-based BCI systems and their applications are described in Section
2.6. Note that signal processing and classification steps are covered in the next chapter.
Feedback
Figure 2.1: General block diagram of a BCI system. A subject performs a specific cognitive task or
concentrates on a specific stimulus. Brain signals are acquired and then processed with signal processing
and classification algorithms. The outcome of the classification is fed into an application, for example a
mobile robot. The application generates feedback to inform the subject about the outcome of classification.
2.2 Brain Signal Acquisition
To enable communication using a BCI, first brain signals have to be measured. Several methods
have been used in the literature to achieve this goal, ranging from the invasive measurement of
brain potentials at single neurons to the non-invasive measurement of large-scale brain activity.
Some of these methods are reviewed in this section, starting from the non-invasive EEG method
which allows for measurements of electric potentials at large spatial scales, followed by the
electrocorticogram (ECoG) and the microelectrode arrays, which allow for measurement of
Signal Acquisition
Signal Processing
Classification
Application
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potentials at smaller spatial scales. Since in this work we are using the non-invasive EEG method,
we are not covering the invasive methods in much detail. The different methods are compared in
terms of spatial and temporal resolution, invasiveness versus. non-invasiveness, and in terms of
complexity of the devices and equipments used for performing the measurements.
2.2.1 Electroencephalography (EEG)
The EEG is one of the most widely used noninvasive techniques for recording electrical brain
activity. Since its discovery by Hans Berger [13], the EEG has been employed to answer many
different questions about the functionality of the human brain and has served as a diagnostic
mechanism in clinical practice. EEG signals are recorded with small silver/silver chloride sensors
or electrodes with a radius of about 5 mm, placed on the scalp at standard positions (see Figure
2.2) [45]. Conductive gel or saltwater is used to improve the conductivity between scalp and
electrodes. To fasten the electrodes to the scalp, often an electrode cap is used (Some new sensors
such as Emotiv Epoc [26] do not use caps. Instead the electrodes are attached to a headset). EEG
signals are always recorded with respect to some reference electrodes, i.e. EEG signals are small
potential differences (0 - 100 μV [45]) between electrodes placed at different positions on the
scalp. EEG signals are related to information processing in the brain and generated by brain
neurons ([51 and 52] can be referred to for more information about EEG signals generation). In
other words, the signals measured with the EEG are thought to be mainly an effect of information
processing at pyramidal neurons located in the cerebral cortex of the scalp [51].
Because of the volume conduction in the cerebrospinal fluid, scalp, and skull, signals
from a local collection of neurons spread to distant electrodes. The potentials caused by the
activity of a local collection of neurons also spread to far electrodes [64]. The effect of the tissue
barrier between electrodes and neurons is practically invisible in the EEG. Thus, the EEG is a
global measurement of brain activities.
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Figure 2.2: Electrode placement according to the 10-20 international system. Odd numbers indicate
electrodes located on the left side of the head. Even numbers indicate electrodes located on the right side of
the head. Capital letters are used to reference each cortical zone, namely frontal (F), central (C), parietal
(P),temporal (T), and occipital (O). Fp and A letters stand for frontal pole and auricular. The designation
10-20 comes from the percentage ratio of the inter-electrode distances with respect to the nasion-inion
distance [51].
As a result, it is difficult to use only the EEG to infer the activity of small brain regions,
let alone the activity of single brain neurons [64].
In addition to the effects of volume conduction, the analysis of the EEG is further
complicated due to the presence of different artifacts. These artifacts can be produced by
physiological or non-physiological sources [1, 78 and 79]. Eye movements and eye blinks,
muscle activities, heart activities, and slow potential drifts due to transpiration are some of the
physiological sources for artifacts. Non-physiological sources for artifacts may include main
power supply line noise (50 Hz or 60 Hz), noise generated by the EEG amplifiers, and noise
generated by unexpected changes in the setup of the electrode-scalp interface. Artifacts usually
have much larger amplitudes than the signals of interest. Therefore, artifact removal and filtering
procedures have to be carried out before any EEG signals analysis can be applied [79]. In spite of
the above mentioned EEG drawbacks, the EEG remains one of the most efficient and interesting
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methods for measuring brain signals. It has been used in different BCI systems. Besides the use in
BCIs, there are several other clinical and research applications of the EEG. These may include
research on different human sleep stages, coma outcome prognosis, epilepsy monitoring, and
many other research topics [79].
2.2.2 Invasive Methods
As mentioned before, since in this thesis we are using the non-invasive EEG technique to acquire
brain signals, we only give some brief details about two methods of invasive brain signal
acquiring techniques.
Electrocorticogram (ECoG)
The ECoG is an invasive technique for recording brain electrical potentials. This technique
requires a surgical procedure. In the procedure, an array of electrodes, typically an 8×8 grid, is
placed on the cortex surface. After the implantation, signals which are generated by the same
mechanisms as the EEG can be captured or acquired. However, effects of volume conduction are
less visible in the ECoG than in the EEG, i.e. the signals are less spatially unclear than EEG
signals. More advantages are that ECoG signals are hardly defaced with muscle or eye artifacts
and that activity in frequencies up to about 100 Hz can be easily observed [53].
Due to the above mentioned advantages, ECoG signals have become reasonably
interesting in the BCI community. Different experiments have been performed, mainly with
epilepsy patients by implanting ECoG arrays over a period of time (the period differs from one
experiment to another) for localization of epileptic foci or for pre-surgical monitoring reasons
[53]. The experiments have shown that patients can quickly learn how to accurately control their
ECoG signals through motor imagery [33], motor and speech imagery [27], or in other different
imageries. This makes ECoG an interesting alternative method to the EEG; however tests with
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severely handicapped users and research on the long term tissue compatibilities of ECoG have not
been performed to validate the results.
Microelectrode Arrays
Microelectrode arrays are a method for recording brain activity from individual neurons or from
small or collection groups of neurons. As for ECoG, brain-surgery is necessary before signals can
be measured. The difference to ECoG is that electrodes are implanted in the cortex, i.e. the
cortical tissue is penetrated by needle-like electrodes. A typical microelectrode array has a size of
approximately 5×5 mm and contains around 100 electrodes, which can penetrate the cortex to a
depth of several millimeters [53]. Because of the brain surgery and the invasive procedure that is
required to record brain signals, microelectrode arrays have only been mainly tested in animal
models (for example rhesus monkeys).
Compared with other technologies for measuring brain activity, the advantages of
microelectrode arrays are that signals are captured at high spatial resolution, similarly to ECoG,
and that makes the activity of single neurons can be captured. Capturing the neurons activities in
the motor areas of the brain allows for complicated applications such as real-time 3D control of a
robotic arm [21] which are difficult to be realized using other measurement technologies. The
disadvantage of microelectrode arrays is that brain-surgery is required before signals can be
acquired. During the surgery, an infection risk exists. Also, the reaction of brain tissue to the
implanted electrodes array is not well defined [21]. Due to the death of neurons in the vicinity of
the microelectrodes, signal quality decreases over time and data can only be recorded for a period
of several months [53].
In spite of the problems arising from the invasive nature of the measurements,
microelectrode arrays are, together with the EEG, one of the most often used techniques in BCI
researches.
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2.3 Neurophysiological Signals
An ideal BCI system would directly infer every wish or intention of its user and perform the
corresponding action. However, it is very difficult in BCIs to clearly define how wishes or
intentions are related to neurophysiological signals. As a result, it is practically impossible to
detect the intentions and wishes of a user from his brain activity. Mainly for this reason,
nowadays BCI systems achieve only a much less aspiring goal, specifically the discrimination of
two to five different categories of neurophysiological signals, or the mapping of
neurophysiological signals to continuous 1D, 2D, or 3D movements.
Frequency bands that include most of the useful neurophysiological signals information are
classified as shown in table-2.1.
Band
Frequency (Hz)
Delta
1-4
Theta
4-7
Alpha
7-13
Beta
13-30
Gamma
30+
Table 2.1: Neurophysiological signals frequency bands [45]
To allow for discrimination of different neurophysiological signals or for mapping of
such signals to movements or actions, users have to maintain conscious control over their brain
activity. Basically, two different approaches exist to achieve this [45]. In the first approach
subjects look at a set of stimuli displayed by the BCI system and can control their brain activity
by focusing on one specific stimulus. The changes in the neurophysiological signals resulting
from distinguishing and processing of stimuli are called event-related potentials (ERPs) and are
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