脑机接口中的信号处理与机器学习技术

信号处理

机器学习

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"Signal Processing and Machine Learning for Brain--Machine Interfaces" 是一本由英国工程技术学会（The Institution of Engineering and Technology）出版的书籍，主要探讨了在脑机接口领域中信号处理和机器学习的应用。这本书可能涵盖了从理论到实践的多个方面，旨在帮助读者理解如何利用这两种技术来构建有效的脑机交互系统。在脑机接口（Brain-Machine Interface, BMI）的研究中，信号处理是至关重要的一个环节。它涉及从大脑活动中提取有意义的信息，这通常需要对神经信号进行预处理、特征提取和噪声消除。例如，使用滤波器去除无关的背景噪声，通过谱分析了解大脑活动的频率模式，或者利用时频分析揭示大脑活动的时间动态。这些处理方法有助于提高信号的质量和解析度，使得我们可以更准确地解读大脑的意图。机器学习在脑机接口中的应用则体现在训练模型来理解和预测大脑活动。这些模型可以基于监督学习、无监督学习或强化学习，用于识别特定的脑电图（EEG）模式，比如与特定运动相关的模式。支持向量机（SVM）、神经网络（如深度学习结构）和聚类算法等都是常见的机器学习工具，它们能够从大量的脑电信号中学习规律，并随着时间的推移不断优化其性能。该书可能还讨论了如何结合信号处理和机器学习来改善BMI系统的性能，比如通过集成学习方法提高预测精度，或者使用迁移学习来减少新用户的学习曲线。此外，可能还涵盖了数据集的构建、实验设计以及评估指标，这些都是进行有效研究和开发的关键。该系列图书的其他卷涵盖了控制工程、自动化制造、温度测量与控制等多个领域，显示了作者和出版社对于工程和技术的广泛覆盖。本书的出版遵循严格的版权规定，并声明了作者的道德权利，同时也提醒读者在使用书中的信息和指导时应依靠自己的专业知识和判断，因为作者和出版社不承担任何因错误或遗漏造成的损失责任。 “Signal Processing and Machine Learning for Brain--Machine Interfaces”是一本深入探讨脑机接口技术中信号处理和机器学习相互作用的专业著作，对于从事相关研究和开发的工程师、科研人员以及对此领域感兴趣的读者来说，是一份宝贵的资源。

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Chapter 1

Brain–computer interfaces

and electroencephalogram: basics

and practical issues

Mahnaz Arvaneh

and Toshihisa Tanaka

Abstract

This chapter is a comprehensive overview of noninvasive brain–machine interface

(BMI). Though intended for nonspecialists, it contains some technical details and the

background materials for the rest of the book. After introducing the core components

of the BMI systems, this chapter describes various possibilities for brain activity

measurements. It then emphasizes on electroencephalogram (EEG), which will be

used as the source of the signals for BMI in the rest of the book. Next, possiblestandard

preprocessing algorithms commonly used in EEG-based BMIs are illustrated along

with the main categories of features extracted from EEG and used for classifications.

Finally, some possible applications of BMI are described.

1.1 Introduction

Over the last four decades, brain–computer interface (BCI) has attracted a lot of

attention, triggered by new scientific progress in understanding brain functions and

by impressive applications. The definition of BCI as quoted from the first BCI inter-

national meeting in 1999 is: “A brain–computer interface is a communication system

that does not depend on the brain’s normal output pathways of peripheral nerves and

muscles” [1]. Following this definition, a BCI system is supposed to work for com-

pletely paralyzed patients that are restricted to the use of brain activity signals and

cannot control their muscles. The idea of a BCI system is to exploit brain patterns

associated to mental activities (conscious or unconscious) in order to provide a means

of communication for patients that do not have enough control over their motor system

(muscles) to communicate in a normal way.

Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom

Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology,

Japan

2 Signal processing and machine learning for brain–machine interfaces

Before the BCI technology can reach to its full potential, there are many crit-

ical issues that have to be addressed. The main challenges include improvement

of signal acquisition techniques, development of feature extraction methods and

translation algorithms, development of the hardware, improvement of operational

protocols, and user training strategies [2–4]. In order to address these challenges,

many BCI approaches have been proposed. Even though there is no standard design

that represents a BCI, core components, techniques for measuring the brain activity,

and neurophysiological signals used to drive them are similar, and they are briefly

described in this chapter.

We would like to mention that BCI is nowadays also referred to as brain–machine

interface (BMI), which is an interface between the human brain and a machine. Two

terminologies, BMI and BCI, do not have clear difference. However, in general, BMI

has a wider concept than BCI. In the case of invasive recording of the brain activity,

BMI is more often used in the literature. Therefore, in the title of this book and in

the rest of this chapter, BMI is adopted. In the following, this chapter is devoted to a

comprehensive overview of noninvasive BMI. Technical details and the background

materials to understand the rest of the book are described. More precisely, the rest of

this chapter includes

●

the introduction of the core components of the BMI systems;

●

various possibilities for brain activity measurements;

●

the basic concepts of EEG;

●

standard preprocessing, feature extraction, and machine-learning algorithms

commonly used in EEG-based BMIs;

●

some possible applications of BMI.

1.2 Core components of a BMI system

The whole architecture of an online BMI system is summarized in Figure 1.1. As

shown in Figure 1.1, the BMI input is the brain signals carrying informative neu-

ral features. BMI outputs can be letter or icon selection on a computer screen, a

wheelchair control, neuroprosthesis, etc. [5–7]. Each BMI uses specific algorithms

to translate its input into command signals to control the output device. The core

components of a BMI system are as follows:

1. Data acquisition unit: This part is responsible for recording brain activities using

various types of sensors. After amplification and digitization, the recorded brain

signals serve as BMI inputs.

2. Preprocessing unit: This unit reduces noise and artifacts present in the brain

signals in order to enhance the relevant information hidden in the input signals.

3. Feature extraction:The feature extractor transforms the preprocessed signals into

feature values that correspond to the underlying neurological mechanism. These

features are employed by BMI for controlling the output device.

4. Classification unit: This part is responsible for identifying the intention of the

user from the extracted features.

4 Signal processing and machine learning for brain–machine interfaces

replacement. They might be harmful to the health of the subject too. Therefore, the

noninvasive techniques are preferred in the BMI systems than invasive measurement

techniques [9]. A number of noninvasive techniques explored in BMI applications

are briefly described here including electroencephalogram (EEG), positron emission

tomography (PET), magnetoencephalography (MEG), functional magnetic resonance

imaging (fMRI), and near-infrared spectroscopy (NIRS).

1.3.1 Electroencephalography

EEG [10] is the most widely used noninvasive technique for recording electrical

brain activity. EEG records the electrical signals generated by action potentials of

the neurons in the brain from the scalp using small metal electrodes. EEG is often

called scalp EEG to distinguish from noninvasive electrical recordings, which is called

intracranial EEG or electrocorticogram. The advantages of EEG are essentially the

relatively low cost, high temporal resolution, and the noninvasiveness. Low spatial

resolution due to volume conduction [11] and a high level of noise and variability in

the EEG signal [12] are some of the disadvantages [13]. EEG is used in a variety of

BMI systems.

1.3.2 Positron emission tomography

PET [14] indirectly measures metabolism on a cellular level by tracking injected

radioactive isotopes. It is based on the principle that in areas of increased activity the

metabolism is on a higher level, and more isotopes are supplied by the blood flow.

This knowledge can be used to determine which areas are generating activity. Good

spatial resolution is an advantage of PET. The really bad temporal resolution (about

2 min) is a distinct disadvantage. This is due to the fact that metabolism is a relatively

slow process. Moreover, ionizing radiation makes this method harmful for the human

body and thus unusable for daily applications like BMI.

1.3.3 Magnetoencephalography

MEG [15] directly measures the cortical magnetic fields produced by electrical cur-

rents. This technique is noninvasive and has good spatial and temporal resolutions.

However, the equipment is extremely expensive, and due to the very weak magnetic

fields, it requires a very impractical isolation/shielding room (cage of Faraday).

1.3.4 Functional magnetic resonance imaging

fMRI [16] provides information on brain metabolism by detecting changes in blood

oxygen level. It uses strong fluctuating magnetic field to measure the location of

dipoles. The advantages are the good spatial resolution and the noninvasiveness.

But the temporal resolution is poor (about 1 s), and the required equipment is

expensive.

Brain–computer interfaces and electroencephalogram 5

1.3.5 Near-infrared spectroscopy

Near-infrared light penetrates to sufficient depths of the head to allow functional

mapping of the cerebral cortex. Changes in tissue oxygenation cause the modulation

of absorption and the scattering of photons, which can be measured [17]. NIRS can

measure the attenuation changes due to cerebral hemodynamics that yield localized

blood volume and oxygenation changes. This response has a poor temporal resolution

(around 5–8 s). However, noninvasiveness, no interference, nonionizing, and not

requiring any gel are some advantages of this method.

1.3.6 Commonly used method in BMI—why EEG?

The ideal brain measurement method needs to have high temporal and spatial resolu-

tion. Moreover, it should be cheap, portable, and easy to use. This method does not

yet exist. The characteristics of the introduced signal acquisition techniques are sum-

marized in Table 1.1. Among the listed techniques EEG is by far the most commonly

used in BMI, due to its high temporal resolution and the ease of use. Furthermore,

EEG is also a relatively low-cost method, when compared to methods like PET, MEG,

or fMRI which require expensive equipment and skilled professionals to operate.

EEG signals are composed of different oscillations named rhythms [10]. These

rhythms can have distinct properties in terms of spatial and spectral localization. There

are six classical brain rhythms described in Table 1.2.

Table 1.1 Noninvasive techniques for measuring brain activity

Method Measured quantity Spatial res. Temporal res. Portable?

EEG Electric pot. −++Ye s

MEG Magnetic fields +++ No

fMRI Hemodynamic act. ++ − No

NIRS Hemodynamic act. −− − Ye s

−−:verylowto++: very high, Res.: Resolution, Pot.: Potential

Table 1.2 EEG rhythms

Rhythm Frequency Generally found

(Hz)

Delta 1–4 In adults during a deep sleep

Theta 4–7 During drowsiness, and in young children

Alpha 8–12 In healthy relaxed adults and with closed eyes, in occipital lobe

Mu 8–13 In motor and sensorimotor cortex, changes by doing motor tasks

Beta 13–30 In awaken and conscious people over the frontal and central regions,

also affected by the performing movements in the motor areas

Gamma 30–40 When deep concentration is obtained

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脑机接口中的信号处理与机器学习技术

Advances in Machine Learning and Signal Processing

Signal Processing and Machine Learning for Brain-Machine Interfaces 18

CMU的Bhiksha Raj 教授Machine Learning for Signal Processing的课件

write a python code for it

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