Brain-computer interfaces (BCIs) development is closely related to physics. In this paper, we review the physical principles of BCIs, and underlying novel approaches for registration, analysis, and control of brain activity. We analyse recent advances in BCI studies focusing on their applications for (i) controlling the movement of robots and exoskeletons, (ii) revealing and preventing brain pathologies, (iii) assessing and controlling psychophysiological states, and (iv) monitoring and controlling normal and pathological cognitive activity.

After introducing the topic to the reader in chapter 1, in chapter 2 we consider the BCI as a hardware/software communication system that allows interaction of humans or animals with their surroundings without the involvement of peripheral nerves and muscles, using control signals generated from brain cerebral activity. Classifying BCIs into three main types (active, reactive and passive), we describe their functional models and neuroimaging methods, as well as novel techniques for signal enhancement and artifact recognition and avoidance, to improve BCI performance in real time. In this chapter, we also review different BCI applications, including communications, external device control, movement control, neuroprostheses, and assessment of human psychophysiological states.

In chapters 3 and 4 we talk about the most common techniques for the analysis and classification of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Special attention is paid to modern technology based on machine learning and reservoir computing. Chapters 5–8 are devoted to main results on the creation and application of BCIs based on invasive and noninvasive EEG recordings. First, in chapter 5 we consider neurointerfaces for controlling the movement of robots and exoskeletons. Then, in chapter 6 we describe BCIs for diagnosis and control of pathological brain activity, in particular, epilepsy. We also discuss the results on the development of invasive BCIs for predicting and mitigating absence epileptic seizures. After that, in section 7 we focus on passive neurointerfaces for assessing and controlling a person’s psychophysiological states and cognitive activity. Chapter 8 is devoted to optogenetic brain interfaces using photostimulation to deliver intervention to specific cell types. We outline the basic principles of optogenetic neurocontrol and extracellular electrophysiology recording. At the end of this chapter, we describe the state-of-the-art of miniaturized closed-loop optogenetic devices to control normal and pathological brain activities.

In chapter 9 we discuss the new emerging technological trend in the BCI development which consists in using neurointerfaces to improve the interaction between people, so-called brain-to-brain interfaces (BBIs). Such interfaces can increase the efficiency of collaborative processes when working in a group. We propose a BBI which distributes a cognitive load among all the team members working on a common task. This BBI allows sharing the workload among the participants according to their current cognitive performance, estimated from their electrical brain activity. The novel results of the brain-to-brain interaction are promising for the development of a new generation of communication systems based on the neurophysiological brain activity of interacting persons, where the BBI estimates the physical conditions of each partner and adapts the assigned task accordingly.

Finally, in chapter 10 we trace the main historical epochs in BCI development and applications and highlight possible future directions for this research area, including hybrid BCIs.

脑机接口（BCI）的开发与物理学密切相关。在本文中，我们回顾了BCI的物理原理，以及用于注册，分析和控制脑部活动的潜在新方法。我们分析BCI研究的最新进展，重点关注其在以下方面的应用：（i）控制机器人和外骨骼的运动；（ii）揭示和预防脑部疾病；（iii）评估和控制心理生理状态；（iv）监测和控制正常和病理认知活动。

在第1章向读者介绍了该主题之后，在第2章中，我们将BCI视为一种硬件/软件通信系统，该系统可以使用人或动物与周围环境的交互作用，而无需使用周围神经和肌肉的参与，而是使用大脑产生的控制信号脑活动。将BCI分为三种主要类型（主动，被动和被动），我们描述了它们的功能模型和神经影像学方法，以及信号增强和伪影识别和避免的新技术，以实时改善BCI性能。在本章中，我们还将回顾不同的BCI应用，包括通信，外部设备控制，运动控制，神经假体以及对人类心理生理状态的评估。

在第3章和第4章中，我们讨论了用于脑电图（EEG）和磁脑电图（MEG）数据分析和分类的最常用技术。基于机器学习和储层计算的现代技术特别受关注。第5-8章专门介绍基于有创和无创EEG记录创建和应用BCI的主要结果。首先，在第5章中，我们将考虑用于控制机器人和骨骼外骨骼运动的神经接口。然后，在第6章中，我们将描述用于诊断和控制病理性大脑活动（尤其是癫痫）的BCI。我们还讨论了用于预测和缓解失神癫痫发作的侵入性BCI的发展结果。之后，在第7节中，我们将重点放在被动神经接口上，以评估和控制一个人的心理生理状态和认知活动。第8章专门介绍了使用光刺激对特定细胞类型进行干预的光遗传性大脑界面。我们概述了光遗传神经控制和细胞外电生理学记录的基本原理。在本章的最后，我们描述了用于控制正常和病理性大脑活动的微型闭环光遗传学设备的最新技术。

在第9章中，我们讨论了BCI发展中的新兴技术趋势，其中包括使用神经接口来改善人与人之间的交互，即所谓的脑对脑接口（BBI）。当在团队中工作时，此类界面可以提高协作过程的效率。我们提出了一个BBI，该BBI可以在从事同一任务的所有团队成员之间分配认知负荷。该BBI允许根据参与者的当前认知表现（根据其脑电活动估计）在参与者之间分担工作量。脑脑交互的新结果有望为基于交互人的神经生理学大脑活动的新一代通信系统的发展提供希望，

最后，在第10章中，我们追溯了BCI开发和应用的主要历史时期，并重点介绍了该研究领域的未来方向，包括混合BCI。