Evolving EEG signal processing techniques in the age of artificial intelligence

1 CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China 2 Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China 3 School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518000, Guangdong, China Electroencephalogram (EEG) is an important technique for measuring population‐level electrical activity arising from the human brain. Due to its exquisite temporal sensitivity and implementation simplicity, EEG has been widely applied to dynamically evaluate the function of the brain. Being responded to a specific sensory, cognitive, or motor event, the changes of EEG signals give rise to evoked potentials (EPs) and event‐related potentials (ERPs), which are highly associated with different brain functions, e.g., perception, emotion, and cognition. These advances make the EEG technique popularly used in various basic and clinical applications. To make full use of the EEG technique, signal processing and machine learning methods are crucial in the extraction of information for better understan‐ ding the cerebral functioning. Particularly, in this age of artificial intelligence (AI), rapidly developed AI methods, such as convolutional neural networks and recurrent neural networks, have been applied to EEG signals and have achieved promising performance in many real applications. As a consequence, the field of EEG signal processing has undergone significant growth in the last few years, and the scope and range of practical applications of EEG, such as brain–computer interface (BCI), are steadily increasing. For this reason, the special issue aims to provide a collection of papers discussing the conceptual and methodological innovations as well as practical applications of the EEG techniques. This special session has included seven review papers contributed by experts in this interdisciplinary field, and all authors have worked in the fields of EEG processing methods and applications for many years. First of all, Li [1] shared his insightful and constructive thoughts on EEG signal analysis and classification. Specifically, he focused on several important and emerging topics in EEG processing, such as brain connectivity, tensor decomposition, multi‐modality, deep learning, big data, and naturalistic experiments. These topics, particularly those AI‐related topics, are both crucial and promising for the future advancement of EEG signal analysis and classification. Next, this special issue presented several papers concerning the applications of EEG in psychology, emotion recognition, and BCI. One important and conventional application field of EEG is psychology, in which EEG has been extensively used to decode the psychological Address correspondence to Li Hu, huli@psych.ac.cn; and Zhiguo Zhang, zgzhang@szu.edu.cn