Electroencephalogram (EEG) measures the neuronal activities in the form of electric currents that are generated due to the synchronized activity by a group of specialized pyramidal cells inside the brain. The study presents a brief comparison of various functional neuroimaging techniques, revealing the excellent neuroimaging capabilities of EEG signals such as high temporal resolution, inexpensiveness, portability, and non-invasiveness as compared to the other techniques such as positron emission tomography, magnetoencephalogram, functional magnetic resonance imaging, and transcranial magnetic stimulation. Different types of frequency bands associated with the brain signals are also being summarized. The main purpose of this literature survey is to cover the maximum possible applications of EEG signals based on computer-aided technologies, ranging from the diagnosis of various neurological disorders such as epilepsy, major depressive disorder, alcohol use disorder, and dementia to the monitoring of other applications such as motor imagery, identity authentication, emotion recognition, sleep stage classification, eye state detection, and drowsiness monitoring. After reviewing them, the comparative analysis of the publicly available EEG datasets and other local data acquisition methods, preprocessing techniques, feature extraction methods, and the result analysis through the classification models and statistical tests has been presented. Then the research gaps and future directions in the present studies have been summarized with the aim to inspire the readers to explore more opportunities on the current topic. Finally, the survey has been completed with the brief description about the studies exploring the fusion of brain signals from multiple modalities.

脑电图（EEG）以电流形式测量神经元活动，该电流是由于大脑内部一组特殊的锥体细胞的同步活动而产生的。这项研究对各种功能性神经影像技术进行了简要比较，揭示了与其他技术（例如正电子发射断层扫描，脑磁图，功能性磁学）相比，EEG信号具有出色的神经影像功能，例如高时间分辨率，廉价，便携性和无创性共振成像和经颅磁刺激。与脑信号相关的不同类型的频带也正在被总结。本文献调查的主要目的是涵盖基于计算机辅助技术的脑电信号的最大可能应用，从诊断各种神经系统疾病（例如癫痫，重度抑郁症，饮酒障碍和痴呆症）到监视其他应用程序（例如运动图像，身份认证，情绪识别，睡眠阶段分类，眼神状态检测和嗜睡监测） 。在对它们进行审查之后，对公开的EEG数据集和其他本地数据获取方法，预处理技术，特征提取方法以及通过分类模型和统计检验的结果分析进行了比较分析。然后总结了本研究的研究差距和未来方向，以激发读者在当前主题上探索更多机会。最后，