A brief survey on human activity recognition using motor imagery of EEG signals

Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR. The brain plays a key role in connecting human biology and psychology, influencing our everyday activities. Understanding human activity is crucial for improving health and well-being. This study focuses on how the brain communicates with the outside world through brain signals, particularly using a method called Motor Imagery (MI) in Brain-Computer Interface (BCI) systems. These systems analyze brain signals, known as Electroencephalograms (EEGs), to detect and interpret human movements. Human Activity Recognition (HAR) is a field that identifies physical activities based on signals from the body. In this survey, we review 50 studies that use EEG signals to recognize human activities. We explore the different techniques that have been developed to tackle HAR and examine their performance based on factors like accuracy, speed, and tools used. These techniques are mainly classified into two categories: Machine Learning (ML) and Deep Learning (DL). This survey highlights the challenges that HAR systems face in recognizing activities from EEG signals. It also identifies gaps in the research and suggests areas for improvement to create more effective systems in the future. Ultimately, this research aims to help develop better technology for understanding human activity through brain signals, which could have important applications in health care and other areas.