A Survey for Lie Detection Methodology Using EEG Signal Processing

Abstract

Electroencephalography (EEG) is a hot topic all around the world. EEG signals, a series of measurements taken using electrodes on the scalp, can indicate brain activity. They are more private, sensitive, and difficult to steal and recreate. EEG data are increasingly commonly employed in diagnosing brain illnesses and the field of Brain-Computer interfaces thanks to advancements in biomedical signal processing techniques (BCI). BCI is a brain-computer interface that uses electrical impulses from the brain to communicate. EEG signals are used to interpret the electrical activity of the brain. The electrical activity of the brain is read using EEG signals. Many studies are being conducted in many fields to benefit from this technology. Studying EEG gives a solid understanding of how brain signals function in various moods and activities. Lie detection is a new technology that is being used to combat crime. Traditionally, this has been accomplished by language analysis, face and body movement recognition, training observation, and voice stress analysis. EEG analysis provides a better understanding of brain activity thanks to advances in cognitive science and neuroscience. Deception identification has become a severe issue as crime has increased. Previous surveys have discussed numerous approaches supported with experimental outcomes and compared them. This paper addressed each direction and offered different sets of characteristics and electrodes, EEG signal preprocessing, feature extraction, feature selection, and classification. Also, it discusses many methods which may need some adjustments at each phase of brain signal processing for lie detection.