EEG Signal Processing and Machine Learning. Saeid Sanei
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4 Fundamentals of EEG Signal Processing
4.1 Introduction
Electroencephalography (EEG) signals are the signatures of neural activities and generally are the integrals of active potentials which elicit from the brain with different latencies and populations around each time instant. They are captured by multiple‐electrode EEG machines either from inside the brain, over the cortex under the skull, or in the majority of applications, certain locations over the scalp. The EEG file formats are different for different recording machines but nowadays they can be easily read or converted by conventional software. The signals are normally presented in the time domain, however, many new EEG machines are capable of applying simple signal processing tools such as the Fourier transform to perform frequency analysis and equipped with some imaging tools to visualize EEG topographies (maps of the brain activities in the spatial domain).
There have been many algorithms developed so far for processing EEG signals. The operations include, but are not limited to, time‐domain analysis, frequency‐domain analysis, spatial‐domain analysis, and multiway processing. Also, several algorithms have been developed to visualize the brain activity from images reconstructed from only the EEGs namely topographs. Separation of the desired sources from the multisensor EEGs has been another research area. This can later lead to the detection of brain abnormalities such as epilepsy and the sources related to various physical and mental activities. In Chapter