EEG Signal Processing and Machine Learning. Saeid Sanei

a balance of excitatory and inhibitory interactions within and between populations of neurons. Although it is well established that the EEG (or MEG) signals result mainly from extracellular current flow, associated with summed post‐synaptic potentials in synchronously activated and vertically oriented neurons, the exact neurophysiological mechanisms resulting in such synchronization to a given frequency band, remain obscure.

1.6 The Brain as a Network

      Networks are already an integral part of human daily social, business, and intellectual life. Networking science is appealing to the field of neuroscience as the brain function stems from the communication and signalling between the neurons. The measured EEG amplitudes probed at each electrode have been the main parameter in evaluating brain function. Synchrony between left and right brain lobes gives further insight into detection of abnormalities such as mental fatigue and dementia and is associated with many other brain states such as emotions, as stated in the next chapter. The synchrony is often measured in the frequency domain where the variations in frequency and phase, corresponding to the time delay between the lobes, can be easily measured. More generally, recent developments in network science, however, have created a new direction in the study of brain normal and abnormal functions.

      Although the fundamental concepts in network science originated from mathematics [28] and are used mostly in communications, a number of well established approaches, such as autoregressive modelling, have been used in characterizing the brain functional connectivity from the multichannel EEG. In addition, graph theory has become popular in designing effective classifiers which can segment the EEGs in time–space into the regions each encompassing a separate functionally connected brain region. In [29], a review of recent advances in neuroscience research in the specific area of brain connectivity as a potential biomarker of Alzheimer's disease with a focus on the application of graph theory can be studied.

      Later in this book, we derive equations for the graphs applied to EEG in a similar way to those of brain connectivity estimators. We also observe that machine learning techniques such as deep neural networks can be directly applied to graphs for recognition of the brain state.

1.7 Summary

Following some details on EEG history, this chapter overviews the neuronal level analysis of the brain function. It also provides some information about the head anatomy. Generation of EEG signals as the result of signalling at the dendrite‐dendrite or axon‐dendrite synapses and production of APs, is an important and fundamental concept covered in this chapter. It is also highlighted that normal brain rhythms, brain evoked responses, and brain connectivity are the outcomes of neuronal activities and should be treated differently to recognize the brain normal and abnormal states.

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2 EEG Waveforms

      2.1 Brain Rhythms

      Traditionally, many brain disorders are diagnosed by visual inspection of EEG signals. The