EEG Signal Processing and Machine Learning. Saeid Sanei
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3 EEG Signal Modelling
3.1 Introduction
Generation of electrical potentials or magnetic fields, measurable from the brain, is due to a nonlinear sum/distribution of electrochemical active potentials within all the neurons involved in cognitive or movement‐related processes. An accurate model that can link the chemical processes within corresponding neurons generating the active potentials is hard to achieve due to the involvement of various chemicals and chemical processes. Some models, however, have been introduced since 1950s.
Neurons may be considered as signal converters. The brain is a complicated network of a tremendous number of neurons. Figure 3.1a shows a small network of three neurons. Each axon is extended from a soma which is the main neuron body. Neurons transmit and exchange electric signals called action potentials (APs) or spikes, among each other. Neurons receive the spikes at a synapse. Then, the electric signals or information is transmitted in the direction from a dendrite to an axon. Figure 3.1b illustrates the waveform of APs which have an amplitude of approximately 100 mV in the human brain.
Typical neurons do not generate any spikes without input signals which often come from other neurons. A sufficiently large input pulse causes a neuron to generate an output spike whereas no output spike is generated by a small input. Therefore, a neuron possesses a threshold or all‐or‐none characteristic. There is a special period or timing called the refractory period (the timing of the downstroke of the AP) in which the neuron cannot produce any output spike even though a sufficient amount of inputs is applied to the neuron. Hence, a neuron may be considered as a device which transforms or converts the input spike train to another spike train where each output spike (AP), as we will see later, is the integral of a number of input spikes.
3.2 Physiological Modelling of EEG Generation
The popular physiological models aim to best simulate the coupling between two or more neurons. In [1] three models for generation of brain potentials have been introduced and compared.