EEG Signal Processing and Machine Learning. Saeid Sanei

EEG Signal Processing and Machine Learning - Saeid Sanei


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      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.

      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.

      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.