EEG Signal Processing and Machine Learning. Saeid Sanei

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Название EEG Signal Processing and Machine Learning
Автор произведения Saeid Sanei
Жанр Программы
Серия
Издательство Программы
Год выпуска 0
isbn 9781119386933



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rel="nofollow" href="#ulink_ade8c670-0526-5827-9acf-9e8cfe981329">Figure 2.9.

      Later in Chapter 11 of this book we will show the application of this recording modality in detection of interictal epileptiform discharges (IEDs) from the hippocampus.

      With the advances in microelectronics and microsensor designs, a new type of microsensor array insertable into the brain blood vessels within the motor cortex without any open surgery has been designed. The sensor array, called Stentrode™ is a small metallic mesh tube (stent), with not more than 4 mm diameter and with electrode contacts (small metal discs) within the stent structure.

Photo depicts electrocorticography. Schematic illustration of foramen ovale holes within facial skeleton. Schematic illustration of foramen ovale electrodes.

      2.2.4 Conditioning the Signals

Schematic illustration of a 4-mm diameter Stentrode with electrode contacts within the stent structure.

      In the following sections we highlight the most popular changes in EEG measurements which correlate with physiological and mental abnormalities in the brain.

Schematic illustration of a set of normal EEG signals affected by an eye-blinking artefact.

      Conversely, there are cases where the subject has sleep disorder. These include obstructive sleep apnoea (OSA), insomnia (including parainsomnia and hyperinsomnia), REM sleep behaviour disorder, circadian rhythm sleep disorders, non‐24‐hour sleep–wake disorder, periodic limb movement disorder (PLMD), shift work sleep disorder, narcolepsy. Sleep disorders need to be diagnosed and treated.

      Such disorders are often detected, monitored, or diagnosed through other recording modalities such as heartrate and respiration. Nevertheless, the use of sleep EEG in detection or monitoring of abnormal sleep has been a topic of research recently. Some methods are discussed in the related chapter of this book.

Schematic illustration of a multichannel EEG set with the clear appearance of ECG signals over the electrodes in the occipital region.

      Mental fatigue is often the result of prolonged cognitive load and can negatively affect people over a short or long time and can cause failure of the brain in performing its regular tasks. Mental fatigue mainly affects the function of the brain network rather than changing the normal brain rhythms significantly [21, 22] In these studies it has been shown that there is less synchrony between the left and right brain lobes and less connectivity between various brain lobes when the brain is under mental fatigue.

      Mental fatigue also deteriorates the brain response to audio, visual, and other stimulations by attenuating and time shifting the ERPs. It has been shown that the amplitude of ERP (particularly P3b which is a subcomponent of P300,) reduces and the ERP latency, particularly for P3b, increases with the increase in mental fatigue [23–25].

      A chapter of this book is devoted to detailed analysis of the EEG for under fatigue brains.