Saeid Sanei - EEG Signal Processing and Machine Learning

The CNS stimulants increase the alpha and beta wave activities and reduce the amplitude and the amount of slow waves and background EEGs [69].

The effect of many other drugs especially antiepileptic drugs is investigated and new achievements are published frequently. One of the significant changes of the EEG of epileptic patients with valproic acid consists of reduction or even disappearance of generalized spikes along with seizure reduction. Lamotrigine is another antiepileptic agent that blocks voltage‐gated sodium channels thereby preventing excitatory transmitter glutamate release. With the intake of lamotrigine a widespread EEG attenuation occurs [69].

Penicillin if administered in high dosage may produce jerks, generalized seizures, or even status epilepticus [69].

In this chapter the formation of EEG signals have been briefly explained. The conventional measurement setups for EEG recording and the brain rhythms present in normal or abnormal EEGs have also been described. In addition, the effects of popular brain abnormalities such as mental diseases, ageing, and epileptic and nonepileptic attacks have been pointed out. Despite the known neurological, physiological, pathological, and mental abnormalities of the brain mentioned in this chapter, there are many other brain disorders and dysfunctions which may or may not manifest some kinds of abnormalities in the related EEG signals.

Sleep, fatigue, ageing, emotions and many other states of the human body can directly or indirectly manifest themselves in the EEG patterns. Neurodevelopmental disorders, particularly those with human behaviour have become attractive areas of research as they are associated with child personality development.

Degenerative disorders of the CNS [70, 71] such as a variety of lysosomal disorders, several peroxisomal disorders, a number of mitochondrial disorders, inborn disturbances of the urea cycle, many aminoacidurias, and other metabolic and degenerative diseases as well as chromosomal aberrations have to be evaluated and their symptoms correlated with the changes in the EEG patterns. The similarities and differences within the EEGs of these diseases have to be well understood. Conversely, the developed mathematical algorithms need to take the clinical observations and findings into account to further enhance the outcome of such processing. Although a number of technical methods have been well established for the processing of the EEGs with relation to the above abnormalities, there is still a long way to go and many questions to be answered.

The following chapters of this book introduce new digital signal processing and machine learning techniques employed mainly for analysis of EEG signals followed by a number of examples in the applications of such methods.

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21 21 Jarchi, D. and Sanei, S. (2010). Mental fatigue analysis by measuring synchronization of brain rhythms incorporating enhanced empirical mode decomposition. Proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP). Elba, Italy.

22 22 Jarchi, D. and Sanei, S. (2010). A novel method for analysis of mental fatigue from normal brain rhythms. Proceedings of the 17th European Signal Processing Conference, EUSIPCO. Denmark.

23 23 Jarchi, D., Sanei, S., and Lorist, M.M. (2011). Coupled particle filtering: a new approach for P300‐based analysis of mental fatigue. Journal of Biomedical Signal Processing and Control 6 (2): 175–185.

24 24 Jarchi, D., Makkiabadi, B., and Sanei, S. (2009). Estimation of trial to trial variability of P300 subcomponents by coupled Rao‐Blackwellised particle filtering. Proceedings of the IEEE Workshop on Statistical Signal Processing, SSP2009. Cardiff, UK.

25 25 Jarchi, D., Makkiabadi, B., and Sanei, S. (2009). Separating and tracking ERP subcomponents using constrained particle filter. Proceedings of the 16th International Conference on Digital Signal Processing, DSP2009, Greece.

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