Abstract

Electroencephalogram is often used in determining the brain waves that are recorded in form of EEG signals.  This waves are usually crucial in determining the brain’s activity level. Hence, a variety of these waves such as alpha, beta, delta and theta shows a variety of brain activity levels. Hence, in our experiment the brain waves that were recorded varied according to the activity which the subject was involved in. For instance, different EEG signals were recorded when the subjects brain’s waves were taken with their eyes closed or opened as well  as involved in various activities such as a mental arithmetic or after hyperventilation.

This  indicates that abnormalities in the brain functioning can be determined using this method which is easy to administer. Thus, it is often used in medical facilities to determine the presence of several brain abnormalities as well as the effects of some medications. However, people record different EEG signals according to their ages band medical conditions. However, this experiment effectively determined various brain waves according to the action the subjects were undertaking.

Literature review

Today, electroencephalogram is widely used in the medical practice and research in the correlation of the particular brain waves in different situations such as emotional states, sleep  phases, psychological profiles as well  as the ongoing mental processes. Therefore, from the electroencephalogram signals released it is possible to differentiate them in different categories such as alpha (?), beta (?), delta (?), as well as theta (?) waves together with the spikes in case of epilepsy (Cook et al. 1998). Therefore, the recorded electroencephalogram signal is very closely related to a person’s  level of consciousness. Hence, as the body activity increases, there is also a shift of the  EEG  to a higher frequency that is dominating and lower amplitude (Eimer & Kiss, 2010).

Thus, at times when the eyes are closed, the electroencephalogram is dominated by the alpha waves while when a person fall s asleep there is a decrease in the dominant EEG frequency. However, at certain phases of the sleep, referred to as the rapid eye movement (REM) sleep, dreams are experienced and the and there is an active movements of the eyes which is recorded (Fischbach & Dunning, 2009). Moreover, during the deep sleep there are large and slow deflections in the electroencephalogram resulting to the delta waves. Alternatively, the brain waves signals are directly proportional to the state of activity (?enbil et al. 2002).

However, this method is not only used in the detection of the mental conditions but also it can be used to detect the brain activity in different conditions. For instance, previous research has clearly demonstrated that electroencephalogram can be used in distinguishing various brain activity at different conditions (Eimer & Kiss, 2010). Therefore, the hypothesis aimed to be tested by this experiment is that “different physical activities of the body results to recording of varied brain waves by the electroencephalogram such as alpha, beta, delta and theta”. Hence, this will be the hypothesis to be tested in this experiment.

Materials

v  BIOPAC electrodes lead set (SS2LA/L)

v  BIOPAC electrode gel and abrasive pad or alcohol prep or skin cleaner

v  Three BIOPAC disposable vinyl electrodes per person

v  Lyra swim cap or supportive wrap

v  Lab table or cot and pillow

v  Computer system

v  BIOPAC Student Lab System

Results Table

References

Cook, I.A., O’Hara, R., Uijtdehaage, S.H.J., Mandelkern, M. & Leuchter, A.F. (1998). Assessing the accuracy of topographic EEG mapping for determining local brain function. Electroencephalography and Clinical Neurophysiology, 107(6), 408-414.

Eimer, M., & Kiss, M. (2010). An electrophysiological measure of access to representations in visual working memory. Psychophysiology, 47(1), 197-200.

Fischbach, F.T. & Dunning, M.B. eds. (2009). Manual of Laboratory and Diagnostic Tests, 8th ed. Philadelphia: Lippincott Williams and Wilkins.

Kaiser, D. A. (2006). ELECTROENCEPHALOGRAPHY. Journal of Neurotherapy, 10(4), 75-79.

Pagana, K.D & Pagana, T.J. (2010). Mosby’s Manual of Diagnostic and Laboratory Tests, 4th ed. St. Louis: Mosby Elsevier.

?enbil, N., Sonel, B., Aydin, Ö., & Gürer, Y. (2002). Epileptic and non-epileptic cerebral palsy: EEG and cranial imaging findings. Brain & Development, 24(3), 166.

Wackermann, J. & Matouek, M. (1998). From the ‘EEG age’ to a rational scale of brain electric maturation. Electroencephalography and Clinical Neurophysiology, 107(6), 415-421.