Abstract
In this project, an algorithm, together with a testing module, has been developed to classify the handedness of a person. Electroencephalogram (EEG) signals at the homologous occipital region were captured when test subjects were asked to rest or expose to some graphical stimulus. Handedness of the person can be determined from the EEG data captured and further confirmed using a simple game as testing module. EEG signals are obtained from three locations, namely A1, O1 and O2. The signals are processed using wavelet transform to classify the signal into four different frequency bands, alpha, beta, delta, and theta, before it is used to find out the mean EEG coherence (MEC). Generally left-handed person has higher MEC, which means that there are more connections between the left and right hemisphere of cerebrums through the corpus callosum (CC).
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