Feature generation of EEG data using wavelet analysis

Wavelet analysis is a modern method of time-frequency analysis that can be used to analyze EEG signals. There are several popular methods of generating wavelet-based features for the purposes of classification and brain modeling. These methods generate one feature per wavelet decomposition level, effectively averaging out the temporal information contained in the wavelet transform. This thesis proposes a method of generating features based on segments of the continuous wavelet transform and provides a Matlab software tool capable of generating features of EEG data using this and a number of other methods. The methods are then tested in an example study on attention networks in individuals with autism spectrum disorder (ASD). There is evidence of a selective attention abnormality in autism that is identified by the attention network task (ANT). The primary area of activation in the brain related to selective attention is the prefrontal cortex and anterior cingulate. The ANT task was given to a group of five participants diagnosed with ASD and a control group of five neuro-typical participants. The EEGs were recorded using a 64-channel EGI system and preprocessed using EEGLab. The Matlab software tool proposed herein was used to generate features of the data using coherence, conventional average power, wavelet-power, and time-segmented wavelet power. The results are examined by comparing the number of features that pass a t-test for each method. The time-averaged wavelet power method produced more significant features than conventional average power, and the time-segmented wavelet power method produced more features than the time-averaged wavelet-power method. As hypothesized, the prefrontal cortex and anterior cingulate were the most significant area of activation for the wavelet-based methods. The average values of the power features were larger in the autistic group, while the average values of coherence were larger in the controls group. The occipital lobe was also an area of significant difference between the autistic and controls groups but not within the groups, supporting evidence of hypersensitivity to visual stimuli in autistic individuals. While the time-averaged wavelet method produced a small number of significant features, the time-segmented wavelet method produced a much larger number of significant features that create a model of the unfolding nature of the processes of the brain.