Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

This paper describes a discrete wavelet transform-
based feature extraction scheme for the classification
of EEG signals. In this scheme, the discrete wavelet transform
is applied on EEG signals and the relative wavelet
energy is calculated in terms of detailed coefficients and the
approximation coefficients of the last decomposition level.
The extracted relative wavelet energy features are passed to
classifiers for the classification purpose. The EEG dataset
employed for the validation of the proposed method consisted
of two classes: (1) the EEG signals recorded during
the complex cognitive task—Raven’s advance progressive
metric test and (2) the EEG signals recorded in rest condition—
eyes open. The performance of four different classifiers
was evaluated with four performance measures, i.e.,
accuracy, sensitivity, specificity and precision values. The
accuracy was achieved above 98 % by the support vector
machine, multi-layer perceptron and the K-nearest neighbor
classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of
0.53–3.06 and 3.06–6.12 Hz, respectively. The findings of
this study demonstrated that the proposed feature extraction
approach has the potential to classify the EEG signals
recorded during a complex cognitive task by achieving a
high accuracy rate.