  Electroencephalography is frequently used for diagnostic evaluation of
various brain-related disorders due to its excellent resolution, non-invasive
nature and low cost. However, manual analysis of EEG signals could be strenuous
and a time-consuming process for experts. It requires long training time for
physicians to develop expertise in it and additionally experts have low
inter-rater agreement (IRA) among themselves. Therefore, many Computer Aided
Diagnostic (CAD) based studies have considered the automation of interpreting
EEG signals to alleviate the workload and support the final diagnosis. In this
paper, we present an automatic binary classification framework for brain
signals in multichannel EEG recordings. We propose to use Wavelet Packet
Decomposition (WPD) techniques to decompose the EEG signals into frequency
sub-bands and extract a set of statistical features from each of the selected
coefficients. Moreover, we propose a novel method to reduce the dimension of
the feature space without compromising the quality of the extracted features.
The extracted features are classified using different Gradient Boosting
Decision Tree (GBDT) based classification frameworks, which are CatBoost,
XGBoost and LightGBM. We used Temple University Hospital EEG Abnormal Corpus
V2.0.0 to test our proposed technique. We found that CatBoost classifier
achieves the binary classification accuracy of 87.68%, and outperforms
state-of-the-art techniques on the same dataset by more than 1% in accuracy and
more than 3% in sensitivity. The obtained results in this research provide
important insights into the usefulness of WPD feature extraction and GBDT
classifiers for EEG classification.
