Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine

Cognitive load level identification is an interesting challenge in the field of brain-computer-interface. The sole objective of this work is to classify different cognitive load levels from multichannel electroencephalogram (EEG) which is computationally though-provoking task This proposed work utilized discrete wavelet transform (DWT) to decompose the EEG signal for extracting the non-stationary features of task-wise EEG signals. Furthermore, a support vector machine (SVM) implemented to classify the task from the DWT-based extracted features.. The proposed methodology has been implemented on a renowned EEG dataset that captured three levels of cognitive load from the n-back test. In this work, two different approaches: r) Low vs High cognitive load (0-back vs [2-back+3-back]) and ii) Low vs Medium vs High (0-back vs 2-back vs 3-back) are investigated for the performance measurement. The linear SVM achieved the highest average classification accuracy that is 77.20 +/- 6.63 and 87.89 +/- 7.3 for 3-class and 2-class approaches, respectively.