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Automatic EEG-based Alertness Classification using Sparse Representation and Dictionary Learning

Automation of human alertness identification has been widely investigated in recent decades. Many applications can benefit from automatic alertness state identification, such as driver fatigue detection, monotonous task workers' vigilance detection and sleep studies in the medical field.  Many researchers have tried to exploit different types of behavioural aspects in vigilance detection, such as eye movement, head position and facial expression. On the other hand, some biomedical signals like ECG, EEG and heart rhythm are also exploited; however,  there is a consensus of the superiority of EEG signal in alertness classification due to its close relation with different human vigilant states. In this paper, we propose an automatic method for vigilance detection using a single EEG channel along with sparse representation and dictionary learning. We used Discrete Wavelet Packet Transform to extract the features related to different human vigilance states. We use well-known other classifiers to compare the performance of our proposed method. Results of classification with sparse representation and dictionary learning produced better accuracy results than the other methods.