Cognitive workload classification: Towards generalization through innovative pipeline interface using HMM

Cognition is an important aspect to realize one’s potential and accelerate progress towards achieving a better quality of life. The cognitive workload is characterized by the occupancy of working memory during task performance. In spite of low spatial resolution, Electroencephalogram (EEG) continues to be a substantial tool in cognitive load research. The current methods use various time and frequency domain features for the assessment of cognitive load levels using EEG. The present work intends to model the temporal dynamics of the EEG signals by proposing a pipeline interface to classify cognitive workload under a continuous engagement/attention environment. The short segments of decomposed data are assumed to be stationary and are modeled using the autoregressive method. Later, fuzzy c-means maps the AR coefficients to low dimensional space. These clustered items are scaled further in the temporal domain by finding the interplay between the successive items through the most likely sequence of hidden states using the hidden Markov model (HMM). The successive segments group, having similar HMM states form variable-length frames. The handcrafted feature set from variable-length frames is classified using a deep recurrent neural network (RNN) structure. Finally, the maximum voting scheme on the predicted RNN output enhances the classification accuracy. The performance of this method is evaluated on a publicly available, and self-collected dataset. The findings reveal that handcrafted features supported by modeling of temporal dynamics outperform state-of-the-art methods. As a result, 97.8% accuracy is observed for multi-class classification with an optimum of four electrodes. •Modeling temporal dynamics through AR+FCM+HMM pipeline-interface handles nonstationarity in EEG.•HMM finds the interplay between the internal cognitive state.•Deep RNN structures improve classification accuracy with long-term memory capability.•The maximum voting scheme is effective for continuous monitoring in a multitasking environment.