Key result
Wavelet-based ensemble model achieves ~98% accuracy classifying focused, unfocused, and drowsy states from EEG.
Why the study?
The non-stationary and nonlinear nature of EEG signals complicates accurate subjective analysis, creating a need for automated mental state detection to improve brain-computer interface systems.
An ensemble wavelet decomposition-based model achieved up to 97.8% accuracy in classifying focused, unfocused, and drowsy mental states from EEG signals, potentially improving brain-computer interface systems.
Hypothesis-generating for EEG mental-state detection; leaves open prospective clinical validation before BCI use.
Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain-computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.
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Khare et al. (2023) studied Mental states (focused, unfocused, drowsy). Ensemble wavelet decomposition-based detection model was evaluated on Classification accuracy of three mental states. An ensemble wavelet decomposition-based model using an optimized ensemble classifier achieved up to 97.8% accuracy in classifying focused, unfocused, and drowsy mental states from EEG signals.
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