Key result
A recurrent self-evolving fuzzy neural network (RSEFNN) for EEG-based driving fatigue prediction demonstrated superior performance compared with state-of-the-art models without using independent component analysis.
Why the study?
Does a recurrent self-evolving fuzzy neural network improve EEG-based driving fatigue prediction compared to state-of-the-art models?
Does a recurrent self-evolving fuzzy neural network improve EEG-based driving fatigue prediction compared to state-of-the-art models?
A recurrent self-evolving fuzzy neural network can effectively predict driving fatigue from EEG signals in real-time without requiring independent component analysis for noise cancellation.
May support real-time EEG fatigue monitoring in drivers; leaves open prospective clinical validation.
We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-the-arts models.
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Liu et al. (2016) studied Driving fatigue. Recurrent self-evolving fuzzy neural network (RSEFNN) vs. State-of-the-art models was evaluated on Prediction of fatigue degrees. A recurrent self-evolving fuzzy neural network (RSEFNN) for EEG-based driving fatigue prediction demonstrated superior performance compared with state-of-the-art models without using independent component analysis.
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