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This study considers the prediction of driver's cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver's cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-and-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks...
Motion sickness is a common symptom which occurs when the brain receives conflicting sensory information. Although many motion sickness-related biomarkers have been identified, estimating humans' motion sickness level (MSL) remains a challenge in operational environments. Traditionally, questionnaire and physical check are the common ways to passively evaluate subject's sickness level. This study...
This study explores electroencephalographic (EEG) dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events during a sustained-attention driving task, which immediately agitated subject's responses to the events...
Ongoing brain activity can be recorded as electroen-cephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific...
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