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Driver Drowsiness is one of the leading causes of road accidents. Electroencephalography (EEG) is highly affected by drowsiness; hence, EEG-based methods detect drowsiness with the highest accuracy. Developments in manufacturing dry…
As autonomous driving systems prevail, it is becoming increasingly critical that the systems learn from databases containing fine-grained driving scenarios. Most databases currently available are human-annotated; they are expensive,…
Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to…
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR navigation dataset, we examined whether EEG signals can classify…
Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient's physical mobility, such as freezing of hand movements. Although rehabilitation training from external devices is beneficial for hand…
A lack of driver's vigilance is the main cause of most vehicle crashes. Electroencephalography(EEG) has been reliable and efficient tool for drivers' drowsiness estimation. Even though previous studies have developed accurate and robust…
In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution…
Brain computer interface based assistive technology are currently promoted for motor rehabilitation of the neuromuscular ailed individuals. Recent studies indicate a high potential of utilising electroencephalography (EEG) to extract motor…
Electroencephalography (EEG) signals signals are often used to learn about brain structure and to learn what thinking. EEG signals can be easily affected by external factors. For this reason, they should be applied various pre-process…
Driving information and data under potential vehicle crashes create opportunities for extensive real-world observations of driver behaviors and relevant factors that significantly influence the driving safety in emergency scenarios.…
Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging(fMRI) data - while offering millimeter-scale spatial resolution (0.5-2 mm^3 voxels), these approaches suffer from limited temporal…
Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer…
Visual perception is the most critical input for driving decisions. In this study, our aim is to understand relationship between saliency and driving decisions. We present a novel attention-based saliency map prediction model for making…
One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its…
Fatigue is the most vital factor of road fatalities and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to analyze an electroencephalogram (EEG) dataset captured during a simulated…
Several changes occur in the brain in response to voluntary and involuntary activities performed by a person. The ability to retrieve data from the brain within a time space provides a basis for in-depth analyses that offer insight on what…
An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and…
There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable…
Electroencephalogram (EEG) based brain-computer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in…
For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of…