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Drowsy driving is the main reason for sleep-related crashes. We have observed that an alpha wave attenuation-disappearance phenomenon and a typical alpha blocking phenomenon commonly exist in the eye closure events during daytime simulated driving experiments. These two alpha-related phenomena prove to respectively represent two different sleepiness levels: the sleep onset and the relaxed wakefulness...
Physiological signals such as EEG and EOG have been successfully applied to detect driving fatigue in single modality. In this paper, we propose a multimodal approach by combining partial EEG and forehead EOG to enhance driving fatigue detection. We investigate the key brain area where we collect the EEG to combine with forehead EOG. Our experiment results demonstrate that the temporal EEG signals...
Attenuation of alpha wave is considered as the most valid marker of sleep onset during sleep, but this has received little attention during driving. Interestingly, from our simulated driving experiments, a new alpha wave's attenuation-disappearance phenomenon was observed to frequently appear in eye closure events (ECEs), with an obvious split point, which divides ECE into alpha attenuation phase...
Attention of subjects in EEG-based emotion recognition experiments determines the quality of EEG data. Traditionally, self-assessment with questionnaires is used to evaluate the attention degree of subjects in experiments. However, this kind of self-assessment approach is subjective and inaccurate. Low quality EEG data from subjects without attention might influence the experiment evaluation and degrade...
This paper aims to explore the neural patterns between Chinese and Germans for electroencephalogram (EEG)-based emotion recognition. Both Chinese and German subjects, wearing electrode caps, watched video stimuli that triggered positive, neutral, and negative emotions. Two emotion classifiers are trained on Chinese EEG data and German EEG data, respectively. The experiment results indicate that: a)...
Slow eye movement (SEM) is reported as a reliable indicator of sleep onset period (SOP) in sleep researches, but its characteristics and functions for detecting driving fatigue have not been fully studied. Through visual observations on ten subjects' experimental data, we found that SEMs tend to occur during eye closure events (ECEs). SEMs accompanied with alpha wave's attenuation during simulated...
In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine women participate in our driving simulation experiments. Two fusion strategies are adopted: feature level fusion (FLF) and decision level fusion (DLF). PERCLOS (the percentage of eye closure)...
This study aims at measuring last-night sleep quality from electroencephalography (EEG). We design a sleep experiment to collect waking EEG signals from eight subjects under three different sleep conditions: 8 hours sleep, 6 hours sleep, and 4 hours sleep. We utilize three machine learning approaches, k-Nearest Neighbor (kNN), support vector machine (SVM), and discriminative graph regularized extreme...
To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from...
Addressing the structural and functional variability between subjects for robust affective brain-computer interface (aBCI) is challenging but of great importance, since the calibration phase for aBCI is time-consuming. In this paper, we propose a subject transfer framework for electroencephalogram (EEG)-based emotion recognition via component analysis. We compare two state-of-the-art subspace projecting...
This paper presents a new emotion recognition method which combines electroencephalograph (EEG) signals and pupillary response collected from eye tracker. We select 15 emotional film clips of 3 categories (positive, neutral and negative). The EEG signals and eye tracking data of five participants are recorded, simultaneously, while watching these videos. We extract emotion-relevant features from EEG...
EEG signals, which can record the electrical activity along the scalp, provide researchers a reliable channel for investigating human emotional states. In this paper, a new algorithm, manifold regularized extreme learning machine (MRELM), is proposed for recognizing human emotional states (positive, neutral and negative) from EEG data, which were previously evoked by watching different types of movie...
In recent years, there are many great successes in using deep architectures for unsupervised feature learning from data, especially for images and speech. In this paper, we introduce recent advanced deep learning models to classify two emotional categories (positive and negative) from EEG data. We train a deep belief network (DBN) with differential entropy features extracted from multichannel EEG...
In this demo paper, we designed a novel framework combining EEG and eye tracking signals to analyze users' emotional activities in response to multimedia. To realize the proposed framework, we extracted efficient features of EEG and eye tracking signals and used support vector machine as classifier. We combined multimodel features using feature-level fusion and decision-level fusion to classify three...
In this research, we used EEG signals to analyze gender processing with the ERSP method. Not only facial images, but also images of clothing and shoes, were used. We applied the ICA method to obtain a gender-related component which appeared quite significant in the majority of electrode sites for the occipital lobe. This showed differences of energy between the two genders, even for the clothing and...
This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine....
For many human machine interaction systems, to ensure work safety, the techniques for continuously estimating the vigilance of operators are highly desirable. Up to now, various methods based on electroencephalogram (EEG) are proposed to solve this problem. However, most of them are static methods and are based on supervised learning strategy. The main deficiencies of the existing methods are that...
This study aims at using electrooculographic (EOG) features, mainly slow eye movements (SEM), to estimate the human vigilance changes during a monotonous task. In particular, SEMs are first automatically detected by a method based on discrete wavelet transform, then linear dynamic system is used to find the trajectory of vigilance changes according to the SEM proportion. The performance of this system...
Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery...
Electroencephalogram (EEG) based vigilance detection of those people who engage in long time attention demanding tasks such as monotonous monitoring or driving is a key field in the research of brain-computer interface (BCI). However, robust detection of human vigilance from EEG is very difficult due to the low SNR nature of EEG signals. Recently, compressive sensing and sparse representation become...
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