Analysis of Affective Effects on Steady-State Visual Evoked Potential Responses

Wang, Shangfei; Wu, Guobing; Zhu, Yachen

doi:10.1007/978-3-642-33932-5_71

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…The bigger the dissimilarity degree, the larger the Fisher distance. The calculation of Fisher distance is as following [22]:…”

Section: Fisher-based Distance Metricmentioning

confidence: 99%

“…To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following equation set is often used in literature for examining performance quality as follows [32]: (22) where TP (true positive) represents the number of fatigue EEG signals identified as fatigue EEG signals; TN (true negative), the number of normal EEG signals classified as normal EEG signals; FP (false positive), the number of normal EEG signals recognized as fatigue EEG signals; FN (false negative), the number of fatigue EEG signals distinguished as normal EEG signals; Sn represents sensitivity; Sp represents specificity; Acc represents accuracy; and MCC represents Mathew's correlation coefficient.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Zhang

Min

2017

Applied Sciences

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Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.

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“…The bigger the dissimilarity degree, the larger the Fisher distance. The calculation of Fisher distance is as following [22]:…”

Section: Fisher-based Distance Metricmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Zhang

Min

2017

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Dauwels et al [30] studied early Alzheimer's disease on changes based on pattern visual evoked potentials. Wang investigated the effect of different emotional states on healthy subjects' steady-state visual evoked potential responses [31]. Mousa identified early visual field losses in glaucoma patients using the multifocal visual evoked potential technique [32].…”

Section: Introductionmentioning

confidence: 99%

Comparison of different entropies as features for person authentication based on EEG signals

Zhang

Min

et al. 2017

IET biom.

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Person authentication is an important part to protect individual privacy in the informational society. With the development of electroencephalogram (EEG), it gradually becomes feasible using EEG signals to identify person recognition. However, the analysis of EEG signals is complex, unstable and non-linear. With this fact, non-linear analysis such as entropy would be more appropriate. In this study, four types of entropies are used to extract EEG signals features for the purpose of person authentication, and the performance of person authentication based on different entropies is compared. In this study, self-face and non-self-face images are used to induce EEG signals for the authentication process. Eventually, the average accuracy of 16 subjects by jackknife test was 90.7%, which demonstrating its better authentication performance and the proposed method achieving higher performance compared with previous methods of EEG-based person authentication. The results also show that, though the four types of entropies were used as the feature extraction methods, the fuzzy entropy achieved the best performance for person authentication.

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Analysis of Affective Effects on Steady-State Visual Evoked Potential Responses

Cited by 2 publications

References 19 publications

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Comparison of different entropies as features for person authentication based on EEG signals

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