脑力负荷识别对提高人机交互操作人员的工作绩效具有重要意义。目前已有研究表明,通过提取脑电(electroencephalogram,EEG)信号的能量特征进行脑力负荷识别取得了不错的分类效果。但该方法只关注到信号的幅度信息,而忽略了相位信息;只...脑力负荷识别对提高人机交互操作人员的工作绩效具有重要意义。目前已有研究表明,通过提取脑电(electroencephalogram,EEG)信号的能量特征进行脑力负荷识别取得了不错的分类效果。但该方法只关注到信号的幅度信息,而忽略了相位信息;只分析了各个通道的频域特征,没有考虑不同通道信号之间的同步关系。为充分考虑不同脑区间的功能连接性,提出一种基于加权相位滞后指数(weighted phase lag index,WPLI)热力图的脑力负荷分类方法。对预处理后的脑电信号计算两两通道间的WPLI并绘制热力图,用于评估不同通道信号之间的相位耦合情况,由此反映不同脑区间的功能连接性。由WPLI热力图可以直观地观察到:在高、低负荷状态下,大脑功能连接性的分布存在明显差异。通过实验分别对能量特征图和WPLI热力图采用方向梯度直方图-支持向量机(histogram of oriented gradient-support vector machine,HOG-SVM)和LeNet-5两种方法进行分类。结果表明:WPLI热力图和LeNet-5的组合具有较好的分类结果。展开更多
The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine ...The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.展开更多
Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic s...Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students’ mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners’ performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students’ performance.展开更多
Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alph...Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alpha, and beta power spectrum, these two states can be clearly discriminated. In these two states, frontal areas were activated in delta power, both frontal and occipital areas were activated in theta band, and occipital areas were activated in alpha and beta bands. These four bands had significantly higher power in frontal, parietal, and occipital areas when eyes were close. The results also implied that the optimum order of AR model could be more suitable for estimating EEG power spectrum of different states.展开更多
Carotid angioplasty and stenting (CAS) was developed to be a less invasive and complex procedure compared to carotid endarterectomy (CEA). It has emerged as an alternative for patients who are considered to have high ...Carotid angioplasty and stenting (CAS) was developed to be a less invasive and complex procedure compared to carotid endarterectomy (CEA). It has emerged as an alternative for patients who are considered to have high surgical risks due to medical comorbidities or anatomical high-risk features [1]. The procedure is usually done under local anesthesia with light sedation, with the subsequent expectation of less neurologic injury, venous thromboembolisms, and myocardial infarctions—all well-known clinical risks of undergoing surgical procedures under general anesthesia. CAS, however, carries some increased risks of arterial dissection, dislocation of atherothrombotic debris and embolization to the brain or eye, late embolization due to thrombus formation on the damaged plaque, and bradycardia and hypotension as a result of carotid sinus stimulation. Electroencephalography can detect cerebral ischemia and hypoxia along with measuring hypnotic effects, but has not been reported to be used during CAS to signal impending neurological deficit and allow for intervention to prevent stroke. We report on the use of patient state index (PSI), an electroencephalographic (EEG) derived variable used by SEDLine monitor (Masimo Inc., San Diego, CA) to monitor changes in cerebral blood flow during carotid angioplasty and stenting in an awake patient under local anesthesia. PSI was developed to measure the level of hypnosis and sedation during anesthesia and in the ICU. The PSI is based on quantitative electroencephalogram features, recorded from anterior and posterior scalp sites, as input to a multivariate algorithm that quantifies the most probable level of anesthesia or sedation. The PSI is reported as a range from 0 to 100, with decreasing values indicating increasing levels of anesthesia or sedation. Adequate depth of anesthesia is reflected by PSI value of 25 - 50, and a fully awake state by a PSI of 100 [2]. Other EEG analysis techniques have been explored to detect changes in cerebral blood flow during carotid surgery [3], such as entropy described by Khan and Ozcan in his recent work entitled Disagreement in Bilateral State Entropy Values in Carotid Artery Disease [4], but there are no previous reports of the use of PSI during procedural sedation in carotid angioplasty and stenting in an awake patient.展开更多
文摘脑力负荷识别对提高人机交互操作人员的工作绩效具有重要意义。目前已有研究表明,通过提取脑电(electroencephalogram,EEG)信号的能量特征进行脑力负荷识别取得了不错的分类效果。但该方法只关注到信号的幅度信息,而忽略了相位信息;只分析了各个通道的频域特征,没有考虑不同通道信号之间的同步关系。为充分考虑不同脑区间的功能连接性,提出一种基于加权相位滞后指数(weighted phase lag index,WPLI)热力图的脑力负荷分类方法。对预处理后的脑电信号计算两两通道间的WPLI并绘制热力图,用于评估不同通道信号之间的相位耦合情况,由此反映不同脑区间的功能连接性。由WPLI热力图可以直观地观察到:在高、低负荷状态下,大脑功能连接性的分布存在明显差异。通过实验分别对能量特征图和WPLI热力图采用方向梯度直方图-支持向量机(histogram of oriented gradient-support vector machine,HOG-SVM)和LeNet-5两种方法进行分类。结果表明:WPLI热力图和LeNet-5的组合具有较好的分类结果。
基金Key Program of Natural Science Foundation of Shandong Province(No.ZR2013FZ002)The Program of Science and Technology of Suzhou(No.ZXY2013030)Independent Innovation Foundation of Shandong University(No.11170074611102)
文摘The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.
文摘Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students’ mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners’ performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students’ performance.
文摘Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alpha, and beta power spectrum, these two states can be clearly discriminated. In these two states, frontal areas were activated in delta power, both frontal and occipital areas were activated in theta band, and occipital areas were activated in alpha and beta bands. These four bands had significantly higher power in frontal, parietal, and occipital areas when eyes were close. The results also implied that the optimum order of AR model could be more suitable for estimating EEG power spectrum of different states.
文摘Carotid angioplasty and stenting (CAS) was developed to be a less invasive and complex procedure compared to carotid endarterectomy (CEA). It has emerged as an alternative for patients who are considered to have high surgical risks due to medical comorbidities or anatomical high-risk features [1]. The procedure is usually done under local anesthesia with light sedation, with the subsequent expectation of less neurologic injury, venous thromboembolisms, and myocardial infarctions—all well-known clinical risks of undergoing surgical procedures under general anesthesia. CAS, however, carries some increased risks of arterial dissection, dislocation of atherothrombotic debris and embolization to the brain or eye, late embolization due to thrombus formation on the damaged plaque, and bradycardia and hypotension as a result of carotid sinus stimulation. Electroencephalography can detect cerebral ischemia and hypoxia along with measuring hypnotic effects, but has not been reported to be used during CAS to signal impending neurological deficit and allow for intervention to prevent stroke. We report on the use of patient state index (PSI), an electroencephalographic (EEG) derived variable used by SEDLine monitor (Masimo Inc., San Diego, CA) to monitor changes in cerebral blood flow during carotid angioplasty and stenting in an awake patient under local anesthesia. PSI was developed to measure the level of hypnosis and sedation during anesthesia and in the ICU. The PSI is based on quantitative electroencephalogram features, recorded from anterior and posterior scalp sites, as input to a multivariate algorithm that quantifies the most probable level of anesthesia or sedation. The PSI is reported as a range from 0 to 100, with decreasing values indicating increasing levels of anesthesia or sedation. Adequate depth of anesthesia is reflected by PSI value of 25 - 50, and a fully awake state by a PSI of 100 [2]. Other EEG analysis techniques have been explored to detect changes in cerebral blood flow during carotid surgery [3], such as entropy described by Khan and Ozcan in his recent work entitled Disagreement in Bilateral State Entropy Values in Carotid Artery Disease [4], but there are no previous reports of the use of PSI during procedural sedation in carotid angioplasty and stenting in an awake patient.
