Chuen Rue Ng

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain–computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

Article: Extracranial internal carotid artery calcium volume measurement using computer tomography - International Angiology 2017 October;36(5):445-61 - Minerva Medica - Journals

The segmentation of the brain region is known to be vital in many research especially in the field of neuropsychiatric disorders for both detection and diagnosis. The segmentation of the brain and the computation of brain volume proved to be vital in the detection of many brain pathologies using Computed Tomography (CT) scan images. Distance regularized level set is a variation of level set that do not requires reinitialization. Hence, automated algorithm can be developed to segment brain region and quantify brain volume automatically. In this paper, level set was used to segment the brain region. A refine level set is then carried out if the segmented results consist of objects with high or low Hounsfield Unit (HU) as compared to the threshold HU set by calculating the mean of brain region. The automated level set algorithm showed encouraging results after being compared to the ground truth volume traced manually.

In the field of neuropsychiatric disorders, it is known that brain segmentation is important for both detection and diagnosis. The segmentation of the brain, which leads to the computation of brain volume proved to be vital in the detection of many brain pathology having Computed Tomography (CT) scan as the primary modality. Due to the fact that Fuzzy c-Means (FCM) proven to be robust, it is often used in data clustering and also in image segmentation. On the other hand, Graph cut is also a great segmentation algorithm for image segmentation as it allows the separation of the image into numerous partitions according to the similarity between each nodes in the image. In this paper, FCM was first used as global processing on CT scan images that separated the images into clusters based on pixel intensity. After that, local processing with graph cut algorithm was carried out on the automatically selected cluster from the FCM. Manual interaction is needed after the images were separated into partitions to select the appropriate partitions that best represent the brain region. The results showed that the images are less erroneous when they are clustered first with FCM before going through the graph cut algorithm.

Brain segmentation is important in the field of neuropsychiatric disorders. With Computed Tomography (CT) scan being the gold standard in brain scan, brain segmentation in CT images is also very important in the detection of many pathology related to the brain. Fuzzy c-Means (FCM) is a popular method in data clustering and also in image segmentation due to it being robust. Graph cut is a segmentation algorithm that is able to separate the image into several partitions based on the similarity between each nodes in the image. In this paper, the CT scan images were first processed with FCM optimization and are separated into clusters based on pixel intensity. After that the post-FCM images were then loaded into the graph cut algorithm to separate the images into partitions, allowing users to manually select the appropriate partitions that best represent the brain region. The results showed that the images are less erroneous when they are clustered first with FCM before going through the graph cut algorithm.