Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/202
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Article Citation - WoS: 1Citation - Scopus: 2AlN Piezoelectric Quad-Actuators for 2D Optical Micro Scanning(Taylor & Francis Ltd, 2022) Hah, DooyoungPiezoelectric actuation has been one of the frequent choices for optical micro scanning. In most of the cases, lead zirconate titanate (PZT) has been used as the piezoelectric material. However, PZT has a potential issue in biomedical applications due to the content of lead. For this, AlN can be used as an alternative. The main drawback of AlN is its low piezoelectric coefficients. In order to overcome such a drawback, this paper presents a novel actuator configuration, designed for a quasi-static operation mode. Quad-actuators and meander-shaped hinges are the essence of the proposed actuator configuration. Numerical simulation study is carried out to prove the concept of the device. The study also shows that the proposed scanner can have the optical scan angle of 9 degree at a quasi-static mode. Two different scan modes, a raster-like mode and a Lissajous mode are tested, demonstrating the two-dimensional scanning capability of the device.Article Citation - WoS: 11Citation - Scopus: 14Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform(World Scientific Publ Co Pte Ltd, 2021) Karakullukcu, Nedime; Yilmaz, BulentPatients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, p, less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.