WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object Citation - Scopus: 1Sustainable Economic Development Indicators: The Case of Turkey(World Scientific Publ Co Pte Ltd, 2016-08) Soylemez, Ismet; Dogan, Ahmet; Ozcan, UgurSustainable development indicators are a good road map for financial, social and economic targets of countries. This paper aims to show which indicators are affect sustainable development of Turkey for last twelve years. 132 sustainable development indicators determined by European Union Statistical Office (Eurostat). Sustainable development indicators are calculated by related unit, institution or establishment in the direction of definitions determined by Eurostat. These indicators are calculated by TUIK (Turkish Statistical Institute) for Turkey. Some indicators as follows: socio-economic development, sustainable consumption and production, climate change and energy, sustainable transport, financing for sustainable development. However, only economic indicators are presented and analyzed in the case study. Official development assistance has tenfold rise in the last 12 years. These indicators will show which areas at economic changes should be considered to the sustainable development of country.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.