Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 6Citation - Scopus: 7Time-Resolved Eeg Signal Analysis for Motor Imagery Activity Recognition(Elsevier, 2023) Olcay, Bilal Orkan; Karaçalı, BilgeAccurately characterizing brain activity requires detailed feature analysis in the temporal, spatial, and spectral domains. While previous research has proposed various spatial and spectral feature extraction methods to distinguish between different cognitive tasks, temporal feature analysis for each separate brain region and frequency band has been largely overlooked. This study introduces two novel approaches for recognizing cognitive activity: temporal entropic profiling and time-aligned common spatio-spectral patterns analysis. These approaches capture and use discriminative short-lived signal segments for motor imagery activity recognition. In Approach-1, we evaluated nine different measures to determine timing parameters that showed altered behavior associated with maximal inter-activity differences, which we then used in a machine-learning framework. In Approach-2, we used the best-performing signal characteristic measures from Approach-1 to determine the optimum latency of each channel at each frequency band for a CSP-based activity recognition strategy. We evaluated both approaches on two online available motor imagery EEG datasets and achieved average recognition accuracy levels of 86%. We compared our methods with four established BCI methods. The performance results show that our approaches exceeded the benchmark methods' performances, with notable improvements in the proposed time-aligned common spatio-spectral patterns approach. This study demonstrates that motor imagery recognition performance is improved when a temporal analysis is adopted alongside spatio-spectral neural feature analysis and that timing parameters associated with the maximal entropic difference of EEG segments to the cognitive tasks varied between different brain regions and subjects. © 2023 Elsevier LtdArticle Citation - WoS: 3Citation - Scopus: 3Automated Labeling of Cancer Textures in Larynx Histopathology Slides Using Quasi-Supervised Learning(Science Printers and Publishers Inc., 2014) Önder, Devrim; Sarıoğlu, Sülen; Karaçalı, BilgeOBJECTIVE: To evaluate the performance of a quasisupervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. STUDY DESIGN: Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasisupervised learning algorithm. RESULTS: Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. CONCLUSION: Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.Conference Object Citation - Scopus: 1Hierarchical Motif Vectors for Amino Acid Sequence Alignment(2012) Karaçalı, BilgeWe present a new framework for global and local alignment of amino acid sequences based on hierarchical motif vectors that characterize local amino acid configurations. The motif vectors are constructed by carrying out wavelet decomposition on numeric property sequences obtained by replacing each amino acid in a sequence with their respective properties, and concatenating such profiles obtained for a large number of physico-chemical properties into a single column vector. We then formulate different schemes for aligning amino acid sequences based on their respective motif vectors globally as well as locally subject to measures of statistical significance. Experiment results indicate that the motif vectors accurately capture the amino acid composition at and around individual sites along sequences and allow associating sequence segments sharing similar functional attributes.Conference Object Citation - Scopus: 1Doğrusal Olmayan Gömme Teknikleri Altında Gen Dizilerinin Evrimsel İ̇lişkileri(IEEE, 2010) Doğan, Tunca; Karaçalı, BilgeWe present an error analysis on the application of non-linear embedding on pairwise evolutionary distances inferred over a collection of genetic sequences following multiple sequence alignment. To this end, we have generated gene sequences evolved by random substitutions along three different evolutionary pathways with known evolutionary distances between every sequence pair. We have compared the discrepancy between the inferred evolutionary distances to the true distances before and after non-linear embedding into a low dimensional vector space. The results indicate that non-linear embedding achieves significant reduction in error in the estimated evolutionary distances. Consequently, nonlinear embedding of evolutionary distances can provide more reliable inferences on the evolutionary relationships between genetic sequences. ©2010 IEEE.