Altındiş, Fatih
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Altindis, Fatih
Altındiş, Fatih
Fatih Altındiş
Altındiş, Fatih
Fatih Altındiş
Job Title
Arş. Gör.
Email Address
fatih.altindis@agu.edu.tr
Main Affiliation
02.05. Elektrik & Elektronik Mühendisliği
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
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WoS Researcher ID
Sustainable Development Goals
SDG data is not available
Documents
13
Citations
71
h-index
4
This researcher does not have a WoS ID.
Scholarly Output
16
Articles
8
Views / Downloads
453/167
Supervised MSc Theses
1
Supervised PhD Theses
1
WoS Citation Count
51
Scopus Citation Count
71
WoS h-index
4
Scopus h-index
4
Patents
0
Projects
4
WoS Citations per Publication
3.19
Scopus Citations per Publication
4.44
Open Access Source
5
Supervised Theses
2
Google Analytics Visitor Traffic
| Journal | Count |
|---|---|
| -- 16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022 -- Biarritz -- 182947 | 1 |
| -- 2016 Medical Technologies National Conference, TIPTEKNO 2016 -- Antalya -- 126633 | 1 |
| -- 2024 International Conference on Decision Aid Sciences and Applications, DASA 2024 -- Manama -- 206116 | 1 |
| Biomedical Signal Processing and Control | 1 |
| European Signal Processing Conference -- 26th European Signal Processing Conference, EUSIPCO 2018 -- Rome -- 143333 | 1 |
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Article Citation - WoS: 3Citation - Scopus: 4Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Altindis, Fatih; Banerjee, Antara; Phlypo, Ronald; Yilmaz, Bulent; Congedo, MarcoThis article presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used. As compared to the conventional subject-specific train/test pipeline, both group learning and fast alignment significantly improve the classification accuracy except for the database with clinical subjects (average improvement: 2.12 +/- 1.88%). GALIA utilizes cyclic approximate joint diagonalization (AJD) to find a set of linear transformations, one for each domain, jointly aligning the feature vectors of all domains. Group learning achieves a many-to-many transfer learning without compromising the classification performance on non-clinical BCI data. Fast alignment further extends the group learning for any unseen domains, allowing a many-to-one transfer learning with the same properties. The former method creates a single machine learning model using data from previous subjects and/or sessions, whereas the latter exploits the trained model for an unseen domain requiring no further training of the classifier.Article Citation - WoS: 7Citation - Scopus: 9Split-Attention Effects in Multimedia Learning Environments: Eye-Tracking and EEG Analysis(Springer, 2022) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThis study aimed to evaluate the split-attention effect in multimedia learning environments via objective measurements as EEG and eye-tracking. Two different multimedia learning environments in a focused (integrated) and split-attention (separated) format were designed. The experimental design method was used. The participants consisted of 44 students divided into two groups for focused attention and split-attention. There were significant differences between the fixation, brain wave, and retention performance of the two groups. Fixations of the split-attention group were higher than the focused attention group. A significant difference was found in the focused attention group in the alpha brain wave in the frontal region for intra-group comparisons and in the split-attention group in the beta brain wave in the frontal area for the inter-group comparison. The retention performance of the focused attention group was higher than the split-attention group. Accordingly, more cognitive activity emerged in environments where the text was not integrated into the picture. Additionally, the narration of text instead of printed text is effective for focusing attention. To prevent the emergence of a split-attention effect, the text should be integrated into the picture in designs. Due to the split-attention effect, the eye-tracking and EEG data were different between the groups.Master Thesis Lazer ile Oluşturulan Kabarcıkların Göziçi Basınç Ölçümünde Kullanımı(Abdullah Gül Üniversitesi, 2017) ALTINDİŞ, FATİH; Altındiş, Fatih; Yılmaz, BülentGünümüzde göz tansiyonu ölçmeye yönelik farklı yaklaşımlar kullanılmaktadır. Ancak, bu yaklaşımlar bazı durumlarda hastaların göz tansiyonunu ölçmekte zorlanmaktadır. En fazla sıkıntı yaşanan durum, gözünden ameliyat geçirmiş kişilerin göz tansiyonunu ölçme konusunda yaşanmaktadır. Bu kişilerin korneası ameliyat sonrası hassaslaştığı için tonometre cihazları ile göz tansiyonu ölçülememektedir. Bu tez çalışması, 1064 nm dalga boyunda çalışan bir Nd:YAG lazer ile sıvı içerisinde oluşturulan kabarcıkların karakteristiğini inceleyerek, göz tansiyonunu bu kabarcıkların boyutlarından ölçmeye yönelik yeni bir yaklaşım geliştirmeyi amaçlamıştır. Bu doğrultuda, öncelikle göz içi ortamına benzeyen yapay bir ortam tasarlanmıştır. Bu ortam içerisinde lazer ile oluşturulan kabarcıkları takip edecek bir görüntüleme sistemi ve bu sisteme entegre çalışan bir görüntü işleme yazılımı geliştirilmiştir. Farklı sıvı basınçlarında lazer ile oluşturulan kabarcık görüntüleri işlenerek kabarcıklara ait özellikler çıkarılmıştır. Sonuçlar göstermiştir ki, lazer ile oluşturulan kabarcıkların hacimleri düşük basınç altında daha fazla olurken, sıvı basıncı arttıkça bu kabarcıkların hacmi azalmaktadır. Elde edilen veriler ışığında, kabarcıklarda meydana gelen bu hacim değişiminin, kabarcığın içinde bulunduğu sıvının basıncını ölçmekte kullanılabileceği sonucuna varılmıştır. Bu çalışma, lazer ile sıvı içerisinde oluşturulan kabarcıklar ile ilgili elde edilen bu verileri kullanarak, gözün ön kamarasında lazer ile kabarcık oluşturmaya ve göz içi basıncını bu kabarcık yardımıyla ölçmeye yönelik yeni bir yaklaşım geliştirmeyi önermektedir.Article Citation - WoS: 13Citation - Scopus: 14Relationship Between Objective and Subjective Cognitive Load Measurements in Multimedia Learning(Routledge Journals, Taylor & Francis Ltd, 2023) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThe aim of this study is to compare subjective and objective cognitive load measurements in a multimedia learning environment. For this purpose, 20 university students studied in multimedia environments designed by researchers during which eye movements and multichannel electroencephalography (EEG) signals were recorded. Self-report ratings were obtained at the end of the experiment, and retention performances of the students were measured. After the data were collected, Pearson Correlation analysis was applied. According to the results, significant relationship between the number of fixations and EEG frequency band powers was found. In addition, there was a negative relationship between retention performance and number of fixations. Moreover, a negative relationship was found between retention performance and self-reported measurements.Conference Object Citation - WoS: 7Citation - Scopus: 12Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces(European Signal Processing Conference, EUSIPCO, 2018) Altindis, Fatih; Yilmaz, Bulent; İçöz, Kutay; Borisenok, S.This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Detection of Epileptic Seizures With Tangent Space Mapping Features of EEG Signals(IEEE, 2021) Altindis, Fatih; Yilmaz, BulentDetection of epileptic seizures from EEG signals is well-studied topic for the last couple of decades. Lately, automated signal processing and machine learning methods were developed to detect epileptic seizures. However, most of the methods are tailored to subjects and require fine tuning of many parameters. In this study, we proposed to use Riemannian geometry-based signal processing method that already showed superior performance on brain-computer interface problems, to extract features. We showed that tangent space mapping features of EEG signals can be used to detect seizures with high accuracy and precision.Article Citation - WoS: 2Citation - Scopus: 2Crown Shaped Edge Multiband Antenna Design for 5G and X-Band Applications(Springer, 2023) Hakanoglu, Baris Gurcan; Kilic, Veli Tayfun; Altindis, Fatih; Turkmen, MustafaNowadays we are experiencing the fifth-generation (5G) technology with new frequency bands to achieve high broadband speed, minimum latency and more developed end user devices. Due to the different frequency ranges for different applications at 5G bands the antennas should support multiband operation in a compact structure. This paper proposes a new multiband microstrip patch antenna design operating at mid band 5G frequencies and in the X band. The structure of the antenna includes simply loading the top radiating edge with rhombic shaped stubs and slots. This configuration yields the antenna to have resonances at multiple frequencies based on the fact that the stubs and slots affect capacitive and inductive impedances on the lower and higher operating frequencies of the antenna. The unique design enables the antenna to have reasonably high gains at four different bands of 6.76 dBi, 6.47 dBi, 7.76 dBi and 5.51 dBi at 3.34 GHz, 4.61 GHz, 6.01 and 8.02 GHz, respectively. Also, the simulated antenna has been manufactured and measured. The measurement results are in good agreement with the simulation results. The proposed design can be used with many other frequency bands and dielectric materials as well to achieve multiband operation.Article Use of Laser-Induced Bubbles in Intraocular Pressure Measurement: A Preliminary Study(IOP Publishing Ltd, 2019) Altindis, Fatih; Ozdur, Ibrahim T.; Mutlu, Sait N.; Yilmaz, BulentThis work investigates the feasibility of a novel approach for measuring intraocular pressure (IOP) by analyzing micron-level laser-induced bubble characteristics in the intraocular fluid. We believe that this concept may be used as a non-invasive alternative for measuring a patient's IOP by analyzing the laser-induced bubble volume in the intraocular fluid in the anterior chamber of the eye. The behavior of laser-induced bubbles was examined under differing fluid pressure levels and at differing laser pulse energy levels. An intraocular medium-like environment was imitated and an imaging system was designed in order to capture laser-induced bubbles with their movements. The video recordings of the bubbles were processed using custom software, and the volume of the bubbles was estimated using three different approaches. The bubble volumes were estimated more accurately by using the rising velocity of the bubble rather than its direct radii appearances on the images. An inversely proportional relationship was observed between the laser-induced bubble volume and the fluid pressure. IOP can be measured with a non-invasive technique using laser-induced bubble volume. Deeper and detailed studies, including clinical studies, may lead to the use of lasers for measuring IOP.Article Citation - WoS: 16Citation - Scopus: 20Parameter Investigation of Topological Data Analysis for EEG Signals(Elsevier Sci Ltd, 2021) Altindis, Fatih; Yilmaz, Bulent; Borisenok, Sergey; Icoz, KutayTopological data analysis (TDA) methods have become appealing in EEG signal processing, because they may help the scientists explore new features of complex and large amount of data by simplifying the process from a geometrical perspective. Time delay embedding is a common approach to embed EEG signals into the state space. Parameters of this embedding method are variable and the structure of the state space can be entirely different depending on their selection. Additionally, extracted persistent homologies of the state spaces depend on filtration level and the number of points used. In this study, we showed how to adapt false nearest neighbor (FNN) test to find out the suitable/optimal time embedding parameters (i.e., time delay and embedding dimension) for EEG signals, and compared their effects on different types of artefacts and motor intention waves that are commonly used in brain-computer interfaces. We extracted and compared persistent homologies of state spaces that were reconstructed with four different sets of parameters. Later, the effect of filtration level on extracted persistent homologies was compared, and statistical significance levels were computed between leftand right-hand movement imaginations. Finally, computational cost of the discussed methods was found, and the adaptability of this method to a real-time application was evaluated. We demonstrated that the discussed parameters of the TDA approach were highly crucial to extract true topological features of the EEG signals, and the adapted testing approaches depicted the applicability of this approach on real-time analysis of EEG signals.Doctoral Thesis Beyin-Bilgisayar Arayüzlerine Yönelik Riemann Geometrisi ile İleri Sinyal İşleme Yaklaşımı(2023) Altındiş, Fatih; Yılmaz, BülentBu tezde, EEG tabanlı beyin-bilgisayar arayüzlerinde (BBA) Riemann geometrisine dayalı öğrenme transferi kullanımına dayalı gelişmeleri incelemekteyiz. Seçilen EEG sinyal epoklarının sınıflandırma performansına katkısını göstermek adına kayan pencere yaklaşımı geliştirdik. Bunun yanında, sinyal işleme adımlarına filtre bankasının eklenmesinin sınıflandırma doğruluğunu daha fazla arttırdığını gözlemledik. Açık veristelerinden motor-niyet dalgaları içeren verisetlerini kullanarak, klasik Tanjant Uzay Haritalama yöntemine kıyasla sınıflandırma performanısı ortalama % 7 iyileştirdik. Çalışmanın en önemli çıktısı, 'grup öğrenmesi' adlı yeni bir transfer öğrenme yaklaşımı ve bu yaklaşımın uzantısı olan, 'hızlı hizalama' yöntemidir. Grup öğrenmesi, klinik olmayan BBA açık verisetlerinde sınıflandırma performansından ödün vermeden çoklu alan uyarlaması yapmaktadır. Hızlı hizalama, alan uyarlamasını daha önce kullanılmamış yeni veriler için kullanmayı sağlamaktadır. Önerilen grup hizalama algoritması (GALIA), farklı kişilerden ve farklı oturumlardan alınan EEG verileri ile test edilmiştir. Sınıflandırma performansı ve hesaplama maliyeti için optimal hiper-parametre değerleri incelenmiştir. Çalışma, birçok kişiden kayıt edilen verileri kullanarak tek bir makine öğrenimi modelinin oluşturulmasını ve eğitilmiş modelin yeniden eğitilmesine gerek kalmadan yeni veriler üzerinde kullanılabileceğini göstermiştir. Bulgular, birçok kişi üzerinde öğrenme transferi gerçekleştirebilen bütünsel bir sinyal işleme akışı sağlayarak güçlü, genelleştirilebilir, ve yüksek sınıflandırma performansına sahip BBA sistemleri tasarlanmasına olanak sağlamaktadır.
