Browsing by Author "Altindis, Fatih"

Now showing 1 - 13 of 13

Citation - WoS: 4
Citation - Scopus: 5
Transfer 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, Marco
This 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.
Citation - WoS: 7
Citation - Scopus: 9
Split-Attention Effects in Multimedia Learning Environments: Eye-Tracking and EEG Analysis
(Springer, 2022) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, Bulent
This 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.
Citation - WoS: 13
Citation - Scopus: 14
Relationship Between Objective and Subjective Cognitive Load Measurements in Multimedia Learning
(Routledge Journals, Taylor & Francis Ltd, 2023) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, Bulent
The 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.
Citation - WoS: 7
Citation - Scopus: 12
Use 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.
Citation - Scopus: 1
Detection of Epileptic Seizures With Tangent Space Mapping Features of EEG Signals
(IEEE, 2021) Altindis, Fatih; Yilmaz, Bulent
Detection 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.
Citation - WoS: 2
Citation - Scopus: 2
Crown Shaped Edge Multiband Antenna Design for 5G and X-Band Applications
(Springer, 2023) Hakanoglu, Baris Gurcan; Kilic, Veli Tayfun; Altindis, Fatih; Turkmen, Mustafa
Nowadays 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.
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, Bulent
This 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.
Citation - WoS: 16
Citation - Scopus: 20
Parameter Investigation of Topological Data Analysis for EEG Signals
(Elsevier Sci Ltd, 2021) Altindis, Fatih; Yilmaz, Bulent; Borisenok, Sergey; Icoz, Kutay
Topological 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.
Citation - Scopus: 3
İki Durumlu Bir Beyin Bilgisayar Arayüzünde Özellik Çıkarımı ve Sınıflandırma
(Institute of Electrical and Electronics Engineers Inc., 2017) Altindis, Fatih; Yilmaz, Bulent
Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method. © 2017 Elsevier B.V., All rights reserved.
Many-to Transfer Learning on Motor Imagery BCI
(Institute of Electrical and Electronics Engineers Inc., 2024) Altindis, Fatih; Yilmaz, Bulent; Congedo, Marco
This paper presents many-to-many domain adaptation strategy, named group learning, for motor imagery brain-computer interfaces (BCIs). Group learning, grounded in Riemannian geometry, simultaneously aligns multiple domains in a unified model, whereas fast alignment approach integrates new, unseen domains without re-estimating alignment matrices for all domains. Group learning creates a single machine learning model using data from previous subjects and/or sessions. Fast alignment utilizes the already trained model for an unseen domain without requiring any additional classifier training. The tests on five publicly available motor imagery databases demonstrate the robustness of group learning against negative learning. The classification accuracy scores of binary and multiclass databases show comparable, if not superior, performance to conventional subject-wise learning method. © 2025 Elsevier B.V., All rights reserved.
Citation - WoS: 2
Citation - Scopus: 3
Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns
(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Karakullukcu, Nedime; Altindis, Fatih; Yilmaz, Bulent
This study addresses the challenge faced by individuals with upper-limb prostheses in regulating grip force and adapting movements to different object weights. Despite limited exploration, this research pioneers the use of EEG to estimate object weight perception in the context of upper-limb prostheses. Investigating neural correlates in this population provides valuable insights and aids the development of neurofeedback-based strategies for weight perception. Our objective is to identify EEG features predicting the weight perception of held objects. Employing Fourier-based synchrosqueezing transform (FSST) and regularized Common Spatial Patterns (CSP) features, we classify motor imagery waves representing three weight categories (light, medium, heavy). Subjects perform actual motor tasks before imagery sessions, and our approach integrates EEG features of both movements to train subject-specific machine learning models. Results reveal that FSST- singular value decomposition (SVD) features for medium and heavy objects are most distinctive. Achieving up to 90% accuracy, spatial features demonstrate effective classification of motor imagery for different weights. Unlike weight prediction studies, our focus is on visual perception and imagination of object weights, enhancing prosthetic hand system preconditioning. Binary classification surpasses 70% accuracy in predicting object weights, uniquely utilizing actual movement data for CSP algorithm regularization coefficient estimation.
Citation - WoS: 1
Citation - Scopus: 1
Prediction of Preference and Effect of Music on Preference: A Preliminary Study on Electroencephalography from Young Women
(Tubitak Scientific & Technological Research Council Turkey, 2019) Yilmaz, Bulent; Gazeloglu, Cengiz; Altindis, Fatih
Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.
Citation - Scopus: 2
Sliding Window and Filterbank Utilization on Riemannian Geometry
(Institute of Electrical and Electronics Engineers Inc., 2022) Altindis, Fatih; Yilmaz, Bulent
Riemannian geometry-based signal processing approaches on EEG signals provides similar decoding performance compared to state-of-the-art methods. However, Riemannian geometry framework requires predefine EEG signal epoch that is to be used in the analysis. Sliding window approach that operates in Riemannian geometry proposed to enable use of EEG signals without constrained by the record length. Decoding performance of tangent space mapping was increased more than 6% in overall accuracy compared the previous study's results. Instead of using single band-pass filter, utilization of filterbank is proposed to increase decoding performance. Distance based Riemannian classifier's overall performance were increased by 5% compared to standard Riemannian geometry approach. © 2022 Elsevier B.V., All rights reserved.