Browsing by Author "Balli, Tugce"

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Citation - WoS: 3
Citation - Scopus: 3
Comparative Classification Performances of Filter Model Feature Selection Algorithms in Eeg Based Brain Computer Interface System
(Gazi Univ, Fac Engineering Architecture, 2023) Bulut, Cem; Ballı, Tuğçe; Balli, Tugce; Yetkin, Emrullah Fatih; Yetkin, E. Fatih
Brain-computer interface (BCI) systems enable individuals to use a computer or assistive technologies such as a neuroprosthetic arm by translating their brain electrical activity into control commands. In this study, the use of filter-based feature selection methods for design of BCI systems is investigated. EEG recordings obtained from a BCI system designed for the control of a neuroprosthetic device are analyzed. Two feature sets were created; the first set was band power features from six main frequency bands (delta (1.0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-25 Hz), high-beta (25-30Hz) and gamma (30-50 Hz)) and the second set was band power features from ten frequency sub-bands (delta (1-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-12 Hz), beta1 (12-15 Hz), beta2 (15-18 Hz), beta3 (18-25 Hz), gamma1 (30-35 Hz), gamma2 (35-40 Hz), gamma3 (40-50 Hz)). Ten filter-based feature selection methods are investigated along with linear discriminant analysis, random forests, decision tree and support vector machines algorithms. The results indicate that feature selection methods leads to a higher classification accuracy and eigen value centrality (Ecfs) and infinite feature selection (Inffs) methods have consistently provided higher accuracy rates as compared to rest of the feature selection methods.
Citation - WoS: 0
Citation - Scopus: 0
Decoding Functional Brain Data for Emotion Recognition: A Machine Learning Approach
(Assoc Computing Machinery, 2024) Ballı, Tuğçe; Balli, Tugce
The identification of emotions is an open research area and has a potential leading role in the improvement of socio-emotional skills such as empathy, sensitivity, and emotion recognition in humans. The current study aimed at using Event Related Potential (ERP) components (N100, N200, P200, P300, early Late Positive Potential (LPP), middle LPP, and late LPP) of EEG data for the classification of emotional states (positive, negative, neutral). EEG datawere collected from 62 healthy individuals over 18 electrodes. An emotional paradigm with pictures from the International Affective Picture System (IAPS) was used to record the EEG data. A linear Support Vector Machine (C = 0.1) was used to classify emotions, and a forward feature selection approach was used to eliminate irrelevant features. The early LPP component, which was the most discriminative among all ERP components, had the highest classification accuracy (70.16%) for identifying negative and neutral stimuli. The classification of negative versus neutral stimuli had the best accuracy (79.84%) when all ERP components were used as a combined feature set, followed by positive versus negative stimuli (75.00%) and positive versus neutral stimuli (68.55%). Overall, the combined ERP component feature sets outperformed single ERP component feature sets for all stimulus pairings in terms of accuracy. These findings are promising for further research and development of EEG-based emotion recognition systems.
Differentiating Functional Connectivity Patterns in Adhd and Autism Among the Young People: a Machine Learning Solution
(Sage Publications inc, 2025) Sutcubasi, Bernis; Balli, Tugce; Roeyers, Herbert; Wiersema, Jan R.; Camkerten, Sami; Ozturk, Ozan Cem; Sonuga-Barke, Edmund
Objective: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of intrinsic patterns of brain connectivity revealed during resting state using machine learning approaches. We had two key objectives: (a) to determine the extent to which ADHD and autism could be effectively distinguished via machine learning from one another on this basis and (b) to identify the brain networks differentially implicated in the two conditions.Method: Data from two publicly available resting-state functional magnetic resonance imaging (fMRI) resources-Autism Brain Imaging Data Exchange (ABIDE) and the ADHD-200 Consortium-were analyzed. A total of 330 participants (65 females and 265 males; mean age = 11.6 years), comprising equal subgroups of 110 participants each for ADHD, autism, and healthy controls (HC), were selected from the data sets ensuring data quality and the exclusion of comorbidities. We identified region-to-region connectivity values, which were subsequently employed as inputs to the linear discriminant analysis algorithm.Results: Machine learning models provided strong differentiation between connectivity patterns in participants with ADHD and autism-with the highest accuracy of 85%. Predominantly frontoparietal network alterations in connectivity discriminate ADHD individuals from autism and neurotypical group. Networks contributing to discrimination of autistic individuals from neurotypical group were more heterogeneous. These included language, salience, and frontoparietal networks.Conclusion: These results contribute to our understanding of the distinct neural signatures underlying ADHD and autism in terms of intrinsic patterns of brain connectivity. The high level of discriminability between ADHD and autism, highlights the potential role of brain based metrics in supporting differential diagnostics.