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CNS Computational Neuroscience Lab

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The researchers and students working in our lab.

Lab Leadership

  • Prof. Dr. Aydın Akan

    Lab Lead His research focuses on time–frequency analysis of nonstationary signals and its applications in communications and biomedical engineering. akan.aydin@ieu.edu.tr
    Full biography

    Aydın Akan was born in Bursa in January 1967 and completed his primary and secondary education there. He studied electronic engineering at Uludağ University from 1984 to 1988. He began his master’s studies in electronics and communications at Istanbul Technical University in 1988 and completed the program in February 1991. His master’s thesis examined real-time adaptive noise cancellation for biological signals.

    He joined Istanbul University in 1988 and was appointed a research assistant in the Electronics Division of the Department of Electronics Engineering in 1990. In March 1992, he began doctoral studies in signal processing at the University of Pittsburgh. He completed his doctorate on time–frequency analysis with Gabor expansions in April 1996 and returned to Türkiye. In December 1996, he was appointed assistant professor in the Electronics Division at Istanbul University. He became head of the division in April 1997 and deputy head of the department in February 1999. He earned the title of associate professor in signal processing in November 2000. Istanbul University appointed him associate professor in the Telecommunications Division of the Department of Electrical and Electronics Engineering on 2 May 2001 and professor on 31 October 2006.

    In 2004, with a fellowship from TÜBİTAK, he worked as a visiting researcher and faculty member at the University of Pittsburgh. In February 2006, he was appointed founding coordinator of the Biomedical and Clinical Engineering Unit at Istanbul University Hospitals, a role he held until 2017. On 20 March 2017, he was appointed professor in the Department of Biomedical Engineering at İzmir Kâtip Çelebi University. He served as dean of the Faculty of Engineering and Architecture from May 2017 to October 2018. His roles at the university through 31 January 2020 included department chair, Scientific Research Projects coordinator, and director of the Biomedical Testing and Calibration Application and Research Center. On 1 February 2020, he was appointed professor in the Department of Electrical and Electronics Engineering at Izmir University of Economics.

    Professor Akan’s research focuses on time–frequency analysis of nonstationary signals and its applications in communications and biomedical engineering. He has led and contributed to numerous projects supported by TÜBİTAK and university research programs. His work has appeared in books, journal articles, book chapters, and conference proceedings in Türkiye and internationally. He serves on journal review boards and on advisory, executive, organizing, technical program, and review committees for scientific conferences. He is an IEEE Senior Member affiliated with the Signal Processing and Engineering in Medicine and Biology societies. He is also a member of the EURASIP Technical Area Committee on Biomedical Signal and Image Analytics and serves on the editorial board of Elsevier’s Digital Signal Processing journal.

    Publications in the lab (90)
    1. 2026 Investigation of the Effect of Product Mascot Gender on Consumer Emotions Using EEG Signals Medical Technologies Congress (TIPTEKNO)
    2. 2026 Deep Model Families for EEG-Based Multi-Class Dementia Classification International Journal of Neural Systems · DOI
    3. 2026 Analysis of Human Emotions for Different Taste Stimuli Using EEG Signals International Conference on Control, Decision and Information Technologies (CoDIT) · DOI
    4. 2026 Investigating the Effect of Sustainable Product Packaging on Consumer Behavior Using EEG Signals International Conference on Control, Decision and Information Technologies (CoDIT) · DOI
    5. 2025 Analysis of Dementia EEG Signals Using Empirical Mode Decomposition Variants and Deep Learning Medical Technologies Congress (TIPTEKNO) · DOI
    6. 2025 Classification of Emotions Under Multiple Olfactory Stimuli Using EEG Signals and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    7. 2025 Design of a Smart Infusion Pump with Decision Support TIPTEKNO 2025 - Medical Technologies Congress, Proceedings -- 2025 Medical Technologies Congress, TIPTEKNO 2025 -- 26 October 2025 through 28 October 2025 -- Gazi Magusa -- 217812 · DOI
    8. 2025 Olfactory Emotion Recognition Using EEG Spectral Topographic Heatmaps and CNNs Medical Technologies Congress (TIPTEKNO) · DOI
    9. 2025 Detection of Alzheimer’s Disease by Using Time-Frequency Representations of EEG Signals with Deep Learning International Conference on Control, Decision and Information Technologies (CoDIT) · DOI
    10. 2025 Liking State Estimation Using Time-Frequency Image Representation of EEG Signals Signal Processing and Communications Applications Conference (SIU) · DOI
    11. 2025 Signals and Systems Using Matlab® Signals and Systems Using MATLAB® · DOI
    12. 2024 Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    13. 2024 Investigating the Effect of Noise Levels on Mental Tasks Using Artificial Intelligence Medical Technologies Congress (TIPTEKNO) · DOI
    14. 2024 Vital Signs Monitoring Ecosystem for Children Medical Technologies Congress (TIPTEKNO) · DOI
    15. 2024 Alzheimer's Dementia Detection: An Optimized Approach Using ITD of EEG Signals European Signal Processing Conference (EUSIPCO) · DOI
    16. 2024 Detection of Alzheimer's Dementia by Using EEG Feature Maps and Deep Learning European Signal Processing Conference (EUSIPCO) · DOI
    17. 2024 Classification of Alzheimer’s dementia EEG signals using deep learning Transactions of the Institute of Measurement and Control · DOI
    18. 2024 Detection of Attention Deficit Hyperactivity Disorder based on EEG feature maps and deep learning Journal of Applied Biomedicine · DOI
    19. 2024 Consumer Preference Estimation Using EEG Signals and Deep Learning Signal Processing and Communications Applications Conference (SIU) · DOI
    20. 2024 Emotional Stimulus Classification from Brain Electrical Activity using Multivariate Empirical Mode Decomposition Signal Processing and Communications Applications Conference (SIU) · DOI
    21. 2024 Continuous-time signals Elsevier eBooks · DOI
    22. 2024 Continuous-time systems Elsevier eBooks · DOI
    23. 2024 Discrete-time signals and systems Elsevier eBooks · DOI
    24. 2024 Fourier analysis of discrete-time signals and systems Elsevier eBooks · DOI
    25. 2024 Frequency analysis: the Fourier series Elsevier eBooks · DOI
    26. 2024 Frequency analysis: the Fourier transform Elsevier eBooks · DOI
    27. 2024 From the ground up! Elsevier eBooks · DOI
    28. 2024 Introduction to the design of discrete filters Elsevier eBooks · DOI
    29. 2024 Sampling theory Elsevier eBooks · DOI
    30. 2024 The Laplace transform Elsevier eBooks · DOI
    31. 2024 The Z-transform Elsevier eBooks · DOI
    32. 2024 Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods Istanbul University - Journal of Electrical & Electronics Engineering · DOI
    33. 2023 Detection of Alzheimer’s Dementia by Using Deep Time–Frequency Feature Extraction Istanbul University - Journal of Electrical & Electronics Engineering · DOI
    34. 2023 Detection of Attention Deficit Hyperactivity Disorder using Decision-level Fusion of Brain Connectivity Information Based on Deep Learning Medical Technologies Congress (TIPTEKNO) · DOI
    35. 2023 Detection of the Effects of Environmental Condition on Attention/Memory Using EEG Signals Medical Technologies Congress (TIPTEKNO) · DOI
    36. 2023 Classification of Dementia EEG Signals by Using Time-Frequency Images for Deep Learning Innovations in Intelligent Systems and Applications Conference (ASYU) · DOI
    37. 2023 Classification of ADHD by Using Multiple Feature Maps of EEG Signals and Deep Feature Extraction European Signal Processing Conference (EUSIPCO) · DOI
    38. 2023 Combinations of EEG Topographic Feature Maps for the Classification of ADHD European Signal Processing Conference (EUSIPCO) · DOI
    39. 2023 Detection of Attention Deficit Hyperactivity Disorder by Using EEG Feature Maps and Deep Learning European Signal Processing Conference (EUSIPCO) · DOI
    40. 2023 Detection of Alzheimer's Dementia Using Intrinsic Time Scale Decomposition of EEG Signals and Deep Learning International Conference on Control, Decision and Information Technologies (CoDIT) · DOI
    41. 2023 Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time–Frequency Features of EEG Data International Journal of Neural Systems · DOI
    42. 2022 Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals Biomedical Signal Processing and Control · DOI
    43. 2022 Deep Learning-Based Computer-Aided Diagnosis System for Attention Deficit Hyperactivity Disorder Classification Using Synthetic Data CRC Press eBooks · DOI
    44. 2022 An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in EEG Medical Technologies Congress (TIPTEKNO) · DOI
    45. 2022 Deep Time-Frequency Feature Extraction for Alzheimer's Dementia EEG Classification Medical Technologies Congress (TIPTEKNO) · DOI
    46. 2022 Detection of Attention Deficit Hyperactivity Disorder Using EEG Signals and Douglas—Peucker Algorithm Medical Technologies Congress (TIPTEKNO) · DOI
    47. 2022 Realtime Determination of Mood in Classroom Environment Medical Technologies Congress (TIPTEKNO) · DOI
    48. 2022 Detection of Alzheimer’s Dementia by Using Signal Decomposition and Machine Learning Methods International Journal of Neural Systems · DOI
    49. 2022 Classification of Dementia EEG Based on Sub-bands Using Time-Frequency Approaches Signal Processing and Communications Applications Conference (SIU) · DOI
    50. 2022 Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications Signal Processing and Communications Applications Conference (SIU) · DOI
    51. 2022 Liking Status Estimation Using EEG Signals and Mode Decomposition Method Signal Processing and Communications Applications Conference (SIU) · DOI
    52. 2021 Classification of Alzheimers’ Dementia by Using Various Signal Decomposition Methods Medical Technologies Congress (TIPTEKNO) · DOI
    53. 2021 Prediction of Sleep Apnea Using EEG Signals and Machine Learning Algorithms Medical Technologies Congress (TIPTEKNO) · DOI
    54. 2021 Synchronization Analysis In Epileptic EEG Signals Via State Transfer Networks Based On Visibility Graph Technique International Journal of Neural Systems · DOI
    55. 2021 A Diagnostic Strategy Via Multiresolution Synchrosqueezing Transform on Obsessive Compulsive Disorder Internatıonal Journal of Neural Systems · DOI
    56. 2021 Classification of Psychogenic Non-epileptic Seizures Using Synchrosqueezing Transform of EEG Signals European Signal Processing Conference (EUSIPCO) · DOI
    57. 2021 Time–frequency signal processing: Today and future Digital Signal Processing · DOI
    58. 2021 An EEG and Machine Learning based Method for the Detection of Major Depressive Disorder Signal Processing and Communications Applications Conference (SIU) · DOI
    59. 2021 Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning International Journal of Neural Systems · DOI
    60. 2021 Channel Contributions of EEG in Emotion Modelling Based on Multivariate Adaptive Orthogonal Signal Decomposition IETE Journal of Research · DOI
    61. 2020 Detection of Consumer Preferences Using EEG Signals International Journal of Applied Mathematics Electronics and Computers · DOI
    62. 2020 A Dynamic Mode Decomposition Based Approach for Epileptic EEG Classification European Signal Processing Conference (EUSIPCO) · DOI
    63. 2020 Epileptic EEG Classification Using Synchrosqueezing Transform with Machine and Deep Learning Techniques European Signal Processing Conference (EUSIPCO) · DOI
    64. 2020 Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum Journal of Applied Biomedicine · DOI
    65. 2020 Classification of attention deficit hyperactivity disorder (ADHD) by using statistical features of MR images Institute of Physics eBooks · DOI
    66. 2020 Classification of Epileptic EEG Signals Using Synchrosqueezing Transform and Machine Learning International Journal of Neural Systems · DOI
    67. 2020 EEG based Epileptic Seizures Detection using Intrinsic Time-Scale Decomposition Medical Technologies Congress (TIPTEKNO) · DOI
    68. 2020 Epileptic EEG Classification Using Synchrosqueezing Transform and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    69. 2020 Individual-based Estimation of Valence with EEG Medical Technologies Congress (TIPTEKNO) · DOI
    70. 2020 Epileptic EEG Classification by Using Advanced Signal Decomposition Methods IntechOpen eBooks · DOI
    71. 2020 An EEG Based Liking Status Detection Method for Neuromarketing Applications Signal Processing and Communications Applications Conference (SIU) · DOI
    72. 2020 Classification of Epileptic EEG Signals Using Dynamic Mode Decomposition Signal Processing and Communications Applications Conference (SIU) · DOI
    73. 2020 EEG Based Mental Workload Estimation System Signal Processing and Communications Applications Conference (SIU) · DOI
    74. 2020 Estimation of Emotion Status Using IAPS Image Data Set Signal Processing and Communications Applications Conference (SIU) · DOI
    75. 2020 EEG-based emotion recognition with deep convolutional neural networks Biomedizinische Technik/Biomedical Engineering · DOI
    76. 2020 Intrinsic Synchronization Analysis of Brain Activity in Obsessive–compulsive Disorders International Journal of Neural Systems · DOI
    77. 2020 Epileptic seizure classifications using empirical mode decomposition and its derivative BioMedical Engineering OnLine · DOI
    78. 2019 Emotional state detection based on common spatial patterns of EEG Signal Image and Video Processing · DOI
    79. 2019 Comparison of IMF Selection Methods in Classification of Multiple Sclerosis EEG Data Medical Technologies Congress (TIPTEKNO) · DOI
    80. 2019 Control of Serious Games Designed for Alzheimer's and Dementia Patients by EEG Signals Medical Technologies Congress (TIPTEKNO) · DOI
    81. 2019 Designing an Obstacle Detection and Alerting System for Visually Impaired People on Sidewalks Medical Technologies Congress (TIPTEKNO) · DOI
    82. 2019 Desıgn of Brain-Computer Interface for Controlling A Virtual Keyboard Medical Technologies Congress (TIPTEKNO) · DOI
    83. 2019 EEG based Emotional State Estimation using 2-D Deep Learning Technique Medical Technologies Congress (TIPTEKNO) · DOI
    84. 2019 Emotion Recognition with Multi-Channel EEG Signals Using Auditory Stimulus Medical Technologies Congress (TIPTEKNO) · DOI
    85. 2019 Emotion Recognition with Multi-Channel EEG Signals Using Visual Stimulus Medical Technologies Congress (TIPTEKNO) · DOI
    86. 2019 Frontal Synchronization Biases in Obsessive-Compulsive Disorders Medical Technologies Congress (TIPTEKNO) · DOI
    87. 2019 Investigation of Emotional Changes Using Features of EEG-Gamma Band and Different Classifiers Medical Technologies Congress (TIPTEKNO) · DOI
    88. 2019 Real Time Emotion Recognition from Facial Expressions Using CNN Architecture Medical Technologies Congress (TIPTEKNO) · DOI
    89. 2019 Selection of Intrinsic Mode Functions for Epileptic EEG Classification Using Ensemble Empirical Mode Decomposition Medical Technologies Congress (TIPTEKNO) · DOI
    90. 2019 Synchrosqueezing transform based feature extraction from EEG signals for emotional state prediction Biomedical Signal Processing and Control · DOI

Researchers

  • Berkay İlber Tunakan

    Research Assistant, MSc Student He graduated from Izmir University of Economics with a degree in Electrical and Electronics Engineering in 2024. In his undergraduate capstone project, he investigated the effects of aircraft noise on attention and cognitive performance using electroencephalography (EEG). His master's research focuses on investigating the neural correlates of perceived brand trust through EEG-based analysis. At the Computational Neuroscience Laboratory, he conducts research in cognitive neuroscience, neuroengineering, and biomedical signal processing. He contributes to the design and implementation of EEG-based experiments, electrophysiological data acquisition, signal preprocessing, and analysis. His research focuses on feature extraction from EEG signals and the application of machine learning and deep learning techniques to model cognitive and affective processes. Additionally, he develops experimental paradigms, stimulus presentation systems, and behavioral data acquisition interfaces using Python. berkay.tunakan@ieu.edu.trberkay.tunakan@std.ieu.edu.trtunakanmvp@gmail.com
    Publications in the lab (3)
    1. 2026 Investigation of the Effect of Product Mascot Gender on Consumer Emotions Using EEG Signals Medical Technologies Congress (TIPTEKNO)
    2. 2026 Analysis of Human Emotions for Different Taste Stimuli Using EEG Signals International Conference on Control, Decision and Information Technologies (CoDIT) · DOI
    3. 2025 Design of a Smart Infusion Pump with Decision Support TIPTEKNO 2025 - Medical Technologies Congress, Proceedings -- 2025 Medical Technologies Congress, TIPTEKNO 2025 -- 26 October 2025 through 28 October 2025 -- Gazi Magusa -- 217812 · DOI

  • Nilsu Çağla Hepsev

    MSc Student She graduated from Izmir University of Economics with a double major in Mechatronics Engineering and Electrical and Electronics Engineering. At the Computational Neuroscience Laboratory, she has conducted research on the evaluation of neurophysiological responses to taste stimuli using EEG signals and machine learning techniques. Her research focuses on cognitive neuroscience, affective computing, and biomedical signal processing. As part of her master's thesis, she investigates the differentiation between perceived and felt emotions elicited by different makams (modal structures) in Turkish music, employing EEG-based analysis, machine learning, and deep learning methods. nilsu.hepsev@std.ieu.edu.trnncagla@gmail.com
    Publications in the lab

    No publications added yet.

  • Uğur Emin Baynal

    MSc Student He graduated from Izmir University of Economics with a double major in Computer Engineering and Electrical and Electronics Engineering. During his undergraduate studies, he served on the board of the IEEE Student Branch and contributed to electric vehicle development, autonomous systems, and rover projects. His research focuses on neuroengineering, biomedical device development, embedded systems, and artificial intelligence. As part of his master's thesis, he is developing a functional near-infrared spectroscopy (fNIRS) system designed to extend spatial recording coverage beyond the conventional forehead-focused approach, enabling measurements across multiple regions of the scalp. His work involves hardware–software integration and the application of embedded artificial intelligence techniques to neurophysiological signal processing. ugur.baynal@ieu.edu.trugur.baynal@std.ieu.edu.tr
    Publications in the lab

    No publications added yet.

  • Burak Akbuğday

    Research Assistant EEG signal analysis with machine and deep learning methods. burak.akbugday@ieu.edu.trburak.akbugday@std.ieu.edu.tr
    Full biography

    His work focuses on analysing EEG signals with machine learning and deep learning methods. He has contributed to a series of studies on detecting olfactory stimuli from EEG and classifying emotional responses to odours for neuromarketing applications. His other topics include detecting attention deficit hyperactivity disorder (ADHD) and Alzheimer's dementia using EEG feature maps and deep learning, the effect of environmental conditions and noise on attention and mental tasks, real-time mood detection in the classroom, and the effect of sustainable product packaging on consumer behaviour.

    Publications in the lab (13)
    1. 2026 Investigating the Effect of Sustainable Product Packaging on Consumer Behavior Using EEG Signals International Conference on Control, Decision and Information Technologies (CoDIT) · DOI
    2. 2025 Classification of Emotions Under Multiple Olfactory Stimuli Using EEG Signals and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    3. 2024 Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    4. 2024 Investigating the Effect of Noise Levels on Mental Tasks Using Artificial Intelligence Medical Technologies Congress (TIPTEKNO) · DOI
    5. 2024 Vital Signs Monitoring Ecosystem for Children Medical Technologies Congress (TIPTEKNO) · DOI
    6. 2024 Detection of Alzheimer's Dementia by Using EEG Feature Maps and Deep Learning European Signal Processing Conference (EUSIPCO) · DOI
    7. 2024 Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods Istanbul University - Journal of Electrical & Electronics Engineering · DOI
    8. 2023 Detection of the Effects of Environmental Condition on Attention/Memory Using EEG Signals Medical Technologies Congress (TIPTEKNO) · DOI
    9. 2023 Combinations of EEG Topographic Feature Maps for the Classification of ADHD European Signal Processing Conference (EUSIPCO) · DOI
    10. 2023 Detection of Attention Deficit Hyperactivity Disorder by Using EEG Feature Maps and Deep Learning European Signal Processing Conference (EUSIPCO) · DOI
    11. 2022 An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in EEG Medical Technologies Congress (TIPTEKNO) · DOI
    12. 2022 Realtime Determination of Mood in Classroom Environment Medical Technologies Congress (TIPTEKNO) · DOI
    13. 2022 Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications Signal Processing and Communications Applications Conference (SIU) · DOI

  • Sude Pehlivan Akbuğday

    Research Assistant EEG-based biomedical signal processing and neuromarketing. sude.pehlivan@ieu.edu.trsude.pehlivan@std.ieu.edu.tr
    Full biography

    Her work focuses on EEG-based biomedical signal processing and neuromarketing. She has contributed to studies on detecting olfactory stimuli from EEG, decoding olfactory EEG with multi-domain features and machine learning, and classifying emotions evoked by odours. Her other topics include detecting ADHD and Alzheimer's dementia with EEG topographic feature maps and deep learning, classifying epileptic EEG with dynamic mode decomposition, and the effect of environmental conditions on attention and memory.

    Publications in the lab (14)
    1. 2025 Classification of Emotions Under Multiple Olfactory Stimuli Using EEG Signals and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    2. 2024 Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    3. 2024 Investigating the Effect of Noise Levels on Mental Tasks Using Artificial Intelligence Medical Technologies Congress (TIPTEKNO) · DOI
    4. 2024 Detection of Alzheimer's Dementia by Using EEG Feature Maps and Deep Learning European Signal Processing Conference (EUSIPCO) · DOI
    5. 2024 Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods Istanbul University - Journal of Electrical & Electronics Engineering · DOI
    6. 2023 Detection of the Effects of Environmental Condition on Attention/Memory Using EEG Signals Medical Technologies Congress (TIPTEKNO) · DOI
    7. 2023 Combinations of EEG Topographic Feature Maps for the Classification of ADHD European Signal Processing Conference (EUSIPCO) · DOI
    8. 2023 Detection of Attention Deficit Hyperactivity Disorder by Using EEG Feature Maps and Deep Learning European Signal Processing Conference (EUSIPCO) · DOI
    9. 2022 An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in EEG Medical Technologies Congress (TIPTEKNO) · DOI
    10. 2022 Realtime Determination of Mood in Classroom Environment Medical Technologies Congress (TIPTEKNO) · DOI
    11. 2022 Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications Signal Processing and Communications Applications Conference (SIU) · DOI
    12. 2020 A Dynamic Mode Decomposition Based Approach for Epileptic EEG Classification European Signal Processing Conference (EUSIPCO) · DOI
    13. 2020 Classification of Epileptic EEG Signals Using Dynamic Mode Decomposition Signal Processing and Communications Applications Conference (SIU) · DOI
    14. 2019 Designing an Obstacle Detection and Alerting System for Visually Impaired People on Sidewalks Medical Technologies Congress (TIPTEKNO) · DOI

  • Dr. Rıza Sadıkzade

    Assistant Professor Neuromarketing and consumer neuroscience, School of Applied Management Sciences, İzmir University of Economics. riza.sadikzade@ieu.edu.trriza@taimaksan.com
    Full biography

    He is with the School of Applied Management Sciences at İzmir University of Economics and contributes to the lab's work in neuromarketing and consumer neuroscience. His topics include detecting olfactory stimuli from EEG and recognising odour-evoked emotions, the effect of product mascot gender and sustainable packaging on consumer emotions and behaviour, and real-time emotion recognition from facial expressions.

    Publications in the lab (8)
    1. 2026 Investigation of the Effect of Product Mascot Gender on Consumer Emotions Using EEG Signals Medical Technologies Congress (TIPTEKNO)
    2. 2026 Investigating the Effect of Sustainable Product Packaging on Consumer Behavior Using EEG Signals International Conference on Control, Decision and Information Technologies (CoDIT) · DOI
    3. 2025 Olfactory Emotion Recognition Using EEG Spectral Topographic Heatmaps and CNNs Medical Technologies Congress (TIPTEKNO) · DOI
    4. 2024 Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI
    5. 2024 Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods Istanbul University - Journal of Electrical & Electronics Engineering · DOI
    6. 2022 An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in EEG Medical Technologies Congress (TIPTEKNO) · DOI
    7. 2022 Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications Signal Processing and Communications Applications Conference (SIU) · DOI
    8. 2019 Real Time Emotion Recognition from Facial Expressions Using CNN Architecture Medical Technologies Congress (TIPTEKNO) · DOI

  • Dr. Faezeh Yeganli

    Assistant Professor EEG-based emotion recognition and deep learning, Department of Electrical and Electronics Engineering, İzmir University of Economics. faezeh.yeganli@ieu.edu.tr
    Full biography

    She is with the Department of Electrical and Electronics Engineering at İzmir University of Economics and works in the lab on analysing EEG responses to olfactory stimuli. She developed a lightweight method that turns EEG signals into band-specific topographic heatmaps and recognises odour-evoked emotions with convolutional neural networks, and has contributed to studies decoding olfactory EEG with multi-domain features and machine learning.

    Publications in the lab (2)
    1. 2025 Olfactory Emotion Recognition Using EEG Spectral Topographic Heatmaps and CNNs Medical Technologies Congress (TIPTEKNO) · DOI
    2. 2024 Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning Medical Technologies Congress (TIPTEKNO) · DOI

Work With Us

We welcome MSc and PhD applicants who want to work on EEG, biosignal processing or machine learning. We are also keen to work with academics on joint projects and professional collaborations.

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