@conference{oikonomouprediction,

title = {Prediction of Successful Memory Formation during Audiovisual advertising using EEG signals},

author = {Vangelis P Oikonomou and Kostas Geordiadis and Fotis P Kalaganis and Spiros Nikolopoulos and Ioannis Kompatsiaris},

url = {https://ieeecai.org/2024/wp-content/pdfs/540900b114/540900b114.pdf},

year  = {2024},

date = {2024-06-25},

organization = {2024 IEEE Conference on Artificial Intelligence (CAI)},

abstract = {The prediction of memory performance using EEG signals is an active research area in Passive Brain Computer Interfaces community. In this type of prediction problem it is important to be able to use unlabeled data and to tackle the unbalanced nature of the data. In this work we propose two new Sparse Representation Classification schemes that are able to address the above properties of the data. The proposed classifiers have been tested in an EEG dataset related to neuromarketing. In the data analysis, we define a binary classification problem using EEG signals corresponding to the condition of remembering and forgetting. Furthermore, we compare the proposed classifiers with well-known classifiers. The obtained results show all classifiers perform above chance level, and, among them the proposed classifiers present the best performance in terms of Fscore and Kappa Value.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{georgiadis2023harneshing,

title = {Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry},

author = {Kostas Georgiadis and Fotis P Kalaganis and Vangelis P Oikonomou and Spiros Nikolopoulos and Nikos A Laskaris and Ioannis Kompatsiaris},

url = {https://link.springer.com/chapter/10.1007/978-3-031-43075-6_3},

year  = {2023},

date = {2023-09-13},

urldate = {2023-01-01},

booktitle = {International Conference on Brain Informatics},

pages = {21–32},

organization = {Springer},

abstract = {Neuromarketing exploits neuroimaging techniques to study consumers’ responses to various marketing aspects, with the goal of gaining a more thorough understanding of the decision-making process. The neuroimaging technology encountered the most in neuromarketing studies is Electroencephalography (EEG), mainly due to its non-invasiveness, low cost and portability. Opposed to typical neuromarketing practices, which rely on signal-power related features, we introduce an efficient decoding scheme that is based on the principles of Riemannian Geometry and realized by means of a suitable deep learning (DL) architecture (i.e., SPDNet). We take advantage of a recently released, multi-subject, neuromarketing dataset to train SPDNet under the close-to-real-life scenario of product selection from a supermarket leaflet and compare its performance against standard tools in EEG-based neuromarketing. The sample covariance is used as an estimator of the ‘quasi-instantaneous’, brain activation pattern and derived from the multichannel signal recorded while the subject is gazing at a given product. Pattern derivation is followed by proper re-alignment to reduce covariate shift (inter-subject variability) before SPDNet casts its binary decision (i.e., “Buy”-“NoBuy”). The proposed decoder is characterized by sufficient generalizability to derive valid predictions upon unseen brain signals. Overall, our experimental results provide clear evidence about the superiority of the DL-decoder relatively to both conventional neuromarketing and alternative Riemannian Geometry-based approaches, and further demonstrate how neuromarketing can benefit from recent advances in data-centric machine learning and the availability of relevant experimental datasets.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{chanpornpakdiclustering,

title = {Clustering of advertising images using electroencephalogram},

author = {Ingon Chanpornpakdi and Motoi Noda and Toshihisa Tanaka and Yuval Harpaz and Amir B Geva},

url = {http://www.apsipa.org/proceedings/2022/APSIPA%202022/TuAM1-8/1570840214.pdf},

year  = {2022},

date = {2022-11-07},

booktitle = {Proceedings of 2022 APSIPA Annual Summit and Conference},

abstract = {Packaging and advertisements of brands affect customers’ decision-making on purchasing products and could lead to business loss. Hence, neuromarketing, the application of neuroscience in the marketing field, is introduced aiming to understand customers’ cognitive functions toward advertisements or products. Our study focused on identifying how the brain respond to different types of advertising image of the same brand were perceived using electroencephalogram (EEG). We performed an experiment using 33 different Coca-Cola advertising images in RSVP (rapid serial visual presentation) task on 23 participants. A seven channels EEG dry headset was used to record the visual event-related potential (ERP), specifically, the positive peak found at 300 to 700 ms after image onset; P300, to compare the perception response. We applied k-means and hierarchical clustering to the obtained EEG data, and achieved the best clustering for three clusters, yielding different P300 amplitudes and latencies. The typical Coca-Cola ads, red color with Cola-cola text on the ads, induced a faster and larger response, implying better perception than the unconventional or black color ads. We conclude that ERP clustering may be a useful tool for neuromarketing. However, the relationship between the EEG-based cluster and the image-based cluster should be further investigated to confirm the suggestion.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{van2018eeg,

title = {EEG Technology for UX Evaluation: A Multisensory Perspective},

author = {Marieke Van Camp and Muriel De Boeck and Stijn Verwulgen and Guido De Bruyne},

url = {https://link.springer.com/chapter/10.1007/978-3-319-94866-9_34},

year  = {2018},

date = {2018-06-28},

booktitle = {International Conference on Applied Human Factors and Ergonomics},

volume = {775},

pages = {337--343},

publisher = {Advances in Intelligent Systems and Computing },

organization = {Springer},

abstract = {Along with a growing interest in experience-driven design, interest in measuring user experience has progressively increased. This study explores the use of EEG for empirical UX evaluation. A first experimental test was conducted to measure and understand the effect of sensory stimuli on the user experience. A first experimental test was carried out with eight participants. A series of videos, eliciting positive and negative emotional responses, were presented to the participants. Subsequently, auditory stimuli were introduced and the effect on the user experience was evaluated using EEG measurements techniques and analysis software. After the tests the participants were questioned to verify whether the subjective results matched the objective measurements.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}