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<p>In this study, we propose a hybrid decoding scheme for classifying consumer intent in a binary decision-making scenario (“Buy” vs. “NoBuy”), using simultaneous electroencephalography (EEG) and eye-tracking data. The proposed framework integrates graph signal processing-based features derived from EEG functional connectivity with descriptive statistics from eye movement patterns. Given the imbalanced nature of the targeted classification task, the performance of the proposed hybrid scheme is being assessed at the individual subject level via the employment of Cohen’s kappa and F1-score metrics, both of which are well-suited for handling class imbalance by accounting for agreement beyond chance and balancing precision and recall, respectively. The reported results showcase the superiority of the proposed hybrid decoding scheme, as the averaged scores for both Cohen’s kappa and F1-score are exceeding (with statistical significance at 0.05) the presented competing approaches by 0.08–0.30 and 0.06–0.23 respectively. Additionally, our connectivity analysis confirmed two key findings: (i) strong couplings were consistently observed between electrodes spanning distinct brain regions, such as the prefrontal and occipital cortices, in addition to the commonly reported frontal dipoles; and (ii) the most salient functional connections varied across individuals, with only a limited subset shared among subjects. These results highlight the potential of multimodal decoding approaches and subject-specific connectivity patterns in advancing the classification of consumer decision behavior.</p>

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A hybrid neuromarketing approach exploiting EEG graph signal processing and gaze dynamic patterning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月23日

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  • Fotis P. Kalaganis,
  • Kostas Georgiadis,
  • Vangelis P. Oikonomou,
  • Nikos A. Laskaris,
  • Spiros Nikolopoulos,
  • Ioannis Kompatsiaris

摘要

In this study, we propose a hybrid decoding scheme for classifying consumer intent in a binary decision-making scenario (“Buy” vs. “NoBuy”), using simultaneous electroencephalography (EEG) and eye-tracking data. The proposed framework integrates graph signal processing-based features derived from EEG functional connectivity with descriptive statistics from eye movement patterns. Given the imbalanced nature of the targeted classification task, the performance of the proposed hybrid scheme is being assessed at the individual subject level via the employment of Cohen’s kappa and F1-score metrics, both of which are well-suited for handling class imbalance by accounting for agreement beyond chance and balancing precision and recall, respectively. The reported results showcase the superiority of the proposed hybrid decoding scheme, as the averaged scores for both Cohen’s kappa and F1-score are exceeding (with statistical significance at 0.05) the presented competing approaches by 0.08–0.30 and 0.06–0.23 respectively. Additionally, our connectivity analysis confirmed two key findings: (i) strong couplings were consistently observed between electrodes spanning distinct brain regions, such as the prefrontal and occipital cortices, in addition to the commonly reported frontal dipoles; and (ii) the most salient functional connections varied across individuals, with only a limited subset shared among subjects. These results highlight the potential of multimodal decoding approaches and subject-specific connectivity patterns in advancing the classification of consumer decision behavior.