Spatial attention-enhanced EEG analysis for profiling consumer choices
Over the years, research in neuroscience-driven marketing has progressively delved into the conscious and subconscious behaviors of consumers. Existing Electroencephalography (EEG)-based studies related to consumer preferences toward products are not comprehensive. Due to non-stationarity issues o...
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my.upm.eprints.1058682024-03-27T01:13:21Z http://psasir.upm.edu.my/id/eprint/105868/ Spatial attention-enhanced EEG analysis for profiling consumer choices Panda, Debadrita Chakladar, Debashis Das Rana, Sudhir Shamsudin, Mad Nasir Over the years, research in neuroscience-driven marketing has progressively delved into the conscious and subconscious behaviors of consumers. Existing Electroencephalography (EEG)-based studies related to consumer preferences toward products are not comprehensive. Due to non-stationarity issues of EEG, a significant variance is observed in inter-trial and inter-session EEG signals of a subject, which leads to challenges in building a universal consumer preference model across diverse subjects, sessions, and tasks. Transfer learning mitigates this challenge by utilizing data or knowledge from similar subjects, sessions, or tasks to improve the learning process for a new subject, session, or task, thereby enhancing overall model performance. Moreover, high-dimensional EEG features often lead to poor classification results. Therefore, selecting meaningful or refined features is of utmost importance for classification. Therefore, we propose a robust EEG-based neuromarketing framework combining deep transfer learning, spatial attention models, and deep neural networks. The proposed framework predicts the consumer choices (in terms of ‘‘likes’’ and ‘‘dislikes’’) for e-commerce products. Initially, the knowledge distillation is performed from the pre-trained network to the proposed model, and the model is trained on the connectivity features of EEG. Next, the attention-based features are extracted from high-level connectivity features using the spatial attention model (Convolutional Block Attention Module: CBAM). CBAM extracts the attention feature maps along channel and spatial dimensions for adaptive feature refinement. The refined features improve the classification accuracy. Finally, the attention-based features are passed to the 2D CNN-based deep learning model to evaluate consumer choices. The proposed model achieves 95.60% classification accuracy with the experimental dataset. The proposed model achieves a significant improvement of 2.60% over the existing neuromarketing-based studies. Institute of Electrical and Electronics Engineers Inc. 2024 Article PeerReviewed Panda, Debadrita and Chakladar, Debashis Das and Rana, Sudhir and Shamsudin, Mad Nasir (2024) Spatial attention-enhanced EEG analysis for profiling consumer choices. IEEE Access, 12. pp. 13477-13487. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10409161 10.1109/ACCESS.2024.3355977 |
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Over the years, research in neuroscience-driven marketing has progressively delved into the
conscious and subconscious behaviors of consumers. Existing Electroencephalography (EEG)-based studies
related to consumer preferences toward products are not comprehensive. Due to non-stationarity issues of
EEG, a significant variance is observed in inter-trial and inter-session EEG signals of a subject, which leads
to challenges in building a universal consumer preference model across diverse subjects, sessions, and tasks.
Transfer learning mitigates this challenge by utilizing data or knowledge from similar subjects, sessions,
or tasks to improve the learning process for a new subject, session, or task, thereby enhancing overall
model performance. Moreover, high-dimensional EEG features often lead to poor classification results.
Therefore, selecting meaningful or refined features is of utmost importance for classification. Therefore,
we propose a robust EEG-based neuromarketing framework combining deep transfer learning, spatial
attention models, and deep neural networks. The proposed framework predicts the consumer choices (in
terms of ‘‘likes’’ and ‘‘dislikes’’) for e-commerce products. Initially, the knowledge distillation is performed
from the pre-trained network to the proposed model, and the model is trained on the connectivity features of
EEG. Next, the attention-based features are extracted from high-level connectivity features using the spatial
attention model (Convolutional Block Attention Module: CBAM). CBAM extracts the attention feature maps
along channel and spatial dimensions for adaptive feature refinement. The refined features improve the
classification accuracy. Finally, the attention-based features are passed to the 2D CNN-based deep learning
model to evaluate consumer choices. The proposed model achieves 95.60% classification accuracy with the
experimental dataset. The proposed model achieves a significant improvement of 2.60% over the existing
neuromarketing-based studies. |
format |
Article |
author |
Panda, Debadrita Chakladar, Debashis Das Rana, Sudhir Shamsudin, Mad Nasir |
spellingShingle |
Panda, Debadrita Chakladar, Debashis Das Rana, Sudhir Shamsudin, Mad Nasir Spatial attention-enhanced EEG analysis for profiling consumer choices |
author_facet |
Panda, Debadrita Chakladar, Debashis Das Rana, Sudhir Shamsudin, Mad Nasir |
author_sort |
Panda, Debadrita |
title |
Spatial attention-enhanced EEG analysis for profiling consumer choices |
title_short |
Spatial attention-enhanced EEG analysis for profiling consumer choices |
title_full |
Spatial attention-enhanced EEG analysis for profiling consumer choices |
title_fullStr |
Spatial attention-enhanced EEG analysis for profiling consumer choices |
title_full_unstemmed |
Spatial attention-enhanced EEG analysis for profiling consumer choices |
title_sort |
spatial attention-enhanced eeg analysis for profiling consumer choices |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2024 |
url |
http://psasir.upm.edu.my/id/eprint/105868/ https://ieeexplore.ieee.org/document/10409161 |
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1795013302761291776 |
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13.18916 |