Spatial attention-enhanced EEG analysis for profiling consumer choices - Panda, Debadrita; Chakladar, Debashis Das; Rana, Sudhir; Shamsudin, Mad Nasir (Institute of Electrical and Electronics Engineers Inc., 2024)
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.