Preference Measurement Using User Response Electroencephalogram

Kim, Youngrae; Kang, Kyuchang; Lee, Hyung-Jik; Bae, Changseok

doi:10.1007/978-3-662-45402-2_183

Cited by 10 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A performance comparison between few selected published works and our results is tabulated in Table 5. Tough there have been better performances found in [17,18], our model is quite convenient in performance for such own recorded big database in consideration compared to their databases. As the dataset is diferent, comparison between our model and deep learning based models [19,20] is quite inconvenient.…”

Section: Discussionmentioning

confidence: 99%

EEG‐Based Preference Classification for Neuromarketing Application

Sourov

Ahmed

Opu

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.

show abstract

Section: Discussionmentioning

confidence: 99%

EEG‐Based Preference Classification for Neuromarketing Application

Sourov

Ahmed

Opu

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Fast Fourier transform (FFT) as the feature extraction method was used, and the SVM obtained an accuracy of 82.14% [48]. In another study, the researchers used the FFT with the radial SVMs for the preference classification and obtained an accuracy of 75.44% [49]. The last preference model combines time and frequency by analyzing the power spectrum at the time intervals that cover the entire duration of the post stimuli interval to assess the brain signals.…”

Section: Preference Classification Algorithmsmentioning

confidence: 99%

Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020

View full text Add to dashboard Cite

The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

show abstract

“…Emotion classification for the preference of music via preprocessed features obtained from a using a conventional Fast Fourier Transform (FFT) produced a classification accuracy rate of 85.7% using SVMs [16]. Radial SVMs were used in the only published emotion classification of preferences not using music stimuli, in this case for 2D image preferences using power spectrum analysis where the classification outcome produced an accuracy of 88.5% [17].…”

Section: Extraction Of Features From Eeg Signalsmentioning

confidence: 99%

Classification of Affective States via EEG and Deep Learning

Teo¹,

Chew²,

Chia³

et al. 2018

ijacsa

View full text Add to dashboard Cite

Human emotions play a key role in numerous decision-making processes. The ability to correctly identify likes and dislikes as well as excitement and boredom would facilitate novel applications in neuromarketing, affective entertainment, virtual rehabilitation and forensic neuroscience that leverage on subconscious human affective states. In this neuroinformatics investigation, we seek to recognize human preferences and excitement passively through the use of electroencephalography (EEG) when a subject is presented with some 3D visual stimuli. Our approach employs the use of machine learning in the form of deep neural networks to classify brain signals acquired using a brain-computer interface (BCI). In the first part of our study, we attempt to improve upon our previous work, which has shown that EEG preference classification is possible although accuracy rates remain relatively low at 61%-67% using conventional deep learning neural architectures, where the challenge mainly lies in the accurate classification of unseen data from a cohort-wide sample that introduces inter-subject variability on top of the existing intra-subject variability. Such an approach is significantly more challenging and is known as subjectindependent EEG classification as opposed to the more commonly adopted but more time-consuming and less general approach of subject-dependent EEG classification. In this new study, we employ deep networks that allow dropouts to occur in the architecture of the neural network. The results obtained through this simple feature modification achieved a classification accuracy of up to 79%. Therefore, this study has shown that the use of a deep learning classifier was able to achieve an increase in emotion classification accuracy of between 13% and 18% through the simple adoption of the use of dropouts compared to a conventional deep learner for EEG preference classification. In the second part of our study, users are exposed to a roller-coaster experience as the emotional stimuli which are expected to evoke the emotion of excitement, while simultaneously wearing virtual reality goggles, which delivers the virtual reality experience of excitement, and an EEG headset, acquires the raw brain signals detected when exposed to this excitement stimuli. Here, a deep learning approach is used to improve the excitement detection rate to well above the 90% accuracy level. In a prior similar study, the use of conventional machine learning approaches involving k-Nearest Neighbour (kNN) classifiers and Support Vector Machines (SVM) only achieved prediction accuracy rates of between 65% and 89%. Using a deep learning approach here, rates of 78%-96% were achieved. This demonstrates the superiority of adopting a deep learning approach over other machine learning approaches for detecting human excitement when immersed in an immersive virtual reality environment.

show abstract

Preference Measurement Using User Response Electroencephalogram

Cited by 10 publications

References 8 publications

EEG‐Based Preference Classification for Neuromarketing Application

EEG‐Based Preference Classification for Neuromarketing Application

Deep Learning for EEG-Based Preference Classification in Neuromarketing

Classification of Affective States via EEG and Deep Learning

Contact Info

Product

Resources

About