Improving Subject-Independent EEG Preference Classification Using Deep Learning Architectures with Dropouts

Teo, Jason; Chew, Lin Hou; Mountstephens, James

doi:10.1007/978-3-030-03402-3_38

Cited by 3 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rectified linear activation function [37] was used with an adaptive learning rate method [38]. The results of this specific part of the study have been previously published [39]. Table I presents the 10-fold cross-validation results obtained from using the various deep net architectures as well as with dropouts and L1 regularization terms.…”

Section: A Preference Classification Resultsmentioning

confidence: 99%

Classification of Affective States via EEG and Deep Learning

Teo¹,

Chew²,

Chia³

et al. 2018

ijacsa

Self Cite

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

Section: A Preference Classification Resultsmentioning

confidence: 99%

Classification of Affective States via EEG and Deep Learning

Teo¹,

Chew²,

Chia³

et al. 2018

ijacsa

Self Cite

View full text Add to dashboard Cite

show abstract

“…The articles that deal with identifying emotion through brain data using different stimuli, such as e-commerce product images [ 8 , 36 ], video ads [ 37 , 38 ], product shapes [ 39 ] etc., have been considered under this cluster. These articles mainly focused on EEG-based emotion identification/preference prediction by adopting different ML techniques to train the models to predict the future preferences of the consumers.…”

Section: Systematic Reviewmentioning

confidence: 99%

“…Yadava et al [ 8 ] proposed a predictive model to catch consumers’ intentions toward E-commerce products. Teo et al [ 39 ] investigated several deep learning (DL) architecture tunings for increasing the classification rate of the preference classification task. Aldayel et al [ 40 ] used several feature sets of EEG indices to explore preference prediction.…”

Section: Systematic Reviewmentioning

confidence: 99%

“…Yadava et al [ 8 ] used images of 14 different products having 3 different varieties of each resulting in 42 product images for stimuli. Teo et al [ 39 ] presented 60 different bracelet-like 3D shapes to the participants to record their EEG. Kumar et al [ 9 ] used 14 online product categories with 3 variants for each category resulting in a total of 42 different product images.…”

Section: Systematic Reviewmentioning

confidence: 99%

“…The EEG signal has a frequency band ranging from around 0.5 to 100 Hz, leading to the common usage of Band Pass Filter (BPF) [ 42 , 56 , 73 , 77 , 92 ], Low Pass Filter (LPF) [ 52 , 54 , 80 ], and High Pass Filter (HPF) [ 72 , 88 , 89 ]. Notch Filter, which has response characteristics quite the opposite of the BPF, is often used to eliminate power line interference (50/60 Hz) [ 39 , 73 , 115 ]. Another filtering technique found in the literature is the Savitzky-Golay (S-Golay) filter, which is used to smooth the signal and increase the precision of the acquired data without changing the tendency of the raw data [ 8 , 9 , 42 ].…”

Section: Systematic Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques

Khondakar,

Sarowar,

Chowdhury

et al. 2024

Brain Inf.

View full text Add to dashboard Cite

Neuromarketing is an emerging research field that aims to understand consumers’ decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.

show abstract