Aesthetic preference recognition of 3D shapes using EEG

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

doi:10.1007/s11571-015-9363-z

Cited by 76 publications

(53 citation statements)

References 43 publications

(43 reference statements)

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“…Although the answer to what are the most emotion-relevant EEG features is still under investigation, power features from different frequency bands are still the most popular in the context of emotion recognition. Studies [26,197,218] have shown that power spectral density (PSD) extracted from EEG signals performs well on distinguishing affective states.…”

Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

“…[102,123,218,280] Education These articles tracked students' engagement and learning. [38,281] Assistance These articles explored how assistive technologies or learning resources were provided to individuals.…”

Section: Monitoringmentioning

confidence: 99%

“…Because customer feelings in sales areas are strongly influenced by the perception of the surroundings, recognition of emotional responses can reveal true consumer preferences and improve and assist in the (buying) process. Four articles have presented this idea in different applications [102,123,218,280].…”

Section: Domain Description Referencesmentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

Section: Monitoringmentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…We have conducted a number of prior investigations into the use of electroencephalography (EEG) as a method for passively monitoring the brainwaves of users as they are exposed to 3D visual stimuli as well as immersive stimuli and then using different machine learning algorithms to predict their preferences among the various visual stimuli [1], [2]. In the first part our study, we focus on human preference classification.…”

Section: Introductionmentioning

confidence: 99%

“…In our early work with a small set of five test subjects, good classification rates of up to 80% were attained using simple knearest neighbor (kNN) classifiers [1]. However, when the number of test subjects was increased to 16, the noise arising from inter-subject variability became a substantial factor which made the classification process significantly more challenging [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of Affective States via EEG and Deep Learning

Teo¹,

Chew²,

Chia³

et al. 2018

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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.

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