• Corpus ID: 54789899

Preference Classification Using Electroencephalography (EEG) and Deep Learning

  title={Preference Classification Using Electroencephalography (EEG) and Deep Learning},
  author={Jason Teo and Chew Lin Hou and James Mountstephens},
  journal={Journal of Telecommunication, Electronic and Computer Engineering},
Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based… 

Figures and Tables from this paper

Recognition of Consumer Preference by Analysis and Classification EEG Signals

The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.

Deep learning-based electroencephalography analysis: a systematic review

This work reviews 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain–computer interfacing, and cognitive and affective monitoring, to extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.

Feature selection of EEG signals in neuromarketing

The role of feature selection in BCI is investigated to improve the accuracy of preference detection for neuromarketing and it was found that feature selection for EEG signals improves the performance of all classifiers.

Deep Learning for EEG-Based Preference Classification in Neuromarketing

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, and results demonstrated that random forest reaches similar results to deep learning on the same dataset.

Elderly’s preferences towards rehabilitation robot appearance using electroencephalogram signal

Based on the electroencephalogram signal and experimental results, it provides the possibility for objective preference evaluation of the elderly to the robot designed features.

Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

The rich potential for computational neuroaesthetic to take advantages from both neuroaesthetics and computational aesthetics is outlined and some of the challenges and potential prospects are discussed.

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

Three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes.

EEG Signals Based Choice Classification for Neuromarketing Applications



Emotional state classification from EEG data using machine learning approach

Emotion recognition of EEG underlying favourite music by support vector machine

An EEG-based brain computer interface (BCI) music player is presented, which can simultaneously analyse brain activities in real time and objectively provide therapists with physiological data for emotion detection in the experiment, and shows that more than 80% accuracy of elicited emotion is shown.

Classification of human emotion from EEG using discrete wavelet transform

The average classification rate and subsets of emotions classification rate of two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA), are presented for justifying the performance of the emotion recognition system.

Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis

This paper focuses on the discrimination between subjects' electroencephalogram (EEG) responses to self-assessed liked or disliked music by evaluating different feature extraction approaches and classifiers to this end and provides early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.

Affective state recognition from EEG with deep belief networks

  • Kang LiXiaoyi LiYuan ZhangA. Zhang
  • Computer Science
    2013 IEEE International Conference on Bioinformatics and Biomedicine
  • 2013
A novel Deep Belief Networks (DBN) based model for affective state recognition from EEG signals that can successfully handle the aforementioned two challenges and significantly outperform the baselines is proposed.

Emotion classification based on gamma-band EEG

  • Mu LiBao-Liang Lu
  • Computer Science
    2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2009
The experimental results indicate that the gamma band (roughly 30–100 Hz) is suitable for EEG-based emotion classification, and a frequency band searching method to choose an optimal band into which the recorded EEG signal is filtered is proposed.

Extraction of User Preference for Video Stimuli Using EEG‐Based User Responses

The result proves that the proposed approach for classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores is sufficient for employment in personalized video segmentation with high accuracy and classification power.

EEG-based emotion classification using deep belief networks

The experimental results show that the DBN and DBN-HMM models improve the accuracy of EEG-based emotion classification in comparison with the state-of-the-art methods.

Common frequency pattern for music preference identification using frontal EEG

The experimental results show evidence that frontal EEG signal contains sufficient information to discriminate preference of music, and frequency band optimization results indicate that gamma band is essential for EEG-based music preference identification.

Affect recognition based on physiological changes during the watching of music videos

A novel asymmetry index based on relative wavelet entropy for measuring the asymmetry in the energy distribution of EEG signals, which is used for EEG feature extraction and classification systems based on EEG and peripheral physiological signals are presented.