• Corpus ID: 54789899

Preference Classification Using Electroencephalography (EEG) and Deep Learning

@article{Teo2018PreferenceCU,
  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},
  year={2018},
  volume={10},
  pages={87-91}
}
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… 

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References

SHOWING 1-10 OF 25 REFERENCES
Emotional state classification from EEG data using machine learning approach
Emotion recognition of EEG underlying favourite music by support vector machine
This study aims to research the relationship between electroencephalography (EEG) at the prefrontal cortex (PFC) and emotion in the condition of different preference levels of music by applying a
EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation
TLDR
A deep learning network (DLN) is proposed to discover unknown feature correlation between input signals that is crucial for the learning task and provides better performance compared to SVM and naive Bayes classifiers.
Classification of human emotion from EEG using discrete wavelet transform
In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has
Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis
TLDR
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
  • K. Li, Xiaoyi Li, Yuan Zhang, A. Zhang
  • Computer Science
    2013 IEEE International Conference on Bioinformatics and Biomedicine
  • 2013
TLDR
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 Li, Bao-Liang Lu
  • Psychology, Medicine
    2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2009
TLDR
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
TLDR
The proposed classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification.
EEG-based emotion classification using deep belief networks
TLDR
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
In this paper, we investigate the use of 2-channel frontal EEG signal to classify two music preferences: like and dislike. The hypothesis for this investigation is that the frontal EEG signal
...
1
2
3
...