Electroencephalogram-Based Preference Prediction Using Deep Transfer Learning

doi:10.1109/ACCESS.2020.3027429

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Electroencephalogram-Based Preference Prediction Using Deep Transfer Learning

2020-01-01

Mashael Aldayel

Mourad Ykhlef

Abeer Al-Nafjan *

DOI：10.1109/ACCESS.2020.3027429

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Abstract

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Transfer learning is an approach in machine learning where a model that was built and trained on one task is re-purposed on a second task. The success of transfer learning in computer vision has motivated its use in neuroscience. Although common in image recognition, the use of transfer learning in EEG classification remains unexplored. Most EEG-based neuroscience studies depend on using traditional machine learning algorithms to answer a question, rather than on improving the algorithms. Developing algorithms for transfer learning for EEG can also assist with problems of low data availability in EEG classification. The primary objective of this study is to investigate EEG-based transfer learning and propose deep transfer learning models to transfer knowledge from emotion recognition to preference recognition to enhance the classification prediction accuracy. To the best of our knowledge, this is the first study demonstrating the effect of applying deep transfer learning between EEG-based emotion recognition and EEG-based preference detection. We propose different approaches for deep transfer learning models to detect preferences from EEG signals using the preprocessed DEAP dataset. Two types of features were extracted from EEG signals, namely the power spectral density and valence. We built three models of deep neural networks: basic without transfer learning, fine-tuning of deep transfer learning, and retraining of deep transfer learning. We compared the performance of deep transfer learning with those of deep neural networks and other conventional classification algorithms such as support vector machine, random forest, and k-nearest neighbor. Although the deep neural network classifiers achieved a high accuracy of greater than 87%, deep transfer learning achieved the highest accuracy result of 93%. The results demonstrate that although the proposed deep transfer learning approaches exhibit higher accuracy than the support vector machine and k-nearest neighbor classifiers, random forest achieves results similar to those of deep transfer learning.

Keywords:

Electroencephalography

Brain modeling

Task analysis

Emotion recognition

Extraterrestrial measurements

Biological neural networks

Cognition

Data mining

brain-computer interfaces

emotion recognition

supervised learning

artificial neural networks

signal processing

consumer behavior

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