Improving subject-independent EEG preference classification using deep learning architectures with dropouts

Abstract

Human preferences play a key role in numerous decision-making processes. The ability to correctly identify likes and dislikes would facilitate novel applications in neuromarketing, affective entertainment, virtual rehabilitation and forensic neuroscience that leverage on sub-conscious human preferences. In this neuroinformatics investigation, we seek to recognize human preferences 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). Our previous work 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 subject-independent 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%–18% through the simple adoption of the use of dropouts compared to a conventional deep learner for EEG preference classification.