Familiarity effects in EEG-based emotion recognition

Abstract

Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity studies, we allowed subjects to select the same number of familiar and unfamiliar songs; both resulting datasets demonstrated the importance of reporting self-emotion based on the assumption that the emotional state when experiencing music is subjective. We found evidence that music familiarity influences both the power spectra of brainwaves and the brain functional connectivity to a certain level. We conducted an additional experiment using music familiarity in an attempt to recognize emotional states; our empirical results suggested that the use of only songs with low familiarity levels can enhance the performance of EEG-based emotion classification systems that adopt fractal dimension or power spectral density features and support vector machine, multilayer perceptron or C4.5 classifier. This suggests that unfamiliar songs are most appropriate for the construction of an emotion recognition system.

Relieff Matching Feature Selection for Emotion Recognition Based on EEG Signal

“…Momennezhad and A [12] used wavelet transform for feature extraction, and the accuracy rates of the two-class recognition of valence and arousal degree were 0.73 and 0.77, respectively; Lin Jingxin [13][14][15][16] In order to solve the above problems, this paper proposes RMFS algorithm [17][18][19]. This may be achieved by reducing the feature dimension, eliminating the redundant, prioritizing weighting channels and improving the accuracy of emotional recognition for being weighted formula is optimized for the characteristics of the different subjects set of weights, thus optimizing the matching characteristics of the subjects groups.…”

Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier

“…Many existing methods implemented facial expression, speech signals, and self-ratings to classify emotions [3], [4]. However, the systems used in these existing methods usually fail to acknowledge all the detailed emotional inputs for processing, such as the hand gestures or the tone of the voice, thus leading to vague and biased outcome [3].…”

“…However, the systems used in these existing methods usually fail to acknowledge all the detailed emotional inputs for processing, such as the hand gestures or the tone of the voice, thus leading to vague and biased outcome [3]. Some approaches used subjective measurement that can affect the end result as the presence of anomalous trials can be significant [4]. After the high influence of Electroencephalogram (EEG) signals on the field of research, it was observed that human emotion can be represented more accurately with EEG signals than with facial gestures, speech signals, or self-reporting information [5].…”

“…Referring to the previously specified issues [3], [4], [7], [8], a novel methodology, for emotion recognition based on time-frequency analysis, is proposed and evaluated with EEG signals from DEAP dataset [17]. In the proposed model, initially, the EEG signals from only the prefrontal cortex are retrieved for further work since emotional activities occur mainly in the frontal and temporal lobe of the brain [18].…”

EEG Analysis of Person Familiarity with Audio-Video Data Assessing Task

“…Studies [3,4] show that the assessment of the degree of familiarity with the presented material allows much more accurate assessment of the emotional state. Dynamic evaluation of familiarity with the materials, also allows us to estimate the speed of the skills mastering, which require repetition of the material, as shown in [5] using the example of evaluating familiarity with integrated development environments.…”