A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

Abstract

Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.

Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion

“…MI can cause event-related desynchronization (ERD) and event-related synchronization (ERS), i.e., power changes in specific frequency bands of EEG signals, specifically sensory-motor rhythms mu (8-13 Hz) and beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). As a result, the band-pass filter of 8-30 Hz is usually used to filter the MI signals [39].…”

“…It is worth noting that the methods mentioned above primarily focus on individual feature selection, potentially missing the complex relationships among features. By the global search in EEG signal processing, wrapper methods for feature selection involve evaluating subsets of features based on the performance of a specific machine learning model, such as Recursive Feature Elimination (RFE) [22] and Genetic Algorithms (GAs) [23]. RFE and GAs not only consider the feature interactions but the least important features are removed until the desired number of features is reached.…”