Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation.

A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient's relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data.

Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters.

For emotion recognition using EEG signals, the challenge is improving accuracy. This study proposes strategies that concentrate on incorporating emotion lateralization and ensemble learning approach to enhance the accuracy of EEG-based emotion recognition. In this paper, we obtained EEG signals from an EEG-based public emotion dataset with four classes (i.e. happy, sad, angry and relaxed). The EEG signal is acquired from pair asymmetry channels from left and right hemispheres. EEG features were extracted using a hybrid features extraction from three domains, namely time, frequency and wavelet. To demonstrate the lateralization, we performed a set of four experimental scenarios, i.e. without lateralization, right-/left-dominance lateralization, valence lateralization and others lateralization. For emotion classification, we use random forest (RF), which is known as the best classifier in ensemble learning. Tuning parameters in the RF model were done by grid search optimization. As a comparison of RF, we employed two prevalent algorithms in EEG, namely SVM and LDA. Emotion classification accuracy increased significantly from without lateralization to the valence lateralization using three pairs of asymmetry channel, i.e. T7-T8, C3-C4 and O1-O2. For the classification, the RF method provides the highest accuracy of 75.6% compared to SVM of 69.8% and LDA of 60.4%. In addition, the features of energy-entropy from wavelet are important for EEG emotion recognition. This study yields a significant performance improvement of EEG-based emotion recognition by the valence emotion lateralization. It indicates that happy and relaxed emotions are dominant in the left hemisphere, while angry and sad emotions are better recognized from the right hemisphere.