An efficient method for quantifying the multichannel EEG spatial-temporal complexity

The Effect of Time Window Length on EEG-Based Emotion Recognition

Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.

EEG dynamics of experienced Zen meditation practitioners probed by complexity index and spectral measure

Changes in the electroencephalogram (EEG) characteristics in experienced Zen meditation practitioners (n = 23) during 40 minutes of meditation were compared with those in the matched controls (n = 23) taking a rest for 40 minutes. Averaged complexity index ([image omitted] ) evaluation and spectral analysis were measured in three intervals: the first, middle and the last 5-min segments of Zen meditation or relaxing rest. Significant increase in frontal alpha-1 (8-10 Hz) and occipital beta power was found during meditation as compared with the EEG under the rest, whereas an average increase of theta power was observed in the controls. In meditation, brain dynamics exhibited high [image omitted] , which correlated with more beta activity. Control subjects showed no significant change in [image omitted] level. This distinction became more significant during the last 5 minutes of meditation over most electrodes. Deeper meditation state has been reported as having implications of increased beta power that can be more prominent by the approach of [image omitted] estimation. Our results substantiate the idea that long-term training with Zen-Buddhist meditation induces changes in the electro-cortical activity of the brain.