The brain dynamics in the electroencephalogram (EEG) data are often challenging to interpret, specially when the signal is a combination of desired brain dynamics and noise. Thus, in an EEG signal, anything other than the desired electrical activity, which is produced due to coordinated electrochemical process, can be considered as unwanted or noise. To make brain dynamics more analyzable, it is necessary to remove noise in the temporal location of interest, as well as to denoise data from a specific spatial location. In this paper, we propose a novel method for noisy EEG analysis with accompanying toolbox which includes adaptive, data-driven noise removal technique based on the improved intersection of confidence interval (ICI)-based algorithm. Next, a local entropy-based method for EEG data analysis was designed and included in the toolbox. As shown in the paper, the relative intersection of confidence interval (RICI) procedure retains the dominant dipole activity projected on electrodes, while the local (short-term) Rényi entropy-based analysis of the EEG representation in the time-frequency domain is efficient in detecting the presence of P300 event-related potential (ERP) at specific electrodes. Namely, the P300 are detected as sharp drop of entropy in the temporal domain that enabled accurate calculation of the index of the noise class for the EEG signals.

Instantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike and oscillatory characteristics. Unlike previous methods that use IF alone, the proposed method utilizes information obtained from localized Rényi entropies (LREs) to generate a binary map that automatically identifies regions requiring a different estimation strategy. The method combines IF estimation algorithms for multicomponent signals with time and frequency support information to improve signal ridge estimation in the time–frequency distribution (TFD). Our experimental results indicate the superiority of the proposed combined IF and GD estimation approach over the IF estimation alone, without requiring any prior knowledge about the input signal. The LRE-based mean squared error and mean absolute error metrics showed improvements of up to 95.70% and 86.79%, respectively, for synthetic signals and up to 46.45% and 36.61% for real-life EEG seizure signals.

The affective and cognitive states of the user can be effectively monitored by electroencephalography (EEG). The human taste stimulation and perception produces changes in the EEG signals of the brain at several subdivisions of insula, orbitofrontal cortex and anterior cingulate cortex. In this work we propose a method which can be used to monitor these signals elicited by taste stimulation and perception using a simple gaming EEG device. Emotion elicited by taste recall is stronger than stimulus elicited emotion will be re-established but with a gaming EEG device. The results are presented for a number of subjects and obtained using the research edition of the gaming EEG headset.