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Electroencephalography (EEG) is well known method of recording electrical brain activity with electrodes placed along the scalp. One of the challenging tasks in this field is the removal of electrical signals that are not related to brain activity.In this paper, an algorithm for the removal of the EEG signals corresponding to the eye blink artifacts is presented. The presented algorithm is based on ADJUST artifact removing tool, which uses independent component analysis (ICA) for signal decomposition. For every signal component returned by the ICA algorithm, temporal-spatial features are calculated, upon which every independent component is classified as artifact or non-artifact, and removed accordingly.

In this paper, generation of oscillatory synthetic signal simulating brain network dynamics is proposed. The methods follows the expectations of brain network connectivity, which include expressed phase angle differences and signal mixture. We have generated thirty-two signals simulating two groups of sixteen electrodes. Each signal is a mixture of two signal components. The first signal component is synchronized within a group of electrodes but delayed to obtain different phase angles, while the second signal component is unique to each electrode. This simulation give us possibility to present that all brain regions or sources are not connected with each other and that the phase difference between connected oscillators (sources or channels) is not necessary close to zero and could be arbitrary. Phase locking value (PLV) and phase lag index (PLI) measures of connectivity have been calculated on the synthetic signals.

This paper describe the analyses of EEG data during and after infrared (IR) stimulation of a head. IR stimulation is a type of photobiomodulation (PBM) therapy, a non-invasive form of therapy that uses light energy to start non – thermal photochemical processes in the cellular structures. IR LEDs at 850 nm were used to generate stimulation modulated with 10 Hz square wave. Spectrum of EEG signals were analyzed, with focus on Alpha frequency band that includes frequency of IR light modulation. Frontal, central, parietal, and occipital electrode sites were taken in consideration in three healthy adults. Changes in Alpha frequency band were observed during stimulation and immediately after (short–term effect). There were no changes between consecutive measurements (long–term effect). Most pronounced change of spectrum was measured for Alpha band and lower frequencies.

EEG data signal processing has been performed in order to outline the parameters for the comparison between participants with autism (5 subjects) and typically developing children acting as the control group (14 subjects). Based on the previous studies, the autistic group has a significantly higher delta/alpha ratio than the typical developing children. In this paper, we have tested this hypothesis on new participants. Furthermore, statistical features such as mean value, grand-mean value and their ratios have been calculated. The research was conducted by observing signals on electrodes F3, Fz, F4, C3, Cz, C4, P3, Pz and P4. We observed power delta-alpha, theta-high beta, theta-low beta, theta-alpha ratio, grand mean values of magnitude spectrum and 1st moments of the grand mean values of the spectral distribution. The statistically significant difference between observed groups we found when tested 1st moments of the grand mean values of the spectral distribution.

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

This paper describes a communication system, designed to help people with disabilities to communicate with others. In this case, it is built on Event Related Potentials (ERP) which occur as a response to stimuli. The stimuli are auditory answers to the asked questions, where one of the stimulus is the correct answer. The stimulus which is the possible correct answer is the target stimulus and other stimuli are considered as non- targets. The only reliable information available is the onset of the stimuli, based on this information the spatio-temporal pattern for the target and non-targets is expected to be uncorrelated to the ongoing brain activity and with each other. This allows us to train the spatial filter coefficients for the know targets, so that it can use this trained data set to correctly classify targets. Results are obtained by optimizing the spatial filter parameters prior to classification, and the classification results are analyzed for several healthy participants.

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.