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Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

This study is to examine the correlation between customers’ interactive experience with flower stores and their purchase intentions through the aesthetic experience along with the examination of customers’ brainwaves. Among a total of 56 effective questionnaires, 10 random participants are chosen to give electroencephalogram (EEG) examinations and interviews. Regression analyses showed the explained variance 57.9% and the regression coefficients 0.415 (interactive experience) and 0.422 (esthetic experience). The EEG in interactive experiences varied case by case, but delta tended to increase in aesthetic experiences.

Utilizing electroencephalography (EEG) for emotion recognition enables the direct collection of physiological signals, thereby circumventing potential deception associated with facial expressions and language cues. Concurrently, there has been a widespread adoption of deep learning techniques in this domain, aimed at achieving enhanced results through model and parameter adjustments. An innovative approach is introduced by incorporating the Fast Fourier Transform (FFT) for data preprocessing in conjunction with the CBi-GRU model, merging the Convolutional Neural Network (CNN) and the Bidirectional Gated Recurrent Unit (BiGRU). Comparative analysis underscores the significance of exploring the intrinsic characteristics of the data itself: the application of FFT not only substantially reduces processing time but also enhances model performance. The integration of CBiGRU and FFT leverages the strengths of both CNN and BiGRU methods, resulting in increased accuracy and rapid convergence.

The human brain has several major levels of functioning, e.g.: quantum, genetic, single neuron, neural networks, cognitive, each of them involving a complex dynamic interaction between participating elements and signals. There are also complex dynamic interactions across functional levels [1].

With the advancement of the bioinformatics and brain research technologies more data become available tracing the activity of genes, neurons, neural networks and brain areas over time [2,3,4]. How can such data be used to create a dynamic model that captures dynamic interactions at a particular level over time? How can we integrate dynamic models at different levels? How do we use these models to better understand brain dynamics? These are the main questions addressed in the paper through dynamic interaction network modelling.

Dynamic Interaction Networks (DIN) are popular methods [1] especially for modelling a biological gene regulatory network (GRN) of n genes expressed over time. A node Nj(t) in the model represents the gene Gj(t) activation (expression) at a particular time t and the weighted arcs Wij represent the degree of interaction between genes Gi (i=1,2,…,n) and Gj at two consecutive time moments t and (t+1). In order to evaluate Nj(t+1) a function Fj (Ni, Wij; i=1,2,…,n) is used. The regulatory network model is created through optimisation procedure that optimises the connection weight matrix W, the functions Fj (j=1,2,…,n) from time course gene expression data. There are several methods used so far to create a GRN from time course data.

The paper demonstrates how DIN can be used to model brain dynamics at different levels. At a genetic level, a GRN can be created for a single neuron involving a set of relevant to a particular neuronal function genes. At a next, more complex level, a GRN can be created to capture the dynamics of the expression of several genes related to a complex brain function of a brain area, such as LTP in the CA3 area of the hippocampus. A next level of complexity is to create a model that includes a GRN and matches not only gene expression values over time, but brain signals as well, such as EEG, related to a particular brain function or a disease, e.g. epilepsy. All these models are called computational neurogenetic models [5].

A DIN can also be created from a time series of EEG measurements related to perceptual or cognitive functions, such as visual and auditory perception. The DIN model represents EEG channels as nodes and the connection weight matrix represents the temporal interaction between the brain signals measured by the channels. It is demonstrated that building a DIN model from EEG data can help tracing and discovering hidden brain signal interactions for particular brain functions. Comparing DIN models, built on EEG data measured for different stimuli and different individuals, can also help understand differences in the brain functioning for different perception and cognitive tasks and for different individuals.

The paper demonstrates all the above levels of DIN modelling and illustrates them on real brain data. The paper suggests directions for further research in modelling and discovery of dynamic brain interactions relating to complex brain functions at different functional level.

There is strong evidence pointing to the existence of sudden jumps and apparent discontinuities in spatio-temporal correlates of cognitive activity over the theta frequency band. This topic, however, is highly controversial as it requires revision of our traditional view on brain functions and the nature of intelligence. Human cognition performs a granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehendible chunks of fuzzy concepts and complex behavioral schemas. They are accessed during future action selection and decisions. In this work a dynamical approach to higher cognition and intelligence is presented to interpret experimental findings. In the model, meaningful knowledge is continuously created, processed, and dissipated in the form of sequences of oscillatory patterns of neural activity distributed across space and time. Oscillatory patterns can be viewed as intermittent manifestations of a generalized symbol system, with which brains compute. These patterns are not rigid but flexible and they dissipate soon after they have been generated through spatio-temporal phase transitions. The proposed biologically-motivated computing using dynamic patterns provides an alternative to the notoriously difficult symbol grounding problem and it has been implemented in computational and robotic environments.