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Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

The pressure exerted on the body by clothes is one important factor affecting the comfort of clothing, it is an effective method to evaluate pressure comfort by physiology and psychology. The purpose of this paper is to measure, electroencephalography (EEG), an index of brain activity in order to examine the effect on brain activity conditions caused by oppression exerted by clothing on the body.

EEG power spectrum analysis was conduct to verify the electrophysiological characteristic of brain caused by pressure on the body by girdle.

Experimental results showed that the intensity of α waves in the pressure condition is decreased compared to the non-pressure condition, and the somatosensory activated by pressure of girdle mainly in occipital, frontal and parietal region of brain.

It was clarified that it is impossible to evaluate the clothes pressure by physiological technique of EEG, this study has enriched methods of evaluation pressure comfort.

This paper aims to investigate whether electroencephalography (EEG) technology is effective in qualifying the tactile sensation evoked by non-steady cutaneous electrical stimulation. EEG is a novel method for electrotactile analysis and has demonstrated the discrimination ability for electrotactile sensation under steady contact conditions in recent years. However, in non-steady contact conditions, it is necessary to test its effectiveness. This study aims to explore an objective analysis method in comparison to psychophysical approach and to provide a methodology for non-steady electrotactile research.

With EEG experimentation on 13 volunteers, the authors collected evoked potentials by the predesigned “1” and “0” stimulation events. In addition, with a series of data preprocessing including artifact elimination, band-pass filtering, baseline normalization, data superposition and fast Fourier transform transformation, the authors got the power spectrum of alpha, beta and gamma rhythms. Furthermore, statistics analysis and ANOVA test were adopted for exploring the discrepancy of the spectrum characterizations for different non-steady electrostimulation events.

The EEG power spectrum of the central cortical brain is valuable in discriminating the two types of stimulation events. The power of alpha rhythm especially in the central cortical brain evoked by event “1,” whose current level is equal to the threshold, was significantly lower than that evoked by event “0,” whose level is less than the threshold (p < 0.05). Then, the power of the beta rhythm presented counter-change (p < 0.05). This study suggests that EEG may have the potential to qualify non-steady electrotactile sensation for engineering applications.

Limiting factors of non-steady electrotactile stimulation were considered in this study. Different tapping frequency and contact time should be investigated in future studies.

This paper fulfills a challenge in qualifying the tactile sensations evoked by non-steady electrical stimulation with EEG characteristics.

The purpose of this paper is to analyze the effects of wearing compression pants of varying pressure levels on the wearer’s attention/concentration to investigate the appropriate level of compression for sport performance and confirm whether this methodology is feasible as a means of evaluating sportswear functionality.

After wearing compression pants of varying compression levels, spontaneous potentials were analyzed by calculating the spontaneous electroencephalography (EEG) indices: relative low beta (RLB) power spectrum ((12~15 Hz)/(4~50 Hz)), relative mid beta (RMB) power spectrum ((15~20 Hz)/(4~50 Hz)), and ratio of sensory motor rhythm to theta waves ((12~15 Hz)/(4~8 Hz)). The activation of brain waves was mapped and visualized from EEG data using BioScan-Map (BioBrain Inc., Daejeon, Korea).

The influence of pressure levels on brain waves was confirmed: RLB power, RMB power and RST varied by experimental clothing. CP3, the compression pants that applied moderate pressure (1.57±0.41 kPa), was associated with a relatively higher level of attention/concentration – i.e., the results confirmed that sports compression pants that apply approximately 1.0~2.0 kPa to the area between the thighs and shins are improve attention/concentration. It was further confirmed that EEG is a useful tool for evaluating the psychophysiological effects of functional apparel.

Unlike preceding studies that considered only alpha waves and the effects of clothing on comfort, this study investigated the influence of compression garments on attention/concentration.

Considers the thesis that intelligence differences and EEG‐intelligence correlations can both be explained in terms of differences in the frequency of neural transmission errors. Considers an alternative theory which holds that intelligence variance and correlated EEG variance are both caused by variation of cerebral arousability. Refers to technical and methodological problems that bedevil the EEG‐intelligence literature and measurement difficulties that have arisen through lack of adequate concepts. Concludes that the principal measurement problems derive from failure to appreciate the important distinction that must be made between “cerebral arousal” and “cerebral arousability”; and that any useful EEG‐intelligence concept must go beyond vague and general ideas such as “neural efficiency” or “neural transmission errors” to explain how EEG differences relate to differences in brain function that can account for the main facts recorded in the intelligence literature.

The purpose of this paper is to assess emotional response to music and its EEG correlates in children with autism spectrum disorders (ASD).

Six musical fragments eliciting emotional states of calmness/serenity, sadness and anxiety/fear were presented to children with ASD (n=21, aged 5–9) and typically developing (TD) peers (n=21), while 19-channel EEG was recorded. Emotion self-reports were assessed using visual analogous scales.

Children with ASD assessed most music fragments similarly to their TD peers, with likelihood of EEG oscillatory patterns closely corresponding to emotion self-reports. Somewhat contrary to the expectations, a major difference was observed for one fragment only, which was identified as sad by TD children and adult neurotypical raters, but found “angry and frightening” by children with ASD, with EEG oscillatory response confirming greater cortical activation, particularly for the right hemisphere.

The data suggest that children with ASD may have emotional reactions to music either similar or highly aberrant compared to TD peers, rather than having general difficulties in assessing emotions. The data should be confirmed by further studies, ideally involving high functioning adult autists.

The findings may increase the understanding of autists’ difficulties in perceiving prosodic nuances and reading emotional cues. The results can be taken into consideration when developing music-based interventions.

The findings show that music may be perceived by children with ASD in a unique way, which may be difficult to predict by neurotypical raters.

This paper aims to investigate an approach for mental fatigue detection and estimation of assembly operators in the manual assembly process of complex products, with the purpose of founding the basis for adaptive transfer and demonstration of assembly process information (API), and eventually making the manual assembly process smarter and more human-friendly.

The proposed approach detects and estimates the mental state of assembly operators by electroencephalography (EEG) signal recording and analysis in an engine assembly experiment. When the subjects perform assembly tasks, their EEG signal is recorded by a portable EEG recording system called Emotiv EPOC+ headset. The feature set of the EEG signal is then extracted by calculating its power spectrum density (PSD), followed by data dimension reduction based on principal component analysis (PCA). The dimension-reduced data are classified by using support vector machines (SVMs), and hence, the mental state of assembly operators can be estimated during the assembly process.

The experimental result shows that the proposed approach is able to estimate the mental state of assembly operators within an acceptable accuracy range, and the PCA-based dimension reduction method performs very well by representing the high-dimensional EEG feature set with just a few principal components.

This paper provides theoretical and experimental basis for the API transfer and demonstration based on human cognition. It provides a new idea to seek balance between the improvement of production efficiency and the sustainable utilization of human resources.

The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially, pre-processing is done on EEG signal for quality improvement. Then, by using wavelet transform (WT) feature extraction is done. The artefacts present in the EEG are removed using deep convLSTM. This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.

Nowadays' EEG signals play vital role in the field of neurophysiologic research. Brain activities of human can be analysed by using EEG signals. These signals are frequently affected by noise during acquisition and other external disturbances, which lead to degrade the signal quality. Denoising of EEG signals is necessary for the effective usage of signals in any application. This paper proposes a new technique named as flower pollination fractional calculus optimisation (FPFCO) algorithm for the removal of artefacts from EEG signal through deep learning scheme. FPFCO algorithm is the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM. The existed FPO algorithm is used for solution update through global and local pollinations. In this case, the fractional calculus (FC) method attempts to include the past solution by including the second order derivative. As a result, the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization (FPO) method. Initially, 5 EEG signals are contaminated by artefacts such as EMG, EOG, EEG and random noise. These contaminated EEG signals are pre-processed to remove baseline and power line noises. Further, feature extraction is done by using WT and extracted features are applied to deep convLSTM, which is trained by proposed fractional calculus based flower pollination optimisation algorithm. FPFCO is used for the effective removal of artefacts from EEG signal. The proposed technique is compared with existing techniques in terms of SNR and MSE.

The proposed technique is compared with existing techniques in terms of SNR, RMSE and MSE.

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Technological innovation has made it possible to review how a film cues particular reactions on the part of the viewers. The purpose of this paper is to capture and interpret visual perception and attention by the simultaneous use of eye tracking and electroencephalography (EEG) technologies.

The authors have developed a method for joint analysis of EEG and eye tracking. To achieve this goal, an algorithm was implemented to capture and interpret visual perception and attention by the simultaneous use of eye tracking and EEG technologies. All parameters have been measured as a function of the relationship between the tested signals, which, in turn, allowed for a more accurate validation of hypotheses by appropriately selected calculations.

The results of this study revealed a coherence between EEG and eye tracking that are of particular relevance for human perception.

This paper endeavors both to capture and interpret visual perception and attention by the simultaneous use of eye tracking and EEG technologies. Eye tracking provides a powerful real-time measure of viewer region of interest. EEG technologies provides data regarding the viewer’s emotional states while watching the movie.

The approach in this paper is distinct from similar studies because it takes into account the integration of the eye tracking and EEG technologies. This paper provides a method for determining a fully functional video introspection system.

The purpose of this paper is to assess whether or not electroencephalography (EEG) provides a valuable and substantial contribution to the prediction of consumer behaviour and their preferences during product consumption. In this study, the authors especially focus on individual preferences during a wine tasting experience.

A consumer neuroscience experiment was carried out with 26 participants that evaluated different red wines while their brain activity was recorded with EEG. A within-subjects design was employed and the experiment was carried out in two sessions. All participants took part in a blind taste session (no label session), in which information about the wine was not disclosed, and a normal taste session (label session), during which the bottle and its label were visible.

The findings suggest that EEG is a useful tool to study brain activity during product experience. EEG has high temporal resolution, low costs, small dimensions and superior manoeuvrability compared to other consumer neuroscience tools. However, it is noticed that there is a lack of solid theoretical background regarding brain areas (e.g. frontal cortex) and brain activity (e.g. brain waves) related to consumer preferences during product experience. This lack of knowledge causes several difficulties in replicating and validating the findings of other consumer neuroscience experiments for studying consumer behaviour.

The experiment presented in this paper is an exploratory study. It provides insights into the possible contribution of EEG data to the prediction of consumer behaviour during product experience.