The influence of fragrances such as perfumes and room fresheners on the psychophysiological activities of humans has been known for a long time, and its significance is gradually increasing in the medicinal and cosmetic industries. A fragrance consists of volatile chemicals with a molecular weight of less than 300 Da that humans perceive through the olfactory system. In humans, about 300 active olfactory receptor genes are devoted to detecting thousands of different fragrance molecules through a large family of olfactory receptors of a diverse protein sequence. The sense of smell plays an important role in the physiological effects of mood, stress, and working capacity. Electrophysiological studies have revealed that various fragrances affected spontaneous brain activities and cognitive functions, which are measured by an electroencephalograph (EEG). The EEG is a good temporal measure of responses in the central nervous system and it provides information about the physiological state of the brain both in health and disease. The EEG power spectrum is classified into different frequency bands such as delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–50 Hz), and each band is correlated with different features of brain states. A quantitative EEG uses computer software to provide the topographic mapping of the brain activity in frontal, temporal, parietal and occipital brain regions. It is well known that decreases of alpha and beta activities and increases of delta and theta activities are associated with brain pathology and general cognitive decline. In the last few decades, many scientific studies were conducted to investigate the effect of inhalation of aroma on human brain functions. The studies have suggested a significant role for olfactory stimulation in the alteration of cognition, mood, and social behavior. This review aims to evaluate the available literature regarding the influence of fragrances on the psychophysiological activities of humans with special reference to EEG changes.

Influence of Fragrances on Human Psychophysiological Activity: With Special Reference to Human Electroencephalographic Response

The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.

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Progress in EEG-Based Brain Robot Interaction Systems.

High-sugar/high-fat foods are related to binge-eating behaviour and especially people with low inhibitory control may encounter elevated difficulties to resist their intake. Incentive sensitization to food-related cues might lead to increased motivated attention towards these stimuli and to cue-induced craving. To investigate the combined influence of olfactory and visual stimuli on craving, inhibitory control and motivated attention, 20 healthy controls and 19 individuals with binge-eating viewed chocolate and neutral pictures, primed by chocolate or neutral odours. Subjective craving and electroencephalogram activity were recorded during the task. N2 and Late Positive Potential (LPP) amplitudes were analysed. Patients reported higher craving than controls. Subjective craving, N2 and LPP amplitudes were higher for chocolate versus neutral pictures. Patients showed a higher relative increase in N2 amplitudes to chocolate versus neutral pictures than controls. Chocolate images induced significant increases in craving, motivated attention and measures of cognitive control. Chocolate odour might potentiate the craving response to visual stimuli, especially in patients with binge-eating.

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Subjective craving and event-related brain response to olfactory and visual chocolate cues in binge-eating and healthy individuals.

An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

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Past, Present, and Future of EEG-Based BCI Applications

Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic components or modes from electroencephalogram (EEG) signals for the development of robust brain–computer interface (BCI) systems. This study proposes a novel automated computerized framework for proficient identification of motor and mental imagery (MeI) EEG tasks by employing empirical Fourier decomposition (EFD) and improved EFD (IEFD) methods. Specifically, the multiscale principal component analysis (MSPCA) is rendered to denoise EEG data first, and then, EFD is utilized to decompose nonstationary EEG into subsequent modes, while the IEFD criterion is proposed for a single conspicuous mode selection. Finally, the time- and frequency-domain features are extracted and classified with a feedforward neural network (FFNN) classifier. Extensive experiments are conducted on four multichannel motor and MeI data sets from BCI competitions II and III using a tenfold cross-validation strategy. Results compared with the other existing methods demonstrated that the highest classification accuracies of 99.82% (data set IV-a), 93.33% (data set IV-b), 91.96% (data set III), and 88.08% (data set V) in subject-specific scenarios, while 82.70% (data set IV-a) in the subject-independent framework are achieved for IEFD with FFNN classifiers collectively. The overall exploratory results authenticate that the proposed IEFD-based automated computerized framework not only outperforms the conventional SD methods but is also robust and computationally efficient for the development of subject-dependent and subject-independent BCI systems.

A New Framework for Automatic Detection of Motor and Mental Imagery EEG Signals for Robust BCI Systems

The aim of the present study was to examine human central nervous system response to three different odors. Electrophysiological activity was recorded in the baseline state and for 3 odors, lemon, peppermint, and vanilla, in 16 healthy participants. Electrodes were separated into groups according to the spatial position on the head. Fast Fourier transformation was performed on every set, and mean value of activity in theta was exported. As theta showed statistically significant results, further analysis was based only on the theta frequency band. On electrodes FP1, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, TP9, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, PO9, and PO10 there was statistically significant difference in the electrical activity of the brain between four conditions. For peppermint and lemon, there was statistically significant difference in activity between different regions-F(1.576, 23.637)=16.030, P=.000 and F(1.362, 20.425)=4.54, P=.035, respectively-where the activity in the central area was significantly reduced compared with the activity in the other 4 areas and in the left and right anterior and left posterior area, respectively. There was no statistically significant difference for vanilla between specific areas, F(1.217, 18.257)=1.155, P=.309. The results indicate that olfactory stimuli can affect the frequency characteristics of the electrical activity of the brain.

Electroencephalographic Response to Different Odors in Healthy Individuals: A Promising Tool for Objective Assessment of Olfactory Disorders.

Brain-computer interface (BCI) is a technology that provides a non-muscular communication channel between a brain and the outside world. Imagination of left and right hand movements results in spatially distinct brain activation patterns that can be used as control signals for the BCI. Motor imagery (MI) results in the attenuation (event related desynchronization, ERD) or enhancement (event related synchronization, ERS) of amplitude in a certain frequency band of electroencephalogram (EEG). This frequency band can vary between different participants. Therefore time-frequency (TF) analysis is performed in order to extract interesting features from EEG. A simple way of performing TF analysis is by using band power features. In this paper we investigate the perspective of Hilbert-Huang transform (HHT) for extracting TF information used for MI classification. HHT is a method that allows calculation of instantaneous frequency and amplitude of the signal. It does that by decomposing the signal into components for which these parameters can be calculated by means of Hilbert transform. We compare classification accuracy of simple band power features and features obtained by means of HHT on BCI competition IV dataset 2b.

Hilbert-Huang Time-Frequency Analysis of Motor Imagery EEG Data for Brain-Computer Interfaces

Brain-computer interfaces (BCIs) allow new ways of communication with computers, independent of the normal neuromuscular pathways. One of the neural mechanisms which can be used in a BCI is the P300 wave. In this paper we focused on the realization of an EEG-based P300 speller application. We used the data set IIb from the BCI Competition 2003. The results achieved with simple methods - using only six electrodes and linear classification - surpass those achieved in the competition. The system achieves full accuracy with four repetitions of the stimuli sequence, and 8.4 seconds are needed to select a symbol.

http://ieeexplore.ieee.org/iel7/6588635/6596208/06596411.pdf

A P300-based brain-computer interface: Towards a simpler approach

Numerous pathologies in medicine are characterized with deficit related to deep sense. Diagnostic methods currently used for testing features of deep sense in individuals are extremely subjective and depend on patients’ co-operability, which is not always possible, especially with patients in coma, patients who are not able to communicate or small children.

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Vibratory Evoked Potentials

The difficulties related to the sense of vibration could be characteristics of the specific neurological disorders, so there is a need for measurable and quantifiable information about its functionality. Currently used methods for testing the sense of vibration do not provide appropriate information, and new method of vibratory stimulation was introduced in the area of the neurophysiological research. Method provides repeatable and quantifiable information about the evoked cortical activity, but there was a need for information about the functional integrity of the whole vibratory sensory pathways. The result of this research was the electrode setup that provides information from the Erb’s point through the levels of the cervical spine to the contralateral sensory area at the cortex. Also, the setup consists only of four electrodes for each side of the stimulation (six electrodes all together, because electrodes positioned on the cervical spinal cord are common for both sides of the body), which reduce time necessary for preparation and allows better use for clinical purposes.

Ana Branka Jerbić

Papers

Electroencephalographic Response to Different Odors in Healthy Individuals: A Promising Tool for Objective Assessment of Olfactory Disorders.

Hilbert-Huang Time-Frequency Analysis of Motor Imagery EEG Data for Brain-Computer Interfaces

A P300-based brain-computer interface: Towards a simpler approach

Vibratory Evoked Potentials

The electrode setup for vibratory evoked potentials