Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review

2021 ◽  
Vol 30 (1) ◽  
pp. 19-33
Author(s):  
Annis Shafika Amran ◽  
Sharifah Aida Sheikh Ibrahim ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Nurfaten Hamzah ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Linear Models ◽  
Brain Activity ◽  
Generative Models ◽  
Education Management ◽  
Latent Space ◽  
Latent Space Models ◽  
Cost Feasibility ◽  
Medical Healthcare ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

scholarly journals The relationship between fasting-induced torpor, sleep and waking in laboratory mice

SLEEP ◽  
2021 ◽  
Author(s):  
Yi-Ge Huang ◽  
Sarah J Flaherty ◽  
Carina A Pothecary ◽  
Russell G Foster ◽  
Stuart N Peirson ◽  
...  
Keyword(s):  
Body Temperature ◽  
Rem Sleep ◽  
Brain Activity ◽  
Mammalian Species ◽  
Nrem Sleep ◽  
Laboratory Mice ◽  
State Classification ◽  
Metabolic Suppression ◽  
The Relationship ◽  
Electroencephalogram Eeg

Abstract Study objectives Torpor is a regulated and reversible state of metabolic suppression used by many mammalian species to conserve energy. Whereas the relationship between torpor and sleep has been well-studied in seasonal hibernators, less is known about the effects of fasting-induced torpor on states of vigilance and brain activity in laboratory mice. Methods Continuous monitoring of electroencephalogram (EEG), electromyogram (EMG) and surface body temperature was undertaken in adult, male C57BL/6 mice over consecutive days of scheduled restricted feeding. Results All animals showed bouts of hypothermia that became progressively deeper and longer as fasting progressed. EEG and EMG were markedly affected by hypothermia, although the typical electrophysiological signatures of NREM sleep, REM sleep and wakefulness enabled us to perform vigilance-state classification in all cases. Consistent with previous studies, hypothermic bouts were initiated from a state indistinguishable from NREM sleep, with EEG power decreasing gradually in parallel with decreasing surface body temperature. During deep hypothermia, REM sleep was largely abolished, and we observed shivering-associated intense bursts of muscle activity. Conclusions Our study highlights important similarities between EEG signatures of fasting-induced torpor in mice, daily torpor in Djungarian hamsters and hibernation in seasonally-hibernating species. Future studies are necessary to clarify the effects on fasting-induced torpor on subsequent sleep.

Enhancement of Eye Motor Response and Electrical Brain Activity during Noise and Vibration in Rabbits

1982 ◽  
Vol 90 (1) ◽  
pp. 130-138 ◽  
Author(s):  
Ilmari Pyykkö ◽  
Izuru Matsuoka ◽  
Shinsuke Ito ◽  
Manabe Hinoki
Keyword(s):  
Motor Response ◽  
Optokinetic Nystagmus ◽  
Dorsal Hippocampus ◽  
Brain Activity ◽  
Noise And Vibration ◽  
Implanted Electrodes ◽  
Theta Waves ◽  
Electrical Brain Activity ◽  
The Relationship ◽  
Electroencephalogram Eeg

The relationship between electroencephalogram (EEG) and eye movements was studied in rabbits during optokinetic, vestibular, and optovestibular tests. EEG was recorded through permanently implanted electrodes. Exposure to noise and vibration increased the frequency and the velocity of optokinetic nystagmus (OKN). The increase was greater during vibration but greatest during combined noise and vibration. EEG activity was closely linked to changes in OKN and was particularly evident with the appearance of theta waves in the dorsal hippocampus. Also, rotation of the rabbit produced considerable activation in the EEG.

scholarly journals Incorporating structural knowledge into unsupervised deep learning for two-photon imaging data

2021 ◽  
Author(s):  
Florian Eichin ◽  
Maren Hackenberg ◽  
Caroline Broichhagen ◽  
Antje Kilias ◽  
Jan Schmoranzer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Live Imaging ◽  
Temporal Changes ◽  
Generative Models ◽  
Structural Knowledge ◽  
Imaging Data ◽  
Two Photon ◽  
Latent Space ◽  
Photon Imaging ◽  
Two Photon Imaging

Live imaging techniques, such as two-photon imaging, promise novel insights into cellular activity patterns at a high spatial and temporal resolution. While current deep learning approaches typically focus on specific supervised tasks in the analysis of such data, e.g., learning a segmentation mask as a basis for subsequent signal extraction steps, we investigate how unsupervised generative deep learning can be adapted to obtain interpretable models directly at the level of the video frames. Specifically, we consider variational autoencoders for models that infer a compressed representation of the data in a low-dimensional latent space, allowing for insight into what has been learned. Based on this approach, we illustrate how structural knowledge can be incorporated into the model architecture to improve model fitting and interpretability. Besides standard convolutional neural network components, we propose an architecture for separately encoding the foreground and background of live imaging data. We exemplify the proposed approach with two-photon imaging data from hippocampal CA1 neurons in mice, where we can disentangle the neural activity of interest from the neuropil background signal. Subsequently, we illustrate how to impose smoothness constraints onto the latent space for leveraging knowledge about gradual temporal changes. As a starting point for adaptation to similar live imaging applications, we provide a Jupyter notebook with code for exploration. Taken together, our results illustrate how architecture choices for deep generative models, such as for spatial structure, foreground vs. background, and gradual temporal changes, facilitate a modeling approach that combines the flexibility of deep learning with the benefits of incorporating domain knowledge. Such a strategy is seen to enable interpretable, purely image-based models of activity signals from live imaging, such as for two-photon data.

scholarly journals Machine Learning for Non-Intrusive Speech Quality Assessment

2021 ◽  
Author(s):  
◽  
Mouna Hakami
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Latent Variable ◽  
Generative Models ◽  
Speech Quality ◽  
Speech Signals ◽  
Latent Space ◽  
Speech Quality Assessment

<p><b>This thesis presents two studies on non-intrusive speech quality assessment methods. The first applies supervised learning methods to speech quality assessment, which is a common approach in machine learning based quality assessment. To outperform existing methods, we concentrate on enhancing the feature set. In the second study, we analyse quality assessment from a different point of view inspired by the biological brain and present the first unsupervised learning based non-intrusive quality assessment that removes the need for labelled training data.</b></p> <p>Supervised learning based, non-intrusive quality predictors generally involve the development of a regressor that maps signal features to a representation of perceived quality. The performance of the predictor largely depends on 1) how sensitive the features are to the different types of distortion, and 2) how well the model learns the relation between the features and the quality score. We improve the performance of the quality estimation by enhancing the feature set and using a contemporary machine learning model that fits this objective. We propose an augmented feature set that includes raw features that are presumably redundant. The speech quality assessment system benefits from this redundancy as it results in reducing the impact of unwanted noise in the input. Feature set augmentation generally leads to the inclusion of features that have non-smooth distributions. We introduce a new pre-processing method and re-distribute the features to facilitate the training. The evaluation of the system on the ITU-T Supplement23 database illustrates that the proposed system outperforms the popular standards and contemporary methods in the literature.</p> <p>The unsupervised learning quality assessment approach presented in this thesis is based on a model that is learnt from clean speech signals. Consequently, it does not need to learn the statistics of any corruption that exists in the degraded speech signals and is trained only with unlabelled clean speech samples. The quality has a new definition, which is based on the divergence between 1) the distribution of the spectrograms of test signals, and 2) the pre-existing model that represents the distribution of the spectrograms of good quality speech. The distribution of the spectrogram of the speech is complex, and hence comparing them is not trivial. To tackle this problem, we propose to map the spectrograms of speech signals to a simple latent space.</p> <p>Generative models that map simple latent distributions into complex distributions are excellent platforms for our work. Generative models that are trained on the spectrograms of clean speech signals learned to map the latent variable $Z$ from a simple distribution $P_Z$ into a spectrogram $X$ from the distribution of good quality speech.</p> <p>Consequently, an inference model is developed by inverting the pre-trained generator, which maps spectrograms of the signal under the test, $X_t$, into its relevant latent variable, $Z_t$, in the latent space. We postulate the divergence between the distribution of the latent variable and the prior distribution $P_Z$ is a good measure of the quality of speech.</p> <p>Generative adversarial nets (GAN) are an effective training method and work well in this application. The proposed system is a novel application for a GAN. The experimental results with the TIMIT and NOIZEUS databases show that the proposed measure correlates positively with the objective quality scores.</p>

scholarly journals Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Laura Cornelissen ◽  
Seong-Eun Kim ◽  
Patrick L Purdon ◽  
Emery N Brown ◽  
Charles B Berde
Keyword(s):  
General Anesthesia ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Body Movement ◽  
Video Recording ◽  
Brain State ◽  
Alpha Power ◽  
State Monitoring ◽  
Spatio Temporal ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0–6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4–6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4–6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.

Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

2021 ◽  
pp. 1-10
Author(s):  
Najmeh Pakniyat ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Skin Resistance ◽  
Research Area ◽  
Important Research ◽  
Skin Reactions ◽  
Analysis Technique ◽  
Information Contents ◽  
Important Research Area ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

Applying Independent Component Analysis to the Artifact Detection Problem in Magnetoencephalogram Background Recordings

2008 ◽  
pp. 84-92
Author(s):  
Javier Escudero ◽  
Roberto Hornero ◽  
Daniel Abásolo ◽  
Jesús Poza ◽  
Alberto Fernández
Keyword(s):  
Magnetic Fields ◽  
Quantum Interference ◽  
Brain Activity ◽  
External Noise ◽  
Low Noise ◽  
High Temporal Resolution ◽  
Artifact Detection ◽  
Mental Diseases ◽  
Electroencephalogram Eeg ◽  
The Brain

The analysis of the electromagnetic brain activity can provide important information to help in the diagnosis of several mental diseases. Both electroencephalogram (EEG) and magnetoencephalogram (MEG) record the neural activity with high temporal resolution (Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993). Nevertheless, MEG offers some advantages over EEG. For example, in contrast to EEG, MEG does not depend on any reference point. Moreover, the magnetic fields are less distorted than the electric ones by the skull and the scalp (Hämäläinen et al., 1993). Despite these advantages, the use of MEG data involves some problems. One of the most important difficulties is that MEG recordings may be severely contaminated by additive external noise due to the intrinsic weakness of the brain magnetic fields. Hence, MEG must be recorded in magnetically shielded rooms with low-noise SQUID (Superconducting QUantum Interference Devices) gradiometers (Hämäläinen et al., 1993).

scholarly journals Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yoshihiro Nagano ◽  
Ryo Karakida ◽  
Masato Okada
Keyword(s):  
Activity Pattern ◽  
Latent Variables ◽  
Cluster Structure ◽  
Generative Models ◽  
Cluster Center ◽  
Specific Data ◽  
Global Cluster ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Low Dimensional

Abstract Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer high-quality datasets, such as images. In particular, VAEs can eliminate the noise contained in an image by repeating the mapping between latent and data space. To clarify the mechanism of such denoising, we numerically analyzed how the activity pattern of trained networks changes in the latent space during inference. We considered the time development of the activity pattern for specific data as one trajectory in the latent space and investigated the collective behavior of these inference trajectories for many data. Our study revealed that when a cluster structure exists in the dataset, the trajectory rapidly approaches the center of the cluster. This behavior was qualitatively consistent with the concept retrieval reported in associative memory models. Additionally, the larger the noise contained in the data, the closer the trajectory was to a more global cluster. It was demonstrated that by increasing the number of the latent variables, the trend of the approach a cluster center can be enhanced, and the generalization ability of the VAE can be improved.

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