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11 – 20 of over 1000
Article
Publication date: 29 January 2018

Meng-Hsien (Jenny) Lin, Samantha N.N. Cross, William J. Jones and Terry L. Childers

This paper aims to review past papers focused on understanding consumer-related topics in marketing and related interdisciplinary fields to demonstrate the applications of…

4604

Abstract

Purpose

This paper aims to review past papers focused on understanding consumer-related topics in marketing and related interdisciplinary fields to demonstrate the applications of electroencephalogram (EEG) in consumer neuroscience.

Design/methodology/approach

In addition to the review of papers using EEG to study consumer cognitive processes, the authors also discuss relevant decisions and considerations in conducting event-related potential (ERP) studies. Further, a framework proposed by Plassmann et al. (2015) was used to discuss the applications of EEG in marketing research from papers reviewed.

Findings

This paper successfully used Plassmann et al.’s (2015) framework to discuss five applications of neuroscience to marketing research. A review of growing EEG studies in the field of marketing and other interdisciplinary fields reveals the advantages and potential of using EEG in combination with other methods. This calls for more research using such methods.

Research limitations/implications

A technical overview of ERP-related terminology provides researchers with a background for understanding and reviewing ERP studies. A discussion of method-related considerations and decisions provides marketing researchers with an introduction to the method and refers readers to relevant literature.

Practical implications

The marketing industry has been quick to adopt cutting edge technology, including EEG, to understand and predict consumer behavior for the purpose of improving marketing practices. This paper connects the academic and practitioner spheres by presenting past and potential EEG research that can be translatable to the marketing industry.

Originality/value

The authors review past literature on the use of EEG to study consumer-related topics in marketing and interdisciplinary fields, to demonstrate its advantages over-traditional methods in studying consumer-relevant behaviors. To foster increasing use of EEG in consumer neuroscience research, the authors further provide technical and marketing-specific considerations for both academic and market researchers. This paper is one of the first to review past EEG papers and provide methodological background insights for marketing researchers.

Details

European Journal of Marketing, vol. 52 no. 1/2
Type: Research Article
ISSN: 0309-0566

Keywords

Article
Publication date: 25 June 2020

Minghua Wei and Feng Lin

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 20 October 2021

Jayalaxmi Anem, G. Sateeshkumar and R. Madhu

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…

66

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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

Originality/value

100%.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 13 November 2017

Kanupriya Bhardwaj and Eshita Gupta

The key purpose of this paper is to quantify the size of the energy-efficiency gap (EEG) for air conditioners at the household level in Delhi. Most of the studies in the EEG

391

Abstract

Purpose

The key purpose of this paper is to quantify the size of the energy-efficiency gap (EEG) for air conditioners at the household level in Delhi. Most of the studies in the EEG tradition broadly define EEG as the difference between the actual and optimal level of energy efficiency. The optimal level of energy efficiency is defined at the societal level (that weigh social costs against social benefits) and the private level (that weigh private costs against private benefits).

Design/methodology/approach

The authors base the empirical results in this study on the basis of the primary data collected through in-person interviews of the high-income urban households in Delhi in 2014-2015. The sample of 101 households was collected through purposive random sampling. The survey data include information on type and number of AC possessed, hours of operations, socioeconomic characteristics and awareness and habits of households.

Findings

Using primary data of 101 high-income urban household, the paper finds that average EEG is about 10 per cent of total electricity demand of ACs at the household level. The maximum current saving potential measured as a difference between hypothetical energy consumption, if everyone adopts five star ACs, and actual energy consumption is estimated about 14 per cent of the total electricity demand of ACs. Results from the ordinary least squares regressions demonstrate that individual’s habits, attitude, awareness of energy-efficiency measures and perceptions significantly determine the size of the EEG. Among other things, authors’ empirical analysis shows that information can play a central role in guiding investment in energy-efficient technologies. From the analysis of improving access to understandable information about cost savings, payback period and emission reduction, it is found that full information leads to the significant reduction in the size of the expected private energy-efficiency gap from 10 to 2.98 per cent at the household level.

Research limitations/implications

This paper tests the significance of non-economic and non-social factors in determining the size of the EEG. Apart from socioeconomic factors such as income, occupation and education, individual’s energy-conserving habits and attitudes, awareness of energy-efficiency measures and perceptions are other important factors found to have a significant negative impact on the size of the EEG. This is particularly important for the designing of information programs by policymakers for promoting energy-efficiency choices in view of the change that is required in the behavior and attitudes of the households.

Originality/value

In this study, authors try to estimate the size of the EEG of ACs for the high-income urban households in Delhi. The private energy-efficiency gap estimated at 10 per cent of the household demand for ACs indicates existing saving opportunity for the private households. It is found that provision of comprehensive information about cost savings, payback period and emission reduction reduces the size of the EEG significantly from 10 to 2.72 per cent at the private level. This highlights the existence of limited and incomplete information in the market about the possible costs and benefits of energy-efficiency investments. This paper tests the significance of non-economic and non-social factors in determining the size of the energy-efficiency gap. Apart from socioeconomic factors such as income, occupation and education, individual’s energy-conserving habits and attitudes, awareness of energy-efficiency measures and perceptions are other important factors found to have a significant negative impact on the size of the EEG. This is particularly important for the designing of information programs by policymakers for promoting energy-efficiency choices in view of the change that is required in the behavior and attitudes of the households.

Details

Indian Growth and Development Review, vol. 10 no. 2
Type: Research Article
ISSN: 1753-8254

Keywords

Open Access
Article
Publication date: 29 November 2018

Fatemeh Fahimi, Wooi Boon Goh, Tih-Shih Lee and Cuntai Guan

This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in…

1604

Abstract

Purpose

This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in which the strongest correlation with attentive performance (behavioral index) exists. Finally, to further validate the findings, this paper aims at the prediction of different levels of attention function based on the attention score obtained from repeatable battery for the assessment of neurophysiological status (RBANS).

Design/methodology/approach

The present paper analyzes electroencephalogram (EEG) signals recorded by a single prefrontal channel from 105 elderly subjects while they were responding to Stroop color test which is an attention-demanded task. Beside Stroop test, subjects also performed RBANS which provides their level of functionality in different domains including attention. After data acquisition (EEG during Stroop test and RBANS attention score), the authors extract the spectral features of EEG as neural indexes of attention and subjects’ reaction time in response to Stroop test as behavioral index of attention. Then, they explore the correlation between these post-cue frequency band oscillations of EEG with elderly response time (RT). Next, the authors exploit these findings to classify RBANS attention score.

Findings

The observations of this study suggest that there is significant negative correlation between alpha gamma ratio (AGR) and RT (p < 0.0001), theta beta ratio (TBR) is positively correlated with subjects’ RT (p < 0.0001), these correlations are stronger in a 500ms period right after triggering the cue (question onset in Stroop test), and 4) TBR and AGR can be effectively used to predict RBANS attention score.

Research limitations/implications

Because of the experiment design, the pre-cue EEG of the next trail was very much overlapped with the post-cue EEG of the current trail. Therefore, the authors could analyze only post-cue EEG. In future study, it would be interesting to investigate the predictability of subject’s future performance from pre-cue EEG and mental preparation.

Practical implications

This study provides an insight into the research on detection of human attention level from EEG instead of conventional neurophysiological tests. It has also potential to be used in implementation of feasible and efficient EEG-based brain computer interface training systems for elderly.

Originality/value

To the best of the authors’ knowledge, this study is among very few attempts for early prediction of cognitive decline in the domain of attention from brain activity (EEG) instead of conventional tests which are prone to human errors.

Details

International Journal of Crowd Science, vol. 2 no. 3
Type: Research Article
ISSN: 2398-7294

Keywords

Open Access
Article
Publication date: 29 September 2022

Manju Priya Arthanarisamy Ramaswamy and Suja Palaniswamy

The aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG)…

1001

Abstract

Purpose

The aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG), electromyography (EMG), electrodermal activity (EDA), temperature, plethysmograph and respiration. The experiments are conducted on both modalities independently and in combination. This study arranges the physiological signals in order based on the prediction accuracy obtained on test data using time and frequency domain features.

Design/methodology/approach

DEAP dataset is used in this experiment. Time and frequency domain features of EEG and physiological signals are extracted, followed by correlation-based feature selection. Classifiers namely – Naïve Bayes, logistic regression, linear discriminant analysis, quadratic discriminant analysis, logit boost and stacking are trained on the selected features. Based on the performance of the classifiers on the test set, the best modality for each dimension of emotion is identified.

Findings

 The experimental results with EEG as one modality and all physiological signals as another modality indicate that EEG signals are better at arousal prediction compared to physiological signals by 7.18%, while physiological signals are better at valence prediction compared to EEG signals by 3.51%. The valence prediction accuracy of EOG is superior to zygomaticus electromyography (zEMG) and EDA by 1.75% at the cost of higher number of electrodes. This paper concludes that valence can be measured from the eyes (EOG) while arousal can be measured from the changes in blood volume (plethysmograph). The sorted order of physiological signals based on arousal prediction accuracy is plethysmograph, EOG (hEOG + vEOG), vEOG, hEOG, zEMG, tEMG, temperature, EMG (tEMG + zEMG), respiration, EDA, while based on valence prediction accuracy the sorted order is EOG (hEOG + vEOG), EDA, zEMG, hEOG, respiration, tEMG, vEOG, EMG (tEMG + zEMG), temperature and plethysmograph.

Originality/value

Many of the emotion recognition studies in literature are subject dependent and the limited subject independent emotion recognition studies in the literature report an average of leave one subject out (LOSO) validation result as accuracy. The work reported in this paper sets the baseline for subject independent emotion recognition using DEAP dataset by clearly specifying the subjects used in training and test set. In addition, this work specifies the cut-off score used to classify the scale as low or high in arousal and valence dimensions. Generally, statistical features are used for emotion recognition using physiological signals as a modality, whereas in this work, time and frequency domain features of physiological signals and EEG are used. This paper concludes that valence can be identified from EOG while arousal can be predicted from plethysmograph.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 14 January 2022

Ashutosh Shankhdhar, Pawan Kumar Verma, Prateek Agrawal, Vishu Madaan and Charu Gupta

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an…

Abstract

Purpose

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.

Design/methodology/approach

This paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.

Findings

At the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.

Originality/value

BCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

Details

International Journal of Quality & Reliability Management, vol. 39 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 30 December 2021

Satyender Jaglan, Sanjeev Kumar Dhull and Krishna Kant Singh

This work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.

Abstract

Purpose

This work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.

Design/methodology/approach

In this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.

Findings

For the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.

Originality/value

Epilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

Details

International Journal of Intelligent Unmanned Systems, vol. 11 no. 1
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 2 July 2018

Galina Portnova, Alexandra Maslennikova and Anton Varlamov

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Advances in Autism, vol. 4 no. 3
Type: Research Article
ISSN: 2056-3868

Keywords

Article
Publication date: 2 May 2022

Yiran Li, Liyi Zhang, Wen-Lung Shiau, Liyang Xu and Qihua Liu

Reading represents a basic way by which humans understand the world and acquire knowledge; it is also central to learning and communicating. However, with the rapid development of…

Abstract

Purpose

Reading represents a basic way by which humans understand the world and acquire knowledge; it is also central to learning and communicating. However, with the rapid development of mobile reading, an individual's cognition of objective facts may be affected by the reading environment and text genre, resulting in limited memorization and understanding of the reading material. Therefore, this study aimed to investigate the influence of the reading environment and text genre on individuals' cognitive activities from the perspective of motivational activation level using evidence from electroencephalography (EEG) signals.

Design/methodology/approach

The study employed a mixed design experiment with two reading environments (quiet and distracting) between subjects, two text genres (entertaining and scientific) within subjects and two reading tasks (memory recall and comprehension) within subjects. There were 50 participants in the experiment, and the data obtained from 44 participants while they read the materials and completed the reading tasks were analyzed.

Findings

The results showed that readers are more positively motivated to read in a quiet reading environment than in a distracting reading environment when facing the memory recall tasks of entertaining genre passages and comprehension tasks of scientific genre passages. Entertaining genres are more likely to arouse readers' reading interest but hinder the memory recall of the content details. While scientific genres are not easy to understand, they are helpful for working memory.

Originality/value

This study not only applies a new technology to mobile reading research in the field of library science and addresses the limitations of self-report data, but also provides suggestions for the further improvement of mobile reading service providers. Additionally, the results may provide useful information for learners with different learning demands.

Details

Information Technology & People, vol. 36 no. 3
Type: Research Article
ISSN: 0959-3845

Keywords

11 – 20 of over 1000