Literature survey on applications of electroencephalography (EEG)

Lai, Chi Qin; Ibrahim, Haidi; Abdullah, Mohd Zaid; Abdullah, Jafri Malin; Suandi, Shahrel Azmin; Azman, Azlinda

doi:10.1063/1.5055472

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…The significant advantage provided by neuromarketing techniques is that these techniques, which collect quantitative data, could be used before the launch of a new product, increasing the likelihood of that product's success [3]. [4]. In the www.ijacsa.thesai.org [10][11], the authors have proposed a predictive modeling method based on EEG signals to understand customer preferences for E-commerce products in terms of "likes" and "dislikes".…”

Section: Introductionmentioning

confidence: 99%

Neuromarketing Solutions based on EEG Signal Analysis using Machine Learning

Ullah¹,

Baloch²,

Ali³

et al. 2022

IJACSA

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Marketing campaigns that promote and market various consumer products are a well-known strategy for increasing sales and market awareness. This simply means the profit of a manufacturing unit would increase. "Neuromarketing" refers to the use of unconscious mechanisms to determine customer preferences for decision-making and behavior prediction. In this work, a predictive modeling method is proposed for recognizing product consumer preferences to online (E-commerce) products as "Likes" and "Dislikes". Volunteers of various ages were exposed to a variety of consumer products, and their EEG signals and product preferences were recorded. Artificial Neural Networks and other classifiers such as Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, and Support Vector Machine were used to perform product-wise and subject-wise classification using a user-independent testing method. Though, the subject-wise classification results were relatively low with artificial neural networks (ANN) achieving 50.40 percent and k-Nearest Neighbors achieving 60.89 percent. Furthermore, the results of product-wise classification were relatively higher with 81.23 percent using Artificial Neural Networks and 80.38 percent using Support Vector Machine.

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Section: Introductionmentioning

confidence: 99%

Neuromarketing Solutions based on EEG Signal Analysis using Machine Learning

Ullah¹,

Baloch²,

Ali³

et al. 2022

IJACSA

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“…Due to the previously annotated increasing demand of EEG devices, the number of available devices is on the rise, and they have many different characteristics [3,10,11]. Moreover, these devices are not only present in the research environment [12][13][14][15][16][17], but also in the entertainment one [18][19][20]. A recent article [21] analyzed the number of electrodes included in devices depending on their design.…”

Section: Introductionmentioning

confidence: 99%

MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices

2021

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Collecting data allows researchers to store and analyze important information about activities, events, and situations. Gathering this information can also help us make decisions, control processes, and analyze what happens and when it happens. In fact, a scientific investigation is the way scientists use the scientific method to collect the data and evidence that they plan to analyze. Neuroscience and other related activities are set to collect their own big datasets, but to exploit their full potential, we need ways to standardize, integrate, and synthesize diverse types of data. Although the use of low-cost ElectroEncephaloGraphy (EEG) devices has increased, such as those whose price is below 300 USD, their role in neuroscience research activities has not been well supported; there are weaknesses in collecting the data and information. The primary objective of this paper was to describe a tool for data management and visualization, called MuseStudio, for low-cost devices; specifically, our tool is related to the Muse brain-sensing headband, a personal meditation assistant with additional possibilities. MuseStudio was developed in Python following the best practices in data analysis and is fully compatible with the Brain Imaging Data Structure (BIDS), which specifies how brain data must be managed. Our open-source tool can import and export data from Muse devices and allows viewing real-time brain data, and the BIDS exporting capabilities can be successfully validated following the available guidelines. Moreover, these and other functional and nonfunctional features were validated by involving five experts as validators through the DESMET method, and a latency analysis was also performed and discussed. The results of these validation activities were successful at collecting and managing electroencephalogram data.

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“…EEG, which measures brain electrical signals from the scalp, is a common neuro-monitoring technique used in both clinical and research settings. Depending on the application, it would be beneficial to evaluate the quality of data and know how clean electrophysiological recordings are before attempting to analyze it or use it as input to a model (Lai et al, 2018). For clinical applications where we need these data to be reliable, consistent, and informative, the presence of noise that corrupt the signal of interest can degrade the effectiveness of diagnostic tools and brain-machine interfaces.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Signal Quality From Unimodal and Multimodal Sources: Application to EEG With Ocular and Motion Artifacts

Nahmias

Kontson

2021

Front. Neurosci.

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With prevalence of electrophysiological data collected outside of the laboratory from portable, non-invasive modalities growing at a rapid rate, the quality of these recorded data, if not adequate, could affect the effectiveness of medical devices that depend of them. In this work, we propose novel methods to evaluate electrophysiological signal quality to determine how much of the data represents the physiological source of interest. Data driven models are investigated through Bayesian decision and deep learning-based methods to score unimodal (signal and noise recorded on same device) and multimodal (signal and noise each recorded from different devices) data, respectively. We validate these methods and models on three electroencephalography (EEG) data sets (N = 60 subjects) to score EEG quality based on the presence of ocular artifacts with our unimodal method and motion artifacts with our multimodal method. Further, we apply our unimodal source method to compare the performance of two different artifact removal algorithms. Our results show we are able to effectively score EEG data using both methods and apply our method to evaluate the performance of other artifact removal algorithms that target ocular artifacts. Methods developed and validated here can be used to assess data quality and evaluate the effectiveness of certain noise-reduction algorithms.

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Literature survey on applications of electroencephalography (EEG)

Cited by 13 publications

References 34 publications

Neuromarketing Solutions based on EEG Signal Analysis using Machine Learning

Neuromarketing Solutions based on EEG Signal Analysis using Machine Learning

MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices

Quantifying Signal Quality From Unimodal and Multimodal Sources: Application to EEG With Ocular and Motion Artifacts

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