Beyond BCI—Validating a wireless, consumer-grade EEG headset against a medical-grade system for evaluating EEG effects of a test anxiety intervention in school

Khng, Kiat Hui; Mane, Ravikiran

doi:10.1016/j.aei.2020.101106

Cited by 22 publications

(14 citation statements)

References 25 publications

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“…For this reason the authors have investigated the application of smoothing filters to “clinical quality” EEG data in one publication (see in [ 10 ]), and for this study purposes they decided to investigate the application of smoothing filters to “consumer-grade” hardware-acquired EEG data. The authors’ intention was to pinpoint that the “consumer-grade” equipment should be considered separately, and the pursuit of the optimal smoothing filter for an application should be done wisely and while remembering that the “consumer-grade” equipment does not provide “real” raw data [ 58 , 66 ].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

et al. 2021

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Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky–Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.

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Section: Discussionmentioning

confidence: 99%

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

et al. 2021

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“…Finally, we refer to some recent works [ 18 , 19 ] where new types of EEG sensors are presented. These works are interesting in our context because they present a new generation of EEG sensors, namely EEG head band.…”

Section: Related Workmentioning

confidence: 99%

“…In their deep learning framework, a Convolutional Neural Network and a Long Short-Term Memory Recurrent Neural Network are used to classify raw EEG signals based on the EEG artifacts. In [ 19 ], authors have explored the utility of lightweight, affordable and easy-to-set-up EEG systems to use in school-based research with children. The effects of a deep-breathing-for-test-anxiety intervention on brain electrical activity in eleven-year students are examined.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Nandi

Xhafa

Subirats

et al. 2021

Sensors

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In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners’ emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning outcomes. Several machine learning and deep learning approaches have also been proposed to achieve this goal. All such approaches are suitable for an offline mode, where the data for emotion classification are stored and can be accessed infinitely. However, these offline mode approaches are inappropriate for real-time emotion classification when the data are coming in a continuous stream and data can be seen to the model at once only. We also need real-time responses according to the emotional state. For this, we propose a real-time emotion classification system (RECS)-based Logistic Regression (LR) trained in an online fashion using the Stochastic Gradient Descent (SGD) algorithm. The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream. To validate the performance of RECS, we have used the DEAP data set, which is the most widely used benchmark data set for emotion classification. The results show that the proposed approach can effectively classify emotions in real-time from the EEG data stream, which achieved a better accuracy and F1-score than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario.

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“…Prominent examples include the Neurosky, Emotiv, or Muse systems. Researchers have been evaluating and comparing these devices with research-or medical-grade solutions [47][48][49][50][51][52]. In general, the research-or clinical-grade EEGs are more accurate as they employ multichannel wet sensors.…”

Section: Consumer-grade Eegmentioning

confidence: 99%

Brain Exercising Games With Consumer-Grade Single-Channel Electroencephalogram Neurofeedback: Pre-Post Intervention Study

Israsena¹,

Jirayucharoensak²,

Hemrungrojn³

et al. 2021

JMIR Serious Games

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Background The aging population is one of the major challenges affecting societies worldwide. As the proportion of older people grows dramatically, so does the number of age-related illnesses such as dementia-related illnesses. Preventive care should be emphasized as an effective tool to combat and manage this situation. Objective The aim of this pilot project was to study the benefits of using neurofeedback-based brain training games for enhancing cognitive performance in the elderly population. In particular, aiming for practicality, the training games were designed to operate with a low-cost consumer-grade single-channel electroencephalogram (EEG) headset that should make the service scalable and more accessible for wider adoption such as for home use. Methods Our training system, which consisted of five brain exercise games using neurofeedback, was serviced at 5 hospitals in Thailand. Participants were screened for cognitive levels using the Thai Mental State Examination and Montreal Cognitive Assessment. Those who passed the criteria were further assessed with the Cambridge Neuropsychological Test Automated Battery (CANTAB) computerized cognitive assessment battery. The physiological state of the brain was also assessed using 16-channel EEG. After 20 sessions of training, cognitive performance and EEG were assessed again to compare pretraining and posttraining results. Results Thirty-five participants completed the training. CANTAB results showed positive and significant effects in the visual memory (delayed matching to sample [percent correct] P=.04), attention (median latency P=.009), and visual recognition (spatial working memory [between errors] P=.03) domains. EEG also showed improvement in upper alpha activity in a resting state (open-eyed) measured from the occipital area (P=.04), which similarly indicated improvement in the cognitive domain (attention). Conclusions Outcomes of this study show the potential use of practical neurofeedback-based training games for brain exercise to enhance cognitive performance in the elderly population.

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Beyond BCI—Validating a wireless, consumer-grade EEG headset against a medical-grade system for evaluating EEG effects of a test anxiety intervention in school

Cited by 22 publications

References 25 publications

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Brain Exercising Games With Consumer-Grade Single-Channel Electroencephalogram Neurofeedback: Pre-Post Intervention Study

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