Fundamentals of EEG Signal Processing

Sanei, Saeid; Chambers, Jonathon A.

doi:10.1002/9780470511923.ch2

Cited by 175 publications

(217 citation statements)

References 92 publications

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“…Therefore they are thought to be the main-source EEG signal. By comparison, deep brain activity is more difficult to detect because the voltage fields drop by a factor of the square of distance [31].…”

Section: Eeg Characteristicsmentioning

confidence: 99%

“…When this wave reaches the electrodes on the scalp it creates the difference in voltages between any two electrodes, and can be measured by a voltmeter which gives us the EEG [31]. Because the human head is composed of a number of layers including the scalp, skull, brain and others in between, only the synchronous activity of a large number of active neurons can generate enough potential to be picked up by EEG.…”

Section: Eeg Characteristicsmentioning

confidence: 99%

“…The amplitudes and frequencies of EEG signals change according to the state of a human such as consciousness or unconsciousness [31]. There are five major brainwave patterns differentiated by their frequency ranges including delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) (see Figure 1).…”

Section: Rhythms Of Eegmentioning

confidence: 99%

“…Gamma waves usually have low amplitudes, rare occurrence, and relate to left index finger, right toes, and tongue movement [31].…”

Section: R E T R a C T E Dmentioning

confidence: 99%

“…In this paper we propose to improve and advance the current state of biometric-based random number generators from EEG source which has disadvantages of low sampling rate and low resolution, but has several advantages: 1) it is noisy, complex and chaotic, and non-linear in nature [1,28,31], 2) it is very difficult to mimic because similar mental tasks are person dependent, and 3) it is almost impossible to steal because the brain activity is sensitive to the stress and the mood of the person: an aggressor cannot force the person to reproduce his/her mental pass-phrase [20].…”

Section: R E T R a C T E Dmentioning

confidence: 99%

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Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

Nguyen

Tran

et al. 2018

JCSM

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Current electroencephalogram (EEG)-based methods in security have been mainly used for person authentication and identification purposes only. The non-linear and chaotic characteristics of EEG signal have not been taken into account. In this paper, we propose a new method that explores the use of these EEG characteristics in generating random numbers. EEG signal and its wavebands are transformed into bit sequences that are used as random number sequences or as seeds for pseudo-random number generators. EEG signal has the following advantages: 1) it is noisy, complex, chaotic and non-linear in nature, 2) it is very difficult to mimic because similar mental tasks are person dependent, and 3) it is almost impossible to steal because the brain activity is sensitive to the stress and the mood of the person and an aggressor cannot force the person to reproduce his/her mental pass-phrase. Our experiments were conducted on the four EEG datasets: AEEG, Alcoholism, DEAP and GrazA 2008. The randomness of the generated bit sequences was tested at a high level of significance by comprehensive battery of tests recommended by the National Institute of Standard and Technology (NIST) to verify the quality of random number generators, especially in cryptography application. Our experimental results showed high average success rates for all wavebands and the highest rate is 99.17% for the gamma band.

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Section: Eeg Characteristicsmentioning

confidence: 99%

Section: Eeg Characteristicsmentioning

confidence: 99%

Section: Rhythms Of Eegmentioning

confidence: 99%

“…Gamma waves usually have low amplitudes, rare occurrence, and relate to left index finger, right toes, and tongue movement [31].…”

Section: R E T R a C T E Dmentioning

confidence: 99%

Section: R E T R a C T E Dmentioning

confidence: 99%

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Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

Nguyen

Tran

et al. 2018

JCSM

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Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

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Autonomous vehicles (AVs) will soon become a reality with almost every major automotive original equipment manufacturer (OEM) such as Tesla, BMW, Daimler, and big technology giants such as Google's Waymo and Apple Car pushing toward the full self-driving objective. According to the SAE J3016 taxonomy [1] of autonomous driving, there are six levels of automated driving systems ranging from level 0 (completely manual) to level 5 (full selfdriving [FSD]) automated systems that are expected to function in all geographic locations, all weather conditions, and under all conditions. The proposed benefits of intelligent vehicles include fewer road accidents, enhanced safety, reduced traffic congestion, effective commute time usage, and importantly enjoyable and comfortable ride. With increased autonomy, the drivers also assume the role of mere passengers who are engaged in nondriving activities and are unavailable to participate in traffic interactions. This would increase complexity in a mixed-autonomy traffic environment as interactions with pedestrians and cyclists are based on visual cues with the driver. Therefore, intelligent vehicles also need to autonomously interact with other traffic participants such as pedestrians and cyclists and other vehicles.Human-vehicle interaction (HVI) is closely related to the field of human-robot interaction (HRI). It entails the problem of understanding and shaping the interaction dynamics between humans and vehicles. Particularly, the domain of interaction involves the study of sensation, perception, information exchange, inference, and decision-making. [2] With increasing levels of automation, the driver will have more time and choice to perform various tasks other than driving and this opens new avenues for interaction. As a result, a growing number of sensors are being incorporated into the vehicles to understand the driver's and/or passenger's actions, emotions, and personal choices to offer the precise functionalities and services for an enjoyable ride. [3] Figure 1 shows the schematic of in-vehicle interaction consisting of various interactive interfaces and sensing modalities present in the vehicle as well as some modes of interaction such as gestures, speech, and eye gaze.The advances in HVI have been presented in previous review articles, which have mainly focused on the detection of particular characteristics for driving assistance such as driver attention detection, [4] emotion recognition, [5] drowsiness detection, [6] mental workload, [7] and so on. A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external

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Consumer neuroscience for marketing researchers

Harris

Ciorciari

Gountas

2018

J of Consumer Behaviour

146

152

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Purpose For many consumer neuromarketing researchers, the use of functional magnetic resonance imaging has been the most preferred neuroscience technique. However, electroencephalography, eye tracking, and implicit measurements are becoming increasingly popular market research methods due to rapid technological improvements and reduced costs. Design This article is an overview of the most commonly used consumer neuroscience techniques in marketing research. Findings Many peer‐reviewed journal articles in the consumer neuroscience literature usually provide only a brief summary of the actual functions of each neuroscience technique used in their research. Throughout the consumer neuroscience and marketing research literature, there is a lack of comprehensive evaluation of the relative merits of all neuroscience research tools. There is no rigorous analysis of the relative appropriateness of all the neuroscience, physiological, and biometric research tools currently used in consumer neuroscience market research. Originality/value This is the first paper that provides a comprehensive review of all relevant neuroscience techniques with a proper explanation of their functions and utility for different types of research. Each individual neuromarketing research tools' strengths and weaknesses are analysed for particular types of marketing research questions and highlights some of the key research papers and authors for interested researchers to follow up.

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Fundamentals of EEG Signal Processing

Cited by 175 publications

References 92 publications

Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

Random Number Generators Basedon EEG Non-linear and ChaoticCharacteristics

Intelligent In‐Vehicle Interaction Technologies

Consumer neuroscience for marketing researchers

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