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Cognitive load during planned and unplanned virtual shopping: Evidence from a neurophysiological perspective
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2023 | International Journal of Information Management | Citations: 0
Authors: Kakaria, Shobhit; Saffari, Farzad; Z. Ramsøy, Thomas; Bigné, Enrique

Abstract: Rapid adoption of virtual-reality-assisted retail applications is inadvertently ... Expand

Abstract: Rapid adoption of virtual-reality-assisted retail applications is inadvertently reshaping consumer buying pat terns, making it crucial for businesses to enhance their shopping experience. This new scenario challenges marketers with unique hurdles in both the commercialization of products and in managing information cues derived via VR retailing. Therefore, this study examined consumers’ impulsive behavior and unplanned pur chases in a virtual retail store, using self-reports and electroencephalography. Borrowing assorted perspectives from retailing, virtual reality, and neuromarketing literature, we extended the stimulus-organism-response framework to evaluate how unplanned behavior evolves through conscious and unconscious measures. We found that consumers’ impulsiveness was significantly associated with their unplanned expenditure and the number of unplanned purchases. Using mediation analysis, we observed that flow experience during shopping partially mediated the relationship between the sense of presence and the desire to stay longer in a virtual shopping store. Desire to stay in the virtual store positively influenced store satisfaction, basket-size deviation, and budget deviation. Additionally, cognitive workload obtained via electroencephalogram revealed significant differences during both planned and unplanned purchases. These findings provide fresh opportunities for re tailers to leverage the disruptive potential of immersive and interactive virtual technology to transform consumer shopping experiences. Collapse

Semantic filters: spectral analysis

Topics: virtual reality decision making psychological ownership social media marketing management

Methods: electroencephalography self reported survey longitudinal research neurophysiology experiment
Subject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum

2023 | HICSS | Citations: 0
Authors: Penava, Pascal; Brozat, Marie-Louise; Zimmermann, Yara; Breitenbach, Johannes; Ulrich, Patrick; Buettner, Ricardo

Abstract: The classification of sensorimotor rhythms in electroencephalography signals can ... Expand

Abstract: The classification of sensorimotor rhythms in electroencephalography signals can enable paralyzed individuals, for example, to make yes/no decisions. In practice, these approaches are hard to implement due to the variability of electroencephalography signals between and within subjects. Therefore, we report a novel and fast machine learning model, meeting the need for efficiency and reliability as well as low calibration and training time. Our model extracts finely graded frequency bands from motor imagery electroencephalography data by using power spectral density and training a random forest algorithm for classification. The goal was to create a non-invasive generalizable method by training the algorithm with subject-independent EEG data. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets. With a balanced accuracy of 73.94%, our novel algorithm outperforms other state-of-the-art non-subject-dependent algorithms. Collapse

Semantic filters: spectral analysis

Topics: societal challenge Python

Methods: electroencephalography computational algorithm longitudinal research spectral analysis machine learning
Machine Learning-Based Health Behavior Prediction Using Resting-State EEG Data

2022 | Americas Conference on Information Systems | Citations: 0
Authors: Breitenbach, Johannes; Eisenhardt, Daniel; Rieg, Thilo; Baumgartl, Hermann; Ulrich, Patrick; Timm, Ingo; Buettner, Ricardo

Abstract: Long-term diseases often develop at a slow rate, making it difficult to detect ... Expand

Abstract: Long-term diseases often develop at a slow rate, making it difficult to detect them. These medical conditions often arise due to approached health behavior patterns. The circumstance of negative behavioral patterns encouraging diseases results in treatment issues due to late treatment initialization. This paper presents a machine learning-based approach to predict people's health behavior tendencies at an early stage based on their Future Time Perspective using objective resting-state EEG data. With a balanced accuracy of 95.00 percent based on the EEG frequency bands identified as relevant (20)(21)(27)(28), our model sets a first benchmark in this field, building a base for early recognition and intervention of potential long-term diseases. Collapse

Semantic filters: spectral analysis

Topics: decision making

Methods: electroencephalography machine learning classification method computational algorithm design artifact
Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data
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2021 | HICSS | Citations: 0
Authors: Buettner, Ricardo; Buechele, Michael; Grimmeisen, Benedikt; Ulrich, Patrick

Abstract: We report on promising results concerning the fast and accurate diagnosis of dev ... Expand

Abstract: We report on promising results concerning the fast and accurate diagnosis of developmental coordination disorder (DCD) which heavily impacts the life of affected children with emotional and behavioral issues. Using a machine learning classifier on spectral data of electroencephalography (EEG) recordings and unfolding the traditional frequency bandwidth in a fine-graded equidistant 99-point spectrum we were able to reach an accuracy of over 99.35 percent having only one misclassification. Our machine learning work contributes to healthcare and information systems research. While current diagnostic methods in use are either complicated, time-consuming, or inaccurate, our automated machine-based approach is accurate and reliable. Our results also provide more insights into the relationship between DCD and brain activity which could stimulate future work in medicine. Collapse

Semantic filters: spectral analysis

Topics: online learning system mindfulness technology adoption personality workstation

Methods: electroencephalography machine learning computational algorithm random forest classification longitudinal research
An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals
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2021 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Edla, Damodar Reddy; Dodia, Shubham; Bablani, Annushree; Kuppili, Venkatanareshbabu

Abstract: Brain-Computer Interface is the collaboration of the human brain and a device th ... Expand

Abstract: Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained. Collapse

Semantic filters: spectral analysis

Topics: deception detection terrorist incident personality computer hardware

Methods: machine learning electroencephalography autoencoder data transformation experiment
High-Performance Detection of Mild Cognitive Impairment Using Resting-State EEG Signals Located in Broca’s Area: A Machine Learning Approach

2021 | Americas Conference on Information Systems | Citations: 0
Authors: Gross, Jan; Groiss, Niklas; Rieg, Thilo; Baumgartl, Hermann; Buettner, Ricardo

Abstract: Dementia and Alzheimer's disease represent one of the biggest medical challenge ... Expand

Abstract: Dementia and Alzheimer's disease represent one of the biggest medical challenges of our century, manifesting the risk to individuals of losing their language or selfmanagement skills. It is estimated that by 2050 around 1.2% of the world's population will suffer from these diseases. Since there are no effective treatment options available, it is of great impor tance to detect dementia tendencies at the earliest possible stage. Mild cognitive impairment represents a preclinical stage of Alzheimer's disease, allowing detecting dementia tendencies prematurely. As related work either relies on sen sitive test results, does not manifest a sophisticated level of accuracy, or relies on costly techniques, further research is required. In this study, we propose a machine learning approach using restingstate EEG recordings of channels located in the Broca's area. We achieved a benchmark accuracy of 90.91% in differentiating nonMCI and MCI individuals based on the assessment of phonemic verbal fluency. Collapse

Semantic filters: spectral analysis

Topics: personality database system health information system data quality

Methods: electroencephalography computational algorithm machine learning magnetic resonance imaging descriptive statistic
Machine Learning Based Differentiation Between High and Low Self-Discipline Using EEG Data

2021 | European Conference On Information Systems | Citations: 0
Authors: Sauter, Daniel; Butz, Lars; Buettner, Ricardo

Abstract: Since the detection of high and low self-discipline based on self-reported meas ... Expand

Abstract: Since the detection of high and low self-discipline based on self-reported measures is problematic due to validity problems, we have proposed an objective method based on physiological EEG data. Using a fine grained EEG spectrum in combination with machine learning, we were able to build a stable, reliable classifier, which reaches an accuracy of almost 76 percent. Moreover, we show that there are systematic differences in the theta and beta frequencies. Predicting low self-discipline and high selfdiscipline is an important topic in learning (especially e-learning) research as well as in medicine. Collapse

Semantic filters: spectral analysis

Topics: personality

Methods: electroencephalography computational algorithm survey machine learning oculometry
Two-Level Classification of Chronic Stress Using Machine Learning on Resting-State EEG Recordings

2020 | Americas Conference on Information Systems | Citations: 0
Authors: Baumgartl, Hermann; Fezer, Eric; Buettner, Ricardo

Abstract: While there are several works that diagnose acute stress using electroencephalo ... Expand

Abstract: While there are several works that diagnose acute stress using electroencephalographic recordings and machine learning, there are hardly any works that deal with chronic stress. Currently, chronic stress is mainly determined using questionnaires, which are, however, subjective in nature. While chronic stress has negative influences on health, it also greatly influences decision-making processes in humans. In this paper we propose a novel machine learning approach based on the fine-graded spectral analysis of resting-state EEG recordings, to diagnose chronic stress. By using this new machine learning approach, we achieve a very good balanced accuracy of 81.33%, outperforming the current benchmark by 10%. Our algorithm allows an objective assessment of chronic stress, is accurate, robust, fast and cost-efficient and substantially contributes to decision-making research, as well as Information Systems research in healthcare. Collapse

Semantic filters: spectral analysis

Topics: decision making technostress

Methods: electroencephalography computational algorithm machine learning random forest classification survey
Detection of Excessive Daytime Sleepiness in Resting-State EEG Recordings: A Novel Machine Learning Approach Using Specific EEG Sub-Bands and Channels

2020 | Americas Conference on Information Systems | Citations: 0
Authors: Breitenbach, Johannes; Baumgartl, Hermann; Buettner, Ricardo

Abstract: Excessive Daytime Sleepiness (EDS) is a threat to the safety of the patient as ... Expand

Abstract: Excessive Daytime Sleepiness (EDS) is a threat to the safety of the patient as well as others and can lead to higher rates of mortality. While current tests for EDS are time-consuming, expensive, and inaccurate, modern data-driven approaches can significantly improve diagnosis and treatment quality. Using an improved machine learning (ML) method in combination with the unfolding of electroencephalogram (EEG) bandwidths in a fine-graded equidistant 99-point spectrum, we successfully developed and evaluated a fast classification model to correctly diagnose EDS. Our model achieved a good level of accuracy of 87.00 percent on completely unseen resting-state EEG data. Our model is one of the first to detect long term EDS instead of short-term drowsiness from EEG data and significantly extends related work on the detection of drowsiness by considering the local occurrence of differences in spectral power. Collapse

Semantic filters: spectral analysis

Topics: database system

Methods: electroencephalography computational algorithm literature study digital trace data descriptive statistic
Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings

2020 | HICSS | Citations: 0
Authors: Buettner, Ricardo; Beil, David; Scholtz, Stefanie; Djemai, Aadel

Abstract: While diagnosing schizophrenia by physicians based on patients' history and thei ... Expand

Abstract: While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and nonschizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz. Collapse

Semantic filters: spectral analysis

Topics: mindfulness personality

Methods: electroencephalography random forest classification machine learning longitudinal research spectral analysis

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Found 20 articles

Cognitive load during planned and unplanned virtual shopping: Evidence from a neurophysiological perspective

Subject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum

Machine Learning-Based Health Behavior Prediction Using Resting-State EEG Data

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

High-Performance Detection of Mild Cognitive Impairment Using Resting-State EEG Signals Located in Broca’s Area: A Machine Learning Approach

Machine Learning Based Differentiation Between High and Low Self-Discipline Using EEG Data

Two-Level Classification of Chronic Stress Using Machine Learning on Resting-State EEG Recordings

Detection of Excessive Daytime Sleepiness in Resting-State EEG Recordings: A Novel Machine Learning Approach Using Specific EEG Sub-Bands and Channels

Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings