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Yuan G, Wang T, Ju W, Fu S. A portable affective computing system for identifying mate preference. Sci Rep 2024; 14:17735. [PMID: 39085370 PMCID: PMC11292018 DOI: 10.1038/s41598-024-68772-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024] Open
Abstract
Recognizing an individual's preference state for potential romantic partners based on electroencephalogram (EEG) signals holds significant practical value in enhancing matchmaking success rates and preventing romance fraud. Despite some progress has been made in this field, challenges such as high-dimensional feature space and channel redundancy limited the technology's practical application. The aim of this study is to explore the most discriminative EEG features and channels, in order to enhance the recognition performance of the system, while maximizing the portable and practical value of EEG-based systems for recognizing romantic attraction. To achieve this goal, we first conducted an interesting simulated dating experiment to collect the necessary data. Next, EEG features were extracted from various dimensions, including band power and asymmetry index features. Then, we introduced a novel method for EEG feature and channel selection that combines the sequential forward selection (SFS) algorithm with the frequency-based feature subset integration (FFSI) algorithm. Finally, we used the random forest classifier (RFC) to determine a person's preference state for potential romantic partners. Experimental results indicate that the optimal feature subset, selected using the SFS-FFSI method, attained an average classification accuracy of 88.42%. Notably, these features were predominantly sourced from asymmetry index features of electrodes situated in the frontal, parietal, and occipital lobes.
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Affiliation(s)
- Guangjie Yuan
- School of Psychology, Qufu Normal University, Shandong, China.
- College of Electronic and Information Engineering, Southwest University, Chongqing, China.
| | - Tao Wang
- School of Psychology, Qufu Normal University, Shandong, China
| | - Wei Ju
- School of Psychology, Qufu Normal University, Shandong, China
| | - Sai Fu
- Faculty of Education, Southwest University, Chongqing, China.
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2
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Zhang H, Yin L, Zhang H. Using subjective emotion, facial expression, and gaze direction to evaluate user affective experience and predict preference when playing single-player games. ERGONOMICS 2024:1-21. [PMID: 38832783 DOI: 10.1080/00140139.2024.2359123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 05/18/2024] [Indexed: 06/05/2024]
Abstract
The affective experience generated when users play computer games can influence their attitude and preference towards the game. Existing evaluation means mainly depend on subjective scales and physiological signals. However, some limitations should not be ignored (e.g. subjective scales are not objective, and physiological signals are complicated). In this paper, we 1) propose a novel method to assess user affective experience when playing single-player games based on pleasure-arousal-dominance (PAD) emotions, facial expressions, and gaze directions, and 2) build an artificial intelligence model to identify user preference. Fifty-four subjects participated in a basketball experiment with three difficulty levels. Their expressions, gaze directions, and subjective PAD emotions were collected and analysed. Experimental results showed that the expression intensities of angry, sad, and neutral, yaw angle degrees of gaze direction, and PAD emotions varied significantly under different difficulties. Besides, the proposed model achieved better performance than other machine-learning algorithms on the collected dataset.
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Affiliation(s)
- He Zhang
- School of Design, Hunan University, Changsha, China
| | - Lu Yin
- School of Design, Hunan University, Changsha, China
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3
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Panteli A, Kalaitzi E, Fidas CA. A review on the use of eeg for the investigation of the factors that affect Consumer's behavior. Physiol Behav 2024; 278:114509. [PMID: 38485039 DOI: 10.1016/j.physbeh.2024.114509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/20/2024]
Abstract
This literature review surveys research papers that focused on the use of Electroencephalography (EEG) to study the impact of different factors in consumer behavior. The primary aim of this review is to determine which factors that affect consumer's behavior have already been evaluated in the existing literature and which remain unexplored. 118 papers are included in this survey. In order that the papers were analyzed in this review, a well-established neuromarketing experiment should have been performed indicating the methods of signals' acquisition, processing and analysis. The novelty of this work is that it considers and classifies not only research articles that studied a factor that influences consumers' choices, but also those that studied consumers' decisions as a result of the interactions that take place among the received marketing messages and the individual's internal or external environment. Findings indicated that the current approaches have mostly evaluated the effects of the promotional campaigns and product features to consumer's behavior. Also, it was shown that the effect of the interactions among different aspects that influence consumer behavior has not yet adequately been studied.
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Affiliation(s)
- Antiopi Panteli
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece.
| | - Eirini Kalaitzi
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
| | - Christos A Fidas
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
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4
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Wang H, Jiang J, Gan JQ, Wang H. Motor Imagery EEG Classification Based on a Weighted Multi-Branch Structure Suitable for Multisubject Data. IEEE Trans Biomed Eng 2023; 70:3040-3051. [PMID: 37186527 DOI: 10.1109/tbme.2023.3274231] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
OBJECTIVE Electroencephalogram (EEG) signal recognition based on deep learning technology requires the support of sufficient data. However, training data scarcity usually occurs in subject-specific motor imagery tasks unless multisubject data can be used to enlarge training data. Unfortunately, because of the large discrepancies between data distributions from different subjects, model performance could only be improved marginally or even worsened by simply training on multisubject data. METHOD This article proposes a novel weighted multi-branch (WMB) structure for handling multisubject data to solve the problem, in which each branch is responsible for fitting a pair of source-target subject data and adaptive weights are used to integrate all branches or select branches with the largest weights to make the final decision. The proposed WMB structure was applied to six well-known deep learning models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, MSFBCNN, and EEG_TCNet) and comprehensive experiments were conducted on EEG datasets BCICIV-2a, BCICIV-2b, high gamma dataset (HGD) and two supplementary datasets. RESULT Superior results against the state-of-the-art models have demonstrated the efficacy of the proposed method in subject-specific motor imagery EEG classification. For example, the proposed WMB_EEGNet achieved classification accuracies of 84.14%, 90.23%, and 97.81% on BCICIV-2a, BCICIV-2b and HGD, respectively. CONCLUSION It is clear that the proposed WMB structure is capable to make good use of multisubject data with large distribution discrepancies for subject-specific EEG classification.
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Wang P, Wang J, Wang L, Mu W, Zhan G, Zhang X, Niu L, Bin J, Dong Z, Zhang L, Jia J, Gan Z, Kang X. Face preference detection task based on EEG energy analysis in the source domain. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082969 DOI: 10.1109/embc40787.2023.10340356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Facial stimulation can produce specific event-related potential (ERP) component N170 in the fusiform gyrus region. However, the role of the fusiform gyrus region in facial preference tasks is not clear at present, and the current research of facial preference analysis based on EEG signals is mostly carried out in the scalp domain. This paper explores whether the region of the fusiform gyrus is involved in processing face preference emotions in terms of the distribution of energy over the source domain, and finds that the pars orbitalis cortex is most energetically active in the face preference task and that there are significant differences between the left and right hemispheres.Clinical Relevance- The role of pars orbitalis in facial preference may help doctors determine whether the pars orbitalis cortex is lost in clinical practice.
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Benaissa B, Kobayashi M. The consumers' response to product design: a narrative review. ERGONOMICS 2023; 66:791-820. [PMID: 36154914 DOI: 10.1080/00140139.2022.2127919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/18/2022] [Indexed: 05/24/2023]
Abstract
This paper reviews the research ideas around consumer response to product design. From the product side, we discuss the most significant design features preferred by average consumers, such as aesthetics and utility. And from the consumer side, we investigate the human factors influencing consumer perceptions. We present the main approaches used to measure the consumer response to product design and summarize the multiple biases that occur during the evaluation. Finally, we present in detail the most commonly used methods to analyze consumer response data and their roles in the design evaluation context. Practitioner Summary: To answer the question: What causes differences in design response? We summarise the research findings related to product design features and human factors. We highlight the biases that can emerge from the measurement approach. And discuss the most common analysis methods used for product design response information.
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Affiliation(s)
- Brahim Benaissa
- Department of Mechanical Systems Engineering, Design Engineering Lab, Toyota Technological Institute, Aichi, Japan
| | - Masakazu Kobayashi
- Department of Mechanical Systems Engineering, Design Engineering Lab, Toyota Technological Institute, Aichi, Japan
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Li J, Wu SR, Zhang X, Luo TJ, Li R, Zhao Y, Liu B, Peng H. Cross-subject aesthetic preference recognition of Chinese dance posture using EEG. Cogn Neurodyn 2023; 17:311-329. [PMID: 37007204 PMCID: PMC10050299 DOI: 10.1007/s11571-022-09821-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 04/21/2022] [Accepted: 05/09/2022] [Indexed: 11/03/2022] Open
Abstract
Due to the differences in knowledge, experience, background, and social influence, people have subjective characteristics in the process of dance aesthetic cognition. To explore the neural mechanism of the human brain in the process of dance aesthetic preference, and to find a more objective determining criterion for dance aesthetic preference, this paper constructs a cross-subject aesthetic preference recognition model of Chinese dance posture. Specifically, Dai nationality dance (a classic Chinese folk dance) was used to design dance posture materials, and an experimental paradigm for aesthetic preference of Chinese dance posture was built. Then, 91 subjects were recruited for the experiment, and their EEG signals were collected. Finally, the transfer learning method and convolutional neural networks were used to identify the aesthetic preference of the EEG signals. Experimental results have shown the feasibility of the proposed model, and the objective aesthetic measurement in dance appreciation has been implemented. Based on the classification model, the accuracy of aesthetic preference recognition is 79.74%. Moreover, the recognition accuracies of different brain regions, different hemispheres, and different model parameters were also verified by the ablation study. Additionally, the experimental results reflected the following two facts: (1) in the visual aesthetic processing of Chinese dance posture, the occipital and frontal lobes are more activated and participate in dance aesthetic preference; (2) the right brain is more involved in the visual aesthetic processing of Chinese dance posture, which is consistent with the common knowledge that the right brain is responsible for processing artistic activities.
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Affiliation(s)
- Jing Li
- Academy of Arts, Shaoxing University, Shaoxing, 312000 China
| | - Shen-rui Wu
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000 China
| | - Xiang Zhang
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000 China
| | - Tian-jian Luo
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350117 China
| | - Rui Li
- National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, 430079 China
| | - Ying Zhao
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000 China
| | - Bo Liu
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000 China
| | - Hua Peng
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, 312000 China
- College of Information Science and Engineering, Jishou University, Jishou, 416000 China
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8
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EEG-Based Preference Classification for Neuromarketing Application. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023. [DOI: 10.1155/2023/4994751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.
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Shah SMA, Usman SM, Khalid S, Rehman IU, Anwar A, Hussain S, Ullah SS, Elmannai H, Algarni AD, Manzoor W. An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:9744. [PMID: 36560113 PMCID: PMC9782208 DOI: 10.3390/s22249744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/21/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.
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Affiliation(s)
- Syed Mohsin Ali Shah
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan
| | - Syed Muhammad Usman
- Department of Creative Technologies, Faculty of Computing and AI, Air University, Islamabad 44000, Pakistan
| | - Shehzad Khalid
- Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan
| | - Ikram Ur Rehman
- School of Computing and Engineering, The University of West London, London W5 5RF, UK
| | - Aamir Anwar
- School of Computing and Engineering, The University of West London, London W5 5RF, UK
| | - Saddam Hussain
- School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
| | - Syed Sajid Ullah
- Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abeer D. Algarni
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Waleed Manzoor
- Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan
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10
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Al-Nafjan A. Feature selection of EEG signals in neuromarketing. PeerJ Comput Sci 2022; 8:e944. [PMID: 35634118 PMCID: PMC9138093 DOI: 10.7717/peerj-cs.944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/16/2022] [Indexed: 06/15/2023]
Abstract
Brain-computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is also used in neuromarketing to study the brain's responses to marketing stimuli. This study sought to detect two preference states (like and dislike) in EEG neuromarketing data using the proposed EEG-based consumer preference recognition system. This study investigated the role of feature selection in BCI to improve the accuracy of preference detection for neuromarketing. Several feature selection methods were used for benchmark testing in multiple BCI studies. Four feature selection approaches, namely, principal component analysis (PCA), minimum redundancy maximum relevance (mRMR), recursive feature elimination (RFE), and ReliefF, were used with five different classifiers: deep neural network (DNN), support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest (RF). The four approaches were compared to evaluate the importance of feature selection. Moreover, the performance of classification algorithms was evaluated before and after feature selection. It was found that feature selection for EEG signals improves the performance of all classifiers.
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11
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Yuan G, He W, Liu G. Is Mate Preference Recognizable Based on Electroencephalogram Signals? Machine Learning Applied to Initial Romantic Attraction. Front Neurosci 2022; 16:830820. [PMID: 35221907 PMCID: PMC8873380 DOI: 10.3389/fnins.2022.830820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Initial romantic attraction (IRA) refers to a series of positive reactions toward potential ideal partners based on individual preferences; its evolutionary value lies in facilitating mate selection. Although the EEG activities associated with IRA have been preliminarily understood; however, it remains unclear whether IRA can be recognized based on EEG activity. To clarify this, we simulated a dating platform similar to Tinder. Participants were asked to imagine that they were using the simulated dating platform to choose the ideal potential partner. Their brain electrical signals were recorded as they viewed photos of each potential partner and simultaneously assessed their initial romantic attraction in that potential partner through self-reported scale responses. Thereafter, the preprocessed EEG signals were decomposed into power-related features of different frequency bands using a wavelet transform approach. In addition to the power spectral features, feature extraction also accounted for the physiological parameters related to hemispheric asymmetries. Classification was performed by employing a random forest classifier, and the signals were divided into two categories: IRA engendered and IRA un-engendered. Based on the results of the 10-fold cross-validation, the best classification accuracy 85.2% (SD = 0.02) was achieved using feature vectors, mainly including the asymmetry features in alpha (8–13 Hz), beta (13–30 Hz), and theta (4–8 Hz) rhythms. The results of this study provide early evidence for EEG-based mate preference recognition and pave the way for the development of EEG-based romantic-matching systems.
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Affiliation(s)
- Guangjie Yuan
- College of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
| | - Wenguang He
- College of Psychology, Qufu Normal University, Qufu, China
| | - Guangyuan Liu
- College of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- *Correspondence: Guangyuan Liu,
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12
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Khurana V, Gahalawat M, Kumar P, Roy PP, Dogra DP, Scheme E, Soleymani M. A Survey on Neuromarketing Using EEG Signals. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3065200] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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13
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Robaina-Calderín L, Martín-Santana JD. A review of research on neuromarketing using content analysis: key approaches and new avenues. Cogn Neurodyn 2021; 15:923-938. [PMID: 34790262 DOI: 10.1007/s11571-021-09693-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 05/14/2021] [Accepted: 06/11/2021] [Indexed: 11/29/2022] Open
Abstract
There is currently a growing interest in a deeper understanding of consumer behaviour. In this context, the union of different disciplines such as neuroscience and marketing has given birth to new fields of knowledge, e.g. neuromarketing. This study is mainly aimed at carrying out a systematic revision of the literature on neuromarketing from a holistic point of view, analysing its definition and processes, as well as more specific aspects such as its ethics and applications. Based on the results of our review, following a combined methodology with a base dictionary and text mining, our study presents both the current lines of research and the future lines of work.
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Affiliation(s)
- Lorena Robaina-Calderín
- Universidad de Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Las Palmas Spain
| | - Josefa D Martín-Santana
- Universidad de Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Las Palmas Spain
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14
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Wang H, Liu X, Li J, Xu T, Bezerianos A, Sun Y, Wan F. Driving Fatigue Recognition With Functional Connectivity Based on Phase Synchronization. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2985539] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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15
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Aldayel M, Ykhlef M, Al-Nafjan A. Consumers’ Preference Recognition Based on Brain–Computer Interfaces: Advances, Trends, and Applications. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05695-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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16
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Rawls E, White R, Kane S, Stevens CE, Zabelina DL. Parametric Cortical Representations of Complexity and Preference for Artistic and Computer-Generated Fractal Patterns Revealed by Single-Trial EEG Power Spectral Analysis. Neuroimage 2021; 236:118092. [PMID: 33895307 PMCID: PMC8287964 DOI: 10.1016/j.neuroimage.2021.118092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/12/2021] [Accepted: 04/14/2021] [Indexed: 11/29/2022] Open
Abstract
Fractals are self-similar patterns that repeat at different scales, the complexity of which are expressed as a fractional Euclidean dimension D between 0 (a point) and 2 (a filled plane). The drip paintings of American painter Jackson Pollock (JP) are fractal in nature, and Pollock's most illustrious works are of the high-D (~1.7) category. This would imply that people prefer more complex fractal patterns, but some research has instead suggested people prefer lower-D fractals. Furthermore, research has suggested that parietal and frontal brain activity tracks the complexity of fractal patterns, but previous research has artificially binned fractals depending on fractal dimension, rather than treating fractal dimension as a parametrically varying value. We used white layers extracted from JP artwork as stimuli, and constructed statistically matched 2-dimensional random Cantor sets as control stimuli. We recorded the electroencephalogram (EEG) while participants viewed the JP and matched random Cantor fractal patterns. Participants then rated their subjective preference for each pattern. We used a single-trial analysis to construct within-subject models relating subjective preference to fractal dimension D, as well as relating D and subjective preference to single-trial EEG power spectra. Results indicated that participants preferred higher-D images for both JP and Cantor stimuli. Power spectral analysis showed that, for artistic fractal images, parietal alpha and beta power parametrically tracked complexity of fractal patterns, while for matched mathematical fractals, parietal power tracked complexity of patterns over a range of frequencies, but most prominently in alpha band. Furthermore, parietal alpha power parametrically tracked aesthetic preference for both artistic and matched Cantor patterns. Overall, our results suggest that perception of complexity for artistic and computer-generated fractal images is reflected in parietal-occipital alpha and beta activity, and neural substrates of preference for complex stimuli are reflected in parietal alpha band activity.
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Affiliation(s)
- Eric Rawls
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Health.
| | - Rebecca White
- Department of Psychology, University of New Hampshire
| | - Stephanie Kane
- Department of Psychological Sciences, University of Arkansas
| | - Carl E Stevens
- Department of Psychological Sciences, University of Arkansas
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17
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Neuroimaging Techniques in Advertising Research: Main Applications, Development, and Brain Regions and Processes. SUSTAINABILITY 2021. [DOI: 10.3390/su13116488] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Despite the advancement in neuroimaging tools, studies about using neuroimaging tools to study the impact of advertising on brain regions and processes are scant and remain unclear in academic literature. In this article, we have followed a literature review methodology and a bibliometric analysis to select empirical and review papers that employed neuroimaging tools in advertising campaigns and to understand the global research trends in the neuromarketing domain. We extracted and analyzed sixty-three articles from the Web of Science database to answer our study questions. We found four common neuroimaging techniques employed in advertising research. We also found that the orbitofrontal cortex (OFC), the ventromedial prefrontal cortex, and the dorsolateral prefrontal cortex play a vital role in decision-making processes. The OFC is linked to positive valence, and the lateral OFC and left dorsal anterior insula related in negative valence. In addition, the thalamus and primary visual area associated with the bottom-up attention system, whereas the top-down attention system connected to the dorsolateral prefrontal cortex, parietal cortex, and primary visual areas. For memory, the hippocampus is responsible for generating and processing memories. We hope that this study provides valuable insights about the main brain regions and processes of interest for advertising.
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Pei G, Li T. A Literature Review of EEG-Based Affective Computing in Marketing. Front Psychol 2021; 12:602843. [PMID: 33796042 PMCID: PMC8007771 DOI: 10.3389/fpsyg.2021.602843] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 02/19/2021] [Indexed: 01/18/2023] Open
Abstract
Affect plays an important role in the consumer decision-making process and there is growing interest in the development of new technologies and computational approaches that can interpret and recognize the affects of consumers, with benefits for marketing described in relation to both academia and industry. From an interdisciplinary perspective, this paper aims to review past studies focused on electroencephalography (EEG)-based affective computing (AC) in marketing, which provides a promising avenue for studying the mechanisms underlying affective states and developing recognition computational models to predict the psychological responses of customers. This review offers an introduction to EEG technology and an overview of EEG-based AC; provides a snapshot of the current state of the literature. It briefly presents the themes, challenges, and trends in studies of affect evaluation, recognition, and classification; and further proposes potential guidelines for researchers and marketers.
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Affiliation(s)
- Guanxiong Pei
- Zhejiang Laboratory, Research Center for Advanced AI Theory, Hangzhou, China
| | - Taihao Li
- Zhejiang Laboratory, Research Center for Advanced AI Theory, Hangzhou, China
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Aldayel M, Ykhlef M, Al-Nafjan A. Recognition of Consumer Preference by Analysis and Classification EEG Signals. Front Hum Neurosci 2021; 14:604639. [PMID: 33519402 PMCID: PMC7838383 DOI: 10.3389/fnhum.2020.604639] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/23/2020] [Indexed: 12/03/2022] Open
Abstract
Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.
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Affiliation(s)
- Mashael Aldayel
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.,Information System Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Mourad Ykhlef
- Information System Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Abeer Al-Nafjan
- Computer Science Department, College of Computer and Information Sciences, Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia
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Li R, Zhang J. Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science. Brain Inform 2020; 7:16. [PMID: 33196915 PMCID: PMC7669983 DOI: 10.1186/s40708-020-00118-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 10/29/2020] [Indexed: 11/21/2022] Open
Abstract
The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration.
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Affiliation(s)
- Rui Li
- National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, Hubei, People's Republic of China
| | - Junsong Zhang
- National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, Hubei, People's Republic of China.
- Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China.
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Abstract
Design is a challenging task that is crucial to all product development. Advances in design computing may allow machines to move from a supporting role to generators of design content. Generative Design systems produce designs by algorithms and offer the potential for the exploration of vast design spaces, the fostering of creativity, the combination of objective and subjective requirements, and the revolutionary integration of conceptual and detailed design phases. The application of generative methods to the design of discrete, physical, engineered products has not yet been reviewed. This paper reviews the Generative Product Design systems developed since 1998 in order to identify significant approaches and trends. Systems are analyzed according to their primary goal, generative method, the design phase they focus on, whether the generation is automatic or interactive, the number of design options they generate, and the types of design requirements involved in the generation process. Progress using this approach is recognized, and a number of challenges that must be addressed in order to achieve widespread acceptance are identified. Possible solutions are offered, including innovative approaches in Human–Computer Interaction.
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Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inform 2020; 7:10. [PMID: 32955675 PMCID: PMC7505913 DOI: 10.1186/s40708-020-00109-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 08/14/2020] [Indexed: 11/20/2022] Open
Abstract
Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.
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Suhaimi NS, Mountstephens J, Teo J. EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8875426. [PMID: 33014031 PMCID: PMC7516734 DOI: 10.1155/2020/8875426] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/30/2020] [Accepted: 08/28/2020] [Indexed: 11/18/2022]
Abstract
Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.
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Affiliation(s)
- Nazmi Sofian Suhaimi
- Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia
| | - James Mountstephens
- Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia
| | - Jason Teo
- Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia
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EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051619] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Emotion plays a nuclear part in human attention, decision-making, and communication. Electroencephalogram (EEG)-based emotion recognition has developed a lot due to the application of Brain-Computer Interface (BCI) and its effectiveness compared to body expressions and other physiological signals. Despite significant progress in affective computing, emotion recognition is still an unexplored problem. This paper introduced Logistic Regression (LR) with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The Gaussian kernel enhances the EEG data separability in the transformed space. The Laplacian prior promotes the sparsity of learned LR regressors to avoid over-specification. The LR regressors are optimized using the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. For simplicity, the introduced method is noted as LORSAL. Experiments were conducted on the dataset for emotion analysis using EEG, physiological and video signals (DEAP). Various spectral features and features by combining electrodes (power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU)) were extracted from different frequency bands (Delta, Theta, Alpha, Beta, Gamma, and Total) with EEG signals. The Naive Bayes (NB), support vector machine (SVM), linear LR with L1-regularization (LR_L1), linear LR with L2-regularization (LR_L2) were used for comparison in the binary emotion classification for valence and arousal. LORSAL obtained the best classification accuracies (77.17% and 77.03% for valence and arousal, respectively) on the DE features extracted from total frequency bands. This paper also investigates the critical frequency bands in emotion recognition. The experimental results showed the superiority of Gamma and Beta bands in classifying emotions. It was presented that DE was the most informative and DASM and DCAU had lower computational complexity with relatively ideal accuracies. An analysis of LORSAL and the recently deep learning (DL) methods is included in the discussion. Conclusions and future work are presented in the final section.
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Deep Learning for EEG-Based Preference Classification in Neuromarketing. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10041525] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.
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Lamti HA, Ben Khelifa MM, Hugel V. Mental fatigue level detection based on event related and visual evoked potentials features fusion in virtual indoor environment. Cogn Neurodyn 2019; 13:271-285. [PMID: 31168331 DOI: 10.1007/s11571-019-09523-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 12/03/2018] [Accepted: 01/16/2019] [Indexed: 12/15/2022] Open
Abstract
The purpose of this work is to set up a model that can estimate the mental fatigue of users based on the fusion of relevant features extracted from Positive 300 (P300) and steady state visual evoked potentials (SSVEP) measured by electroencephalogram. To this end, an experimental protocol describes the induction of P300, SSVEP and mental workload (which leads to mental fatigue by varying time-on-task) in different scenarios where environmental artifacts are controlled (obstacles number, obstacles velocities, ambient luminosity). Ten subjects took part in the experiment (with two suffering from cerebral palsy). Their mission is to navigate along a corridor from a starting point A to a goal point B where specific flickering stimuli are introduced to perform the P300 task. On the other hand, SSVEP task is elicited thanks to 10 Hz flickering lights. Correlated features are considered as inputs to fusion block which estimates mental workload. In order to deal with uncertainties and heterogeneity of P300 and SSVEP features, Dempster-Shafer (D-S) evidential reasoning is introduced. As the goal is to assess the reliability for the estimation of mental fatigue levels, D-S is compared to multi layer perception and linear discriminant analysis. The results show that D-S globally outperforms the other classifiers (although its performance significantly decreases between healthy and palsied groups). Finally we discuss the feasibility of such a fusion proposal in real life situation.
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Affiliation(s)
- Hachem A Lamti
- 1COnception de Systemes Mecaniques et Robotiques (COSMER) Laboratory, University of Toulon, Toulon, France
| | | | - Vincent Hugel
- 1COnception de Systemes Mecaniques et Robotiques (COSMER) Laboratory, University of Toulon, Toulon, France
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Teng C, Cheng Y, Wang C, Ren Y, Xu W, Xu J. Aging-related changes of EEG synchronization during a visual working memory task. Cogn Neurodyn 2018; 12:561-568. [PMID: 30483364 DOI: 10.1007/s11571-018-9500-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 07/12/2018] [Accepted: 08/22/2018] [Indexed: 12/15/2022] Open
Abstract
Differences of EEG synchronization between normal old and young people during a working memory (WM) task were investigated. The synchronization likelihood (SL) is a novel method to assessed synchronization in multivariate time series for non-stationary systems. To evaluate this method to study the mechanisms of WM, we calculated the SL values in brain electrical activity for both resting state and task state. EEG signals were recorded from 14 young adults and 12 old adults during two different states, respectively. SL was used to measure EEG synchronization between 19 electrodes in delta, theta, alpha1, alpha2 and beta frequency bands. Bad task performance and significantly decreased EEG synchronization were found in old group compared to young group in alpha1, alpha2 and beta frequency bands during the WM task. Moreover, significantly decreased EEG synchronization in beta band in the elder was also detected during the resting state. The findings suggested that reduced EEG synchronization may be one of causes for WM capacity decline along with healthy aging.
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Affiliation(s)
- Chaolin Teng
- 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049 Shaanxi Province People's Republic of China
| | - Yao Cheng
- 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049 Shaanxi Province People's Republic of China
| | - Chao Wang
- 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049 Shaanxi Province People's Republic of China
| | - Yijing Ren
- 2Beijing University of Posts and Telecommunications, Beijing, China
| | - Weiyong Xu
- 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049 Shaanxi Province People's Republic of China.,3Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
| | - Jin Xu
- 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049 Shaanxi Province People's Republic of China
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Wang H, Dragomir A, Abbasi NI, Li J, Thakor NV, Bezerianos A. A novel real-time driving fatigue detection system based on wireless dry EEG. Cogn Neurodyn 2018; 12:365-376. [PMID: 30137873 DOI: 10.1007/s11571-018-9481-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 11/15/2017] [Accepted: 02/16/2018] [Indexed: 11/30/2022] Open
Abstract
Development of techniques for detection of mental fatigue has varied applications in areas where sustaining attention is of critical importance like security and transportation. The objective of this study is to develop a novel real-time driving fatigue detection methodology based on dry Electroencephalographic (EEG) signals. The study has employed two methods in the online detection of mental fatigue: power spectrum density (PSD) and sample entropy (SE). The wavelet packets transform (WPT) method was utilized to obtain the θ (4-7 Hz), α (8-12 Hz) and β (13-30 Hz) bands frequency components for calculating corresponding PSD of the selected channels. In order to improve the fatigue detection performance, the system was individually calibrated for each subject in terms of fatigue-sensitive channels selection. Two fatigue-related indexes: ( θ+α )/ β and θ / β were computed and then fused into an integrated metric to predict the degree of driving fatigue. In the case of SE extraction, the mean of SE averaged across two EEG channels ('O1h' and 'O2h') was used for fatigue detection. Ten healthy subjects participated in our study and each of them performed two sessions of simulated driving. In each session, subjects were required to drive simulated car for 90 min without any break. The results demonstrate that our proposed methods are effective for fatigue detection. The prediction of fatigue is consistent with the observation of reaction time that was recorded during simulated driving, which is considered as an objective behavioral measure.
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Affiliation(s)
- Hongtao Wang
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore.,2School of Information Engineering, Wuyi University, Jiangmen, 529020 Guangdong China
| | - Andrei Dragomir
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
| | - Nida Itrat Abbasi
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore.,3Department of Biomedical Engineering, National University of Singapore, Singapore, 117456 Singapore
| | - Junhua Li
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
| | - Nitish V Thakor
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
| | - Anastasios Bezerianos
- 1Singapore Institute for Neurotechnology(SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore, 117456 Singapore
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Duru AD, Assem M. Investigating neural efficiency of elite karate athletes during a mental arithmetic task using EEG. Cogn Neurodyn 2017; 12:95-102. [PMID: 29435090 DOI: 10.1007/s11571-017-9464-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 11/05/2017] [Accepted: 11/28/2017] [Indexed: 12/15/2022] Open
Abstract
Neural efficiency is proposed as one of the neural mechanisms underlying elite athletic performances. Previous sports studies examined neural efficiency using tasks that involve motor functions. In this study we investigate the extent of neural efficiency beyond motor tasks by using a mental subtraction task. A group of elite karate athletes are compared to a matched group of non-athletes. Electroencephalogram is used to measure cognitive dynamics during resting and increased mental workload periods. Mainly posterior alpha band power of the karate players was found to be higher than control subjects under both tasks. Moreover, event related synchronization/desynchronization has been computed to investigate the neural efficiency hypothesis among subjects. Finally, this study is the first study to examine neural efficiency related to a cognitive task, not a motor task, in elite karate players using ERD/ERS analysis. The results suggest that the effect of neural efficiency in the brain is global rather than local and thus might be contributing to the elite athletic performances. Also the results are in line with the neural efficiency hypothesis tested for motor performance studies.
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Affiliation(s)
- Adil Deniz Duru
- 1Neuroscience in Sports Laboratory, Faculty of Sport Science, Marmara University, Anadolu Hisarı Campus, Beykoz, Istanbul, Turkey
| | - Moataz Assem
- 2Neurosignal Analysis Laboratory, Institute of Biomedical Engineering, Bogazici University, Kandilli Kampusu, Cengelkoy, 34684 Istanbul, Turkey
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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7121239] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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