CogniMeter: EEG-based Emotion, Mental Workload and Stress Visual Monitoring

Hou, Xiyuan; Liu, Yisi; Sourina, Olga; Müeller-Wittig, Wolfgang

doi:10.1109/cw.2015.58

Cited by 44 publications

(21 citation statements)

References 33 publications

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“…That study concluded that only FD could be used to evaluate human mental workload. Several computational intelligence algorithms have been used to classify and detect mental workload levels, such as SVM [72,138,[190][191][192], ANN [193][194][195], and random forest (RF) [196].…”

Section: Plos Onementioning

confidence: 99%

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Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Ismail

Karwowski

2020

PLoS ONE

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Background Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations. Objective The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements. Methods In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords. Results Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual’s mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored. Conclusion This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

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Section: Plos Onementioning

confidence: 99%

“…A reduction in the FD value has been shown to correspond to negative emotions [224]. A positive correlation between mental workload and stress was observed when combining PSD with FD indices to recognize different emotional states and mental workload and was shown to successfully reflect human emotions [190,225,226].…”

Section: The Effect Of Emotion and Stressmentioning

confidence: 99%

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Ismail

Karwowski

2020

PLoS ONE

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“…Other research approaches investigated the relationship of workload, frustration or the driver's stress level and different road types (Miller, 2013;Schweitzer and Green, 2007;Sugiono, Widhayanuriyawan and Andriani, 2017). As workload, frustration and stress level are closely related to emotions and emotional states (Hou, Sourina and Mueller-Wittig, 2015) the research was considered relevant for the current study. Schweitzer and Green compared workload and task acceptability in urban situations, expressways, rural roads and residential roads based on ratings from video clips.…”

Section: Background Researchmentioning

confidence: 99%

Investigation of the dependency of the drivers’ emotional experience on different road types and driving conditions

Weber

Giacomin

Malizia

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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The growing sophistication of technologies and sociological advances are major causes for the dramatic change the automotive sector is currently undergoing. To address changes from a human-centered design perspective an improved understanding of the occupants' emotional experience and behavior is required. Facial-Expression Analysis (FEA) is an emerging tool in support of such an approach, suitable for automotive research due to its noncontact application and low intrusiveness. The research described here investigated the dependency of the occupants' emotional experience on road types and driving conditions by investigating emotional responses and their causes through FEA and observational analysis. Twenty-one university students and staff were recruited for the real-time test on a planned road circuit covering different road types and conditions. Facial-expression data and video information from two in-car cameras were collected during an average driving time of 40 minutes per participant. A multi-method approach was applied for the data analysis, including both quantitative statistical analysis and qualitative observational analysis, as well as an inter-observer reliability test. Emotion frequencies were compared between the different road types, resulting in a percentage difference from the total average of emotion frequency of-6.09% below average for urban roads, +11.15% above average for major roads and +4.88% above average for rural roads. The causes most frequently assigned to the emotional responses in this dataset were poor road conditions and causes related to the navigation device. The research supported the dependency of emotional experiences on the driving condition and type of road. The study presents the first step of a human-centered design approach towards modern automotive design. The results have wide application in automotive design, applicable to the development of, for instance, an affective human-machine interaction or a personalized autonomous driving experience.

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“…In particular, central nervous system activities are related to several aspects of emotions (e.g., from displeasure to pleasure, and from relaxation to excitement); however, the peripheral nervous system activities are only associated with arousal and relaxation (Zhai et al, 2005; Chanel et al, 2011). Therefore, the EEG can provide more detailed information on emotional states than other methods (Takahashi et al, 2004; Lee and Hsieh, 2014; Liu and Sourina, 2014; Hou et al, 2015). Moreover, EEG-based emotion recognition has a greater potential with respect to research than facial and speech-based methods, given that internal nerve fluctuations cannot be deliberately masked or controlled.…”

Section: Introductionmentioning

confidence: 99%

Multi-method Fusion of Cross-Subject Emotion Recognition Based on High-Dimensional EEG Features

Zhao

Jiang

et al. 2019

Front. Comput. Neurosci.

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Emotion recognition using electroencephalogram (EEG) signals has attracted significant research attention. However, it is difficult to improve the emotional recognition effect across subjects. In response to this difficulty, in this study, multiple features were extracted for the formation of high-dimensional features. Based on the high-dimensional features, an effective method for cross-subject emotion recognition was then developed, which integrated the significance test/sequential backward selection and the support vector machine (ST-SBSSVM). The effectiveness of the ST-SBSSVM was validated on a dataset for emotion analysis using physiological signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). With respect to high-dimensional features, the ST-SBSSVM average improved the accuracy of cross-subject emotion recognition by 12.4% on the DEAP and 26.5% on the SEED when compared with common emotion recognition methods. The recognition accuracy obtained using ST-SBSSVM was as high as that obtained using sequential backward selection (SBS) on the DEAP dataset. However, on the SEED dataset, the recognition accuracy increased by ~6% using ST-SBSSVM from that using the SBS. Using the ST-SBSSVM, ~97% (DEAP) and 91% (SEED) of the program runtime was eliminated when compared with the SBS. Compared with recent similar works, the method developed in this study for emotion recognition across all subjects was found to be effective, and its accuracy was 72% (DEAP) and 89% (SEED).

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CogniMeter: EEG-based Emotion, Mental Workload and Stress Visual Monitoring

Cited by 44 publications

References 33 publications

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Investigation of the dependency of the drivers’ emotional experience on different road types and driving conditions

Multi-method Fusion of Cross-Subject Emotion Recognition Based on High-Dimensional EEG Features

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