Evaluating Classifiers for Emotion Recognition Using EEG

Sohaib, Ahmad Tauseef; Qureshi, Shahnawaz; Hagelbäck, Johan; Hilborn, Olle; Jerčić, Petar

doi:10.1007/978-3-642-39454-6_53

Cited by 75 publications

(39 citation statements)

References 20 publications

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“…The combination of statistical and FD features significantly outperformed the best reported features including PSD features, with FD feature improving the performance when combined due to its non-linear property. SVM classifier performed better than k-NN classifier with an average higher accuracy of 9.56% for 4 classes, which agrees with findings in [23].…”

Section: Resultssupporting

confidence: 93%

“…A 5 fold cross validation was performed using Support Vector Machine (SVM) and k Nearest Neighbors (k-NN). Both classifiers have been reported to be reliable for classification of EEG emotion data [23]. In this study, classification was performed for 4 classes and 2 classes with the aim of comparing accuracy between classifiers for mental workload data from EEG.…”

Section: B Eeg Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

EEG-based mental workload recognition related to multitasking

Lim

Sourina

Liu

et al. 2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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Mental workload can be recognized from Electroencephalogram (EEG) and can be used to assess mental efforts of the user performing different tasks. In this work, we designed and implemented an experiment for mental workload recognition related to no-task, visual task, auditory task and multitask performance. The Simultaneous Capacity SIMKAP test was used to induce different levels of mental workload related to multitasking in 12 subjects. EEG data was collected with Emotiv device, processed and analyzed using power, statistical, fractal dimension (FD) features with Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classifiers. The best accuracy of 90.39% for 2 classes and 80.09% for 4 classes using SVM was achieved when statistical and FD feature combination was used. The proposed algorithm can be applied for mental workload monitoring

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Section: Resultssupporting

confidence: 93%

Section: B Eeg Data Processingmentioning

confidence: 99%

EEG-based mental workload recognition related to multitasking

Lim

Sourina

Liu

et al. 2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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“…With respect to affective states recognition, [39] achieved an accuracy of 86.52% for distinguishing music likability. The best accuracy achieved in identifying emotional states was up to 77.78% over 5 subjects and 56.1% over 15 subjects in [40]. In another study, emotion recognition accuracy reached up to 83.33% for distinguishing 6 emotions and 100% for distinguishing fewer emotions [24].…”

Section: Discussionmentioning

confidence: 99%

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

Fan

Wade

Key

et al. 2018

IEEE Trans. Biomed. Eng.

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objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.

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“…Specifically, it is applied for concentration degree measurement, depression diagnosis, stroke diagnosis, progressive bulbar palsy, spinal muscular atrophy, degenerative brain disease, and cranial nerve disease that have motor disturbance [5]. Additionally, EEG is passible the evaluation with reliability than the other bio-signal according to the research results emerged which a part to control the emotion exists on area of brain, hence the research to cognize the emotional state based on the EEG are consistently proceeding [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Passive EEG-based Emotional state Cognition Using Subject-independent Feature Extraction

Kim¹,

Nguyen²

2017

IJMLC

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Abstract-Research on emotion recognition technologies, whereby emotional state is read off via using voice, facial image, bio-signal and electroencephalogram (EEG), has received considerable attention due to its prevalence in various applications including entertainment, health care, on-line education, automobile, marketing research, to robot and etc. Among the emotional state cognitive technologies utilizing the biometric data, the study on EEG is one of the most widely used. However, EEG-based emotion recognition meets challenges because of multi-channels time series data structure in which contains abnormal characteristics changing the statistical component over time. Consequently, this leads to investigate towards appropriate feature extraction approach in order to be more versatile data analysis to capture underlying information for cognitive the emotional state from EEG signals. In this paper, a subject-independent multiclass-common spatial patterns (Si-MCSP) based on passive EEG therefore is proposed to cognitive the emotional states. Si-MCSP can extract the prominent features in the terms of the subject-independence with respect to the each emotional state. Furthermore, Si-MCSP can automatically determine the numbers of the optimal features which provide an improved classification system of emotional state dramatically. Multiclass Support Vector Machine (Multi-SVM) model then is set up to classify these features for emotion recognition. The experimental results show that the proposed feature extraction method, Si-MCSP, performs higher classification accuracy than the popular feature extraction methods.

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Evaluating Classifiers for Emotion Recognition Using EEG

Cited by 75 publications

References 20 publications

EEG-based mental workload recognition related to multitasking

EEG-based mental workload recognition related to multitasking

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

Passive EEG-based Emotional state Cognition Using Subject-independent Feature Extraction

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