Custom Domain Adaptation: A New Method for Cross-Subject, EEG-Based Cognitive Load Recognition

Jiménez-Guarneros, Magdiel; Gómez-Gil, Pilar

doi:10.1109/lsp.2020.2989663

Cited by 47 publications

(32 citation statements)

References 31 publications

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“…The authors proposed a novel approach consisting of two steps that included the initial training of a set of participantspecific classifiers and then combining the trained classifiers to address the problem of subject-independent cognitive workload estimation. In [49], Custom Domain Adaptation (CDA) was used to develop a highly efficient classifier, which was trained with the same dataset as it was applied in [47]. [17] 3, difficulty split kNN, 0.332 (F score) si [36] 2, difficulty split LDA 0.91 (ACC) si [37] 2, difficulty split SVM 0.52 (ACC) si [38] 1 (…”

Section: Related Workmentioning

confidence: 99%

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021

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The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). Understanding cognitive workload is of great importance in analysing human mental fatigue and the performance of intellectual tasks. It is also essential in the context of explanation of the brain cognitive process. Eight three-class classification machine learning models were constructed and analysed. Furthermore, the technique of interpretable machine learning model was applied to obtain the measures of feature importance and its contribution to the brain cognitive functions. The measures allowed improving the quality of classification, simultaneously lowering the number of applied features to six or eight, depending on the model. Moreover, the applied method of explainable machine learning provided valuable insights into understanding the process accompanying various levels of cognitive workload. The main classification performance metrics, such as F1, recall, precision, accuracy, and the area under the Receiver operating characteristic curve (ROC AUC) were used in order to assess the quality of classification quantitatively. The best result obtained on the complete feature set was as high as 0.95 (F1); however, feature importance interpretation allowed increasing the result up to 0.97 with only seven of 20 features applied.

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Section: Related Workmentioning

confidence: 99%

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021

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“…There were two main steps; the first was to pre-train the source domain, and then the Wasserstein algorithm was used for adversarial training to adapt the target domain to the source domain. Similar to the WGAN framework, Jimenez-Guarneros and Gomez-Gil ( 2020 ) proposed a custom domain adaptive method (CDA). This method used adaptive batch normalization (AdaBN) (Li et al, 2018 ) and MMD in two independent networks to reduce the marginal and conditional distribution of the source and target domains.…”

Section: Introductionmentioning

confidence: 99%

Two-Level Domain Adaptation Neural Network for EEG-Based Emotion Recognition

Bao

Zhuang

et al. 2021

Front. Hum. Neurosci.

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Emotion recognition plays an important part in human-computer interaction (HCI). Currently, the main challenge in electroencephalogram (EEG)-based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network (TDANN) to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are then passed through TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy (MMD) to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network (DANN) to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness (84%), anger (87.04%), and fear (85.32%) on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness (83.79%), anger (84.13%), and fear (81.72%) on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition.

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“…Li et al and Bao et al investigated multi-source transfer learning and two-level domain adaptation neural networks, respectively, for cross-subject EEG emotion recognition (Li et al, 2019 ; Bao et al, 2021 ). Jimenez-Guarneros et al proposed custom domain adaptation for cross-subject cognitive load recognition (Jimenez-Guarneros and Gomez-Gil, 2020 ). McKendrick et al focused exclusively on labeling cognitive load data for supervised three-state classification (McKendrick et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection

Song

Zeng

et al. 2021

Front. Hum. Neurosci.

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Single-trial electroencephalogram detection has been widely applied in brain-computer interface (BCI) systems. Moreover, an individual generalized model is significant for applying the dynamic visual target detection BCI system in real life because of the time jitter of the detection latency, the dynamics and complexity of visual background. Hence, we developed an unsupervised multi-source domain adaptation network (P3-MSDA) for dynamic visual target detection. In this network, a P3 map-clustering method was proposed for source domain selection. The adversarial domain adaptation was conducted for domain alignment to eliminate individual differences, and prediction probabilities were ranked and returned to guide the input of target samples for imbalanced data classification. The results showed that individuals with a strong P3 map selected by the proposed P3 map-clustering method perform best on the source domain. Compared with existing schemes, the proposed P3-MSDA network achieved the highest classification accuracy and F1 score using five labeled individuals with a strong P3 map as the source domain. These findings can have a significant meaning in building an individual generalized model for dynamic visual target detection.

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Custom Domain Adaptation: A New Method for Cross-Subject, EEG-Based Cognitive Load Recognition

Cited by 47 publications

References 31 publications

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

Two-Level Domain Adaptation Neural Network for EEG-Based Emotion Recognition

P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection

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