Corrigendum: Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way

López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; Guilera, Teresa; Batalla, Iolanda; Soler-González, Jorge; Garcı́a, Roberto

doi:10.3389/fncom.2016.00119

Cited by 13 publications

(7 citation statements)

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“…Besides that, emotion is also another essential issue need to be addressed, and so far, there is lack of research to tackle this challenge. A complex psychological state known as emotion, which involves three unique components: a physiological response, a behavioural response, and a subjective experience [ 16 ]. Four critical points such as the environment and equipment setting, emotion elicitation procedure, evaluation of categories of stimuli, and evaluation of individual differences should take into account to ensure the recorded EEG signals are related to the emotion rather than physical features [ 17 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour

Liew,

Choo,

Low

et al. 2023

Brain Inf.

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This paper aims to design distraction descriptor, elicited through the object variation, to refine the granular knowledge incrementally, using the proposed probability-based incremental update strategy in Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique. Most of the brainprint authentication models were tested in well-controlled environments to minimize the influence of ambient disturbance on the EEG signals. These settings significantly contradict the real-world situations. Thus, making use of the distraction is wiser than eliminating it. The proposed probability-based incremental update strategy is benchmarked with the ground truth (actual class) incremental update strategy. Besides, the proposed technique is also benchmarked with First-In-First-Out (FIFO) incremental update strategy in K-Nearest Neighbour (KNN). The experimental results have shown equivalence discriminatory performance in both high distraction and quiet conditions. This has proven that the proposed distraction descriptor is able to utilize the unique EEG response towards ambient distraction to complement person authentication modelling in uncontrolled environment. The proposed probability-based IncFRNN technique has significantly outperformed the KNN technique for both with and without defining the window size threshold. Nevertheless, its performance is slightly worse than the actual class incremental update strategy since the ground truth represents the gold standard. In overall, this study demonstrated a more practical brainprint authentication model with the proposed distraction descriptor and the probability-based incremental update strategy. However, the EEG distraction descriptor may vary due to intersession variability. Future research may focus on the intersession variability to enhance the robustness of the brainprint authentication model.

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Section: Literature Reviewmentioning

confidence: 99%

Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour

Liew,

Choo,

Low

et al. 2023

Brain Inf.

View full text Add to dashboard Cite

show abstract

“…They successfully classified four different emotions with an accuracy of more than 85% using a multimodal neural network, outperforming both single modalities (Zheng et al, 2019). Another study on multimodal emotion recognition was conduced by López-Gil et al (2016) who found that combining different signal sources on the feature level enables the detection of self-regulatory behavior more effectively than only using EEG data. Most recently, Wu et al (2021) fused EEG and eye tracking data for emotion classification using effective deep learning for a gradient neural network.…”

Section: Eeg and Eye Tracking Based Mental State Detectionmentioning

confidence: 99%

Multimodal EEG and Eye Tracking Feature Fusion Approaches for Attention Classification in Hybrid BCIs

Vortmann

Majed

Putze

2022

Front. Comput. Sci.

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Often, various modalities capture distinct aspects of particular mental states or activities. While machine learning algorithms can reliably predict numerous aspects of human cognition and behavior using a single modality, they can benefit from the combination of multiple modalities. This is why hybrid BCIs are gaining popularity. However, it is not always straightforward to combine features from a multimodal dataset. Along with the method for generating the features, one must decide when the modalities should be combined during the classification process. We compare unimodal EEG and eye tracking classification of internally and externally directed attention to multimodal approaches for early, middle, and late fusion in this study. On a binary dataset with a chance level of 0.5, late fusion of the data achieves the highest classification accuracy of 0.609–0.675 (95%-confidence interval). In general, the results indicate that for these modalities, middle or late fusion approaches are better suited than early fusion approaches. Additional validation of the observed trend will require the use of additional datasets, alternative feature generation mechanisms, decision rules, and neural network designs. We conclude with a set of premises that need to be considered when deciding on a multimodal attentional state classification approach.

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“…Relatedly, EEG was also employed in [14] where data acquired from students during a college lecture was used to classify the degree of situational interest of each participant, using conventional classifiers such as kNN. Other methods for interest detection have involved technologies such as eye tracking [15] and manufactured expression/pose metrics [16], though less frequently.…”

Section: Human Interest Metricsmentioning

confidence: 99%

Crowd Interest Mapping to Assess Engagement

2022

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Emotionally pleasant experiences trigger repetition in humans whilst their emotional opposite lead to avoidance/refusal of activities. Human interest, a key factor in everyday human life, can be quite useful in the evaluation of participant engagement on activities and their optimization, so as to maximize interest and motivation. A glaring issue however comes from subtle demonstrators associated with human interest, which converge onto its complex assessment/quantization. Yet, there is an inherent correlation between human interest and its provoked emotional response which can be explored in tandem with emotion recognition for the development of an engagement metrics tool. Such a mechanism would be highly beneficial for the improvement of several activities involved with learning and security, by enabling precise control over participant enthusiasm. In this paper we present an interest mapping technique which provides the user with spatio-temporal information extracted from a participant crowd. The technique aims to extract emotional cues from participant facial data, assessing its spatial and temporal distributions over the course of scenarios such as therapy sessions and lectures. The goal is to demonstrate where and when activities must be improved in order to retain attention, maximize efficacy and assure emotional pleasantness in participants. For validation, this study makes use of data collected over a college lecture so as to provide readers with a real demonstration of the technique's advantages.

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Corrigendum: Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way

Cited by 13 publications

References 0 publications

Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour

Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour

Multimodal EEG and Eye Tracking Feature Fusion Approaches for Attention Classification in Hybrid BCIs

Crowd Interest Mapping to Assess Engagement

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