A Wearable EEG Instrument for Real-Time Frontal Asymmetry Monitoring in Worker Stress Analysis

Arpaïa, Pasquale; Moccaldi, Nicola; Prevete, Roberto; Sannino, Isabella; Tedesco, Annarita

doi:10.1109/tim.2020.2988744

Cited by 83 publications

(24 citation statements)

References 39 publications

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“…If an assessment is done discretely, it is not promising for practical application. Nowadays, neurophysiological signals are frequently used to monitor mental states as they can be measured continuously [5]- [8]. Using such signals, the mental workload can be assessed in real-time.…”

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confidence: 99%

EEG-Based Multiclass Workload Identification Using Feature Fusion and Selection

Pei

Wang

Bezerianos

et al. 2021

IEEE Trans. Instrum. Meas.

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The effectiveness of workload identification is one of the critical aspects in a monitoring instrument of mental state. In this field, the workload is usually recognised as binary classes. There are scarce studies towards multi-class workload identification because the challenge of the success of workload identification is much tough, even though one more workload class is added. Besides, most of the existing studies only utilized spectral power features from individual channels but ignoring abundant inter-channel features that represent the interactions between brain regions. In this study, we utilized features representing intra-channel information and inter-channel information to classify multiple classes of workload based on EEG. We comprehensively compared each category of features contributing to workload identification and elucidated the roles of feature fusion and feature selection for the workload identification. The results demonstrated that feature combination (83.12% in terms of accuracy) enhanced the classification performance compared to individual feature categories (i.e., band power features, 75.90%; connection features, 81.72%, in terms of accuracy). With the Fscore feature selection, the classification accuracy was further increased to 83.47%. When the features of graph metric were fused, the accuracy was reached to 84.34%. Our study provided comprehensive performance comparisons between methods and feature categories for the multi-class workload identification and demonstrated that feature selection and fusion played an important role in the enhancement of workload identification. These results could facilitate further studies of multi-class workload identification and practical application of workload identification.

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confidence: 99%

EEG-Based Multiclass Workload Identification Using Feature Fusion and Selection

Pei

Wang

Bezerianos

et al. 2021

IEEE Trans. Instrum. Meas.

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“…Traditional strategies for EEG signal feature extraction, combined with a-priori knowledge on spatial and frequency phenomena related to emotions, can be unusable in case of few electrodes. In a previous work of the Authors, for a single-channel stress detection instrument, a-priori spatial knowledge drove electrodes positioning 57 . However, signal processing was based on innovative and not well-settled strategies.…”

Section: Related Workmentioning

confidence: 99%

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

Apicella

Arpaïa

Mastrati

et al. 2021

Preprint

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An emotional-valence detection method for a very–high wearable EEG-based system is proposed. Valence detection occurs along the interval scale theorized by the circumplex model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. Wearability is guaranteed by a wireless cap with conductive-rubber dry electrodes and 8 data acquisition channels. Experimental validation was realized on 25 volunteers without depressive disorders. The metrological reference was built by combining the rating of a standardized set of pictures from dataset Oasis and results from the Self Assessment Manikin questionnaire. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e. Hemispheric Asymmetry Theories) and (ii) automated. A pipeline of a custom 12-band Filter Bank and Common Spatial Pattern algorithm is the method proposed for automated feature extraction. Four machine learning classifiers were tested for validating the proposed method in discriminating two classes (high or low emotional valence). An intra-individual average accuracy, 96.2 %, was obtained by a shallow artificial neural network, while K-Nearest Neighbors allowed obtaining 80.3 % of inter-individual accuracy. Considering the wearability, and the well-founded metrological reference, results are the state of the art in emotional valence detection.

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“…It is clear that the topographies and responses corresponding to limb movements obtained from the beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) rhythm are distinct in comparison to the mu (8)(9)(10)(11)(12)(13) rhythm. During limb movements, an increase in the oscillatory power is seen in the beta rhythm in the ipsilateral sensorimotor cortex.…”

Section: Introductionmentioning

confidence: 99%

“…Another group developed a wearable EEG-based device used to assess stress conditions in real-time EEG. The amplitude variations in the EEG signals recorded between the left and right region of prefrontal areas were analyzed using a single EEG differential channel [14]. Another research group also proposed a design using off-the-shelf components with SSVEP-based instrument to improve the performance of a real-time application in BCI [15].…”

Section: Introductionmentioning

confidence: 99%

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Gaur

Gupta

Chowdhury

et al. 2021

IEEE Trans. Instrum. Meas.

136

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Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain computer interface (BCI) systems. In this study two sliding window techniques are proposed to enhance binary classification of motor imagery (MI). The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows which is named as SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a dataset of healthy individuals and on a stroke patients dataset. As compared to the existing state-of-the-art the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients dataset for left vs. right hand MI with lower standard deviation. For both the datasets the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window based prediction of MI using SW-LCR and SW-Mode is robust against inter-trial and inter-session inconsistencies in the time of activation within a trial and thus can lead to reliable performance in a neurorehabilitative BCI setting.

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A Wearable EEG Instrument for Real-Time Frontal Asymmetry Monitoring in Worker Stress Analysis

Cited by 83 publications

References 39 publications

EEG-Based Multiclass Workload Identification Using Feature Fusion and Selection

EEG-Based Multiclass Workload Identification Using Feature Fusion and Selection

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

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