Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment

Zhang, Pengbo; Wang, Xue; Zhang, Weihang; Chen, Junfeng

doi:10.1109/tnsre.2018.2884641

Cited by 187 publications

(126 citation statements)

References 42 publications

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“…Therefore, the application of Machine Learning has been considered the solution for classifying the workload and overcoming these issues typical of real applications. The preliminary analysis of the works carried out so far in this context has shown that it is possible to discriminate with acceptable accuracy only two levels of workload [6], [18], [22], [24], [25], [27]- [29], [33], [36]- [39], [42], [45], [46], [58], even though, above all in view of a practical application of the workload measurement, it is necessary to establish at least the value of two thresholds to define the underload and the overload state. The most frequently employed features are the spectral ones, because they can be calculated with a high temporal resolution (up to one second) and allow to monitor brain activity in a quantitative manner without temporal triggers.…”

Section: Discussionmentioning

confidence: 99%

“…The process that leads from the recording of EEG signals to an indication of the workload level passes through the use of signal analysis methods that allow to extract the informative features of the phenomenon to be investigated. Regarding the measurement of the workload have been used in several studies both spectral, temporal and spatial features [33]. The use of spectral features remains the most suitable for the temporal continuity required by the workload monitoring, since the brain activity induces variations in its spectral power which, unlike ERPs used for time domain analysis [34], does not need to be triggered with a certain timing [35].…”

Section: Machine Learning To Get Back Out-of-the-labmentioning

confidence: 99%

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On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Sciaraffa

Aricò

Borghini

et al. 2019

Communications in Computer and Information Science

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The measurement of the mental workload during real tasks by means of neurophysiological signals is still challenging. The employment of Machine Learning techniques has allowed a step forward in this direction, however, most of the work has dealt with binary classification. This study proposed to examine the surveys already performed in the context of EEG-based workload classification and to test different machine learning algorithms on real multitasking activity like the Air Traffic Management. The results obtained on 35 professional Air Traffic Controllers showed that a KNN algorithm allows discriminating up to three workload levels (low, medium and high) with more than 84 % of accuracy on average. Moreover, in such realistic employment it emerges how important is to opportunely choose the set of features to ward off that task-related confounds could affect the workload assessment.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning To Get Back Out-of-the-labmentioning

confidence: 99%

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Sciaraffa

Aricò

Borghini

et al. 2019

Communications in Computer and Information Science

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“…They found that the signals collected from the central (C3 and C4) regions are marginally higher compared with other brain regions, which can be used to distinguish the depressed and normal subjects from the brain wave signals. Zhang et al 45 proposed a concatenated structure of deep recurrent and 3D CNN to obtain EEG features across different tasks. They reported that the DL model can capture the spectral changes of EEG hemispheric asymmetry to distinguish different mental workload effectively.…”

Section: Electroencephalogram Datamentioning

confidence: 99%

Deep learning in mental health outcome research: a scoping review

et al. 2020

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Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

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“…Feature extraction is extremely critical for machine learning algorithms [18,19], and SVM algorithms are no exception. In this paper, to characterize the failure pattern of the sub-array, the feature vectors are constructed based on the received signals of each array element as well as the estimated DOAs.…”

Section: M-dimensional Classification Vector For Svmmentioning

confidence: 99%

Low-Complexity Failed Element Diagnosis for Radar-Communication mmWave Antenna Array with Low SNR

Dai

Zhang

et al. 2019

Electronics

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The millimeter-wave (mmWave) antenna array plays an important role in the excellent performance of wireless sensors networks (WSN) or unmanned aerial vehicle (UAV) clusters. However, the array elements are easily damaged in its harsh working environment but hard to be repaired or exchanged timely, resulting in a serious decline in the beamforming performance. Thus, accurate self-diagnosis of the failed elements is of great importance. In previous studies, there are still significant difficulties for large-scale arrays under extremely low SNR. In this paper, a diagnosis algorithm with low complexity and high reliability for the failed elements is proposed, which is based on a joint decision of communication signal and sensing echoes. Compared with the previous studies, the complexity of the algorithm is reduced by the construction of low-dimensional feature vectors for classification, the decoupling of the degree of arrival (DOA) estimation and the failed pattern diagnosis, with the help of the sub-array division. Simulation results show that, under an ultra-low SNR of −12.5 dB for communication signals and −16 dB for sensing echoes, an accurate self-diagnosis with a block error rate lower than 8% can be realized. The study in this paper will effectively promote the long-term and reliable operation of the mmWave antenna array in WSN, UAV clusters and other similar fields.

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Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment

Cited by 187 publications

References 42 publications

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Deep learning in mental health outcome research: a scoping review

Low-Complexity Failed Element Diagnosis for Radar-Communication mmWave Antenna Array with Low SNR

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