Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment

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

doi:10.1109/tnsre.2019.2913400

Cited by 73 publications

(28 citation statements)

References 41 publications

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“…Their results showed that this architecture can achieve higher accuracy for fatigue classification, when compared to these networks that replace convolutional block by 1D convolution [109]. Zhang et al proposed a two-stream CNN network to learn spectral and temporal features [118]. One stream of CNN was fed by power spectral density topographic maps and the other was fed by topographic maps of amplitude distributions.…”

Section: Operator Functional Statesmentioning

confidence: 99%

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Gong

Xing

Cichocki

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.

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Section: Operator Functional Statesmentioning

confidence: 99%

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Gong

Xing

Cichocki

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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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%

“…where y rx,k is the reconstructed received signal of the no-failure sub-array. For communication signals, it is reconstructed with the decoded communication symbols y est , (18) while, for sensing echoes, it is reconstructed with the known transmitted symbols and the estimated noise power σ n…”

Section: Recognition Of No-failure Subarraymentioning

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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“…Based on the above analysis, the latest temporal convolutional network (TCN) model is introduced. The model is used extensively in many fields such as pattern recognition [25], [26], anomaly detection [27] and mental assessment [28], but its application to the feature extraction task for load forecasting is relatively limited. Due to the integration of both the parallel feature processing of the CNN and the time-domain modeling capability of the RNN [29], the TCN is superior in extracting long-term time series features.…”

Section: Introductionmentioning

confidence: 99%