Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals

BACKGROUND AND OBJECTIVE

Evaluation of human cognitive workload (CW) helps improve the user experience of human-centered systems. To provide a continuous estimation of the CW, we built a CW recognizer that maps human electroencephalograms (EEGs) to discrete CW levels with deep learning tools. However, the EEG distribution varies when humans perform different cognitive tasks. There is thus a question on the capacity for generalizing the CW recognizer across tasks. In this study, we examined the CW's performance when it was trained and tested on two EEG databases corresponding to different human-machine tasks.

METHODS

A novel deep neural network-based EEG recognizer, dynamic residual network with attention mechanism (DRNA-Net), is proposed in the present study. By taking advantage of recurrent networks, the DRNA-Net further incorporates a self-attention mechanism in discovering robust EEG patterns across different cognitive tasks.

RESULTS

We designed an experiment that applied a multidimensional N-back task to induce the CW that consists of visual and auditory memory tasks. We validated the cross-task generalization capability of the DRNA-Net based on the EEG features extracted from the N-back task and a public database. The results show that the DRNA-Net achieves classification accuracy and Macro-F1 values are 0.6055 and 0.6067, respectively.

CONCLUSIONS

The performance of the DRNA-Net indicates that it has a certain ability of cross-task cognitive workload classification, which outperforms several shallow learners and deep convolutional neural networks under various conditions of the feature subsets.

oneM2M-Enabled Prediction of High Particulate Matter Data Based on Multi-Dense Layer BiLSTM Model

High particulate matter (PM) concentrations in the cleanroom semiconductor factory have become a significant concern as they can damage electronic devices during the manufacturing process. PM can be predicted before becoming more concentrated based on its historical data to support factory management in regulating the air quality in the cleanroom. In this paper, a Multi-Dense Layer BiLSTM model is proposed to predict PM2.5 concentrations in the indoor environment of the cleanroom. To obtain reliability, validity, and interoperability data, the datasets containing temperature, humidity, PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10 were retrieved in a standardized manner via oneM2M-defined representational state transfer application programmable interfaces by employing software platforms compliant with the Internet of Things (IoT) standard. Based on the proposed model, an algorithm was built providing short-term PM2.5 concentration predictions (one hour ahead, two hours ahead, and three hours ahead). The proposed model outperformed the RNN, LSTM, CNN-LSTM, and Single-Dense Layer BiLSTM models in terms of MSE, MAE, and MAPE values. The model created in this study could predict high PM2.5 concentration levels more accurately, thus providing vital support for operation and maintenance for the semiconductor industry.