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Description
The effectiveness of EEG based cross-task mental workload evaluation are still unsatisfactory because of the different EEG response patterns in different tasks, which hindered its generalization in real scenario severely. To overcome this problem, in this process, the feature construction method based on EEG tensor representation and transfer learning, which was verified in various task conditions. At First, the original input EEGs are applied to the pre-processing, based on the filtering method to remove the noise from the signal. Then signal is transformed into Alpha, Beta, Gamma, Theta and Delta. At second, the features are extracted by means of the CNN algorithm. Then the classification is used to classify the signal is stress or non-stress. The performance will be evaluated to identify the efficiency of the process.
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