SYSTEM FOR CLASSIFICATING MENTAL WORKLOAD USING EEG AND METHOD THEREOF

摘要

The present technology discloses a brain cognitive load classification system and method. According to a specific example of the present technology, each multi-level feature of each layer is derived through a predetermined number of convolutions on input data of a three-dimensional image including spectral and spatial information of an input EEG signal, and the derived multi-level Each weight of each multi-level feature is derived based on the parameters optimized through features and learning, and a log of each multi-level feature is derived by multiplying the derived weight and multi-level feature, and the log of each derived multi-level feature is derived. The angular loss of each multi-level feature is calculated by applying the classification loss function to Accordingly, the classification accuracy of the cognitive load of the brain can be improved, and the learning speed can be increased by determining the optimized weight by learning the weight based on the EEG signal, thereby improving the performance of the system.