RESUMO
Decoding asynchronous electroencephalogram (A-EEG) signals is a crucial challenge in the emerging field of EEG based brain-computer interface. In the case of A-EEG signals, the time markers of motor activity are absent. The paper proposes a method to decompose the A-EEG signals using gabor elementary function designed with Gabor frames. The scale-space analysis extracts Gabor dominant frequencies from A-EEG signals. Statistical and temporal moment dependent features are used to create the feature vector for each estimated gabor band. The statistical significance of the features is tested with the Kruskal-Wallis test. The deep neural network is implemented with bi-directional long short-term memory block to classify the upper limb movement. The EEG data of healthy volunteers have been collected using the Enobio-20 electrode system and ArmeoSpring rehabilitation device. The proposed methodology has achieved an average classification accuracy of 96.83%, precision 0.96, recall 0.96, and F1-score of 0.93 on the acquired data set. The designed framework for decoding upper limb movement outperforms the existing state-of-the-art methods. In the future, the proposed framework could increase classification performance by incorporating multiple types of biological inputs for investigating various brain functions.