Brain–computer interface‐based target recognition system using transfer learning: A deep learning approach. (19th May 2021)

• Record Type: Journal Article
• Authors: Chen, Ning; Zhang, Yimeng; Wu, Jielong; Zhang, Hongyi; Chamola, Vinay; de Albuquerque, Victor Hugo C.
• Other Names: Lv Zhihan guestEditor.; Lloret Jaime guestEditor.; Song Houbing guestEditor.
• Is Part Of: Computational intelligence. Volume 38:Number 1(2022)
• Page Start: 139
• Page End: 155
• Publication Date: 2021-05-19
• Subjects: brain–computer interface -- deep learning -- target recognition faster‐RCNN -- TensorFlow; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12451
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: The traditional target recognition and classification is mostly done manually, with low efficiency and high cost. Improving the level of target recognition automatically has become an important research topic. This paper proposes a target recognition method based on transfer learning to effectively complete the classification and recognition of targets using a brain–computer interface (BCI) model. Based on the construction of the faster‐RCNN deep learning model, the pre‐training of the model is achieved by VGG‐16 and Inception‐v2, and the transfer learning algorithm is used to optimize the faster‐RCNN deep learning model based on the kinematics model. Experiments are carried out with the aim to detect tableware by the persons whose brain signals recognition rate has been substantially improved using faster‐RCNN. Compared with the traditional recognition methods, the results at the lab‐scale level illustrated that the proposed algorithm can effectively improve the speed and accuracy of target recognition by using the BCI model to classify tableware of different colors and shapes in a complex background.