A brain-computer interface based semi-autonomous robotic system

Xu, Dongcen; Tong, Yixuan; Dong, Xuyang; Wang, Cong; Huo, Liangqing; Li, Yiping; Zhang, Qifeng; Xi-sheng, Feng

doi:10.1109/robio54168.2021.9739367

Cited by 1 publication

(1 citation statement)

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“…A brain-computer interface (BCI) provides direct communication between human brains and machines without using peripheral nerves or muscles [1], thus allowing users to use brain signals to control devices such as spelling interfaces [2,3], wheelchairs [4,5], robot arms [6,7], drones [8,9], exoskeletons [10,11] and robots [12,13]. Among the different BCIs, BCIs based on electroencephalography (EEG) are the most widely used due to their convenience, safety, low cost and high temporal resolution [14].…”

Section: Introductionmentioning

confidence: 99%

FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization

Tang

et al. 2023

Brain Sciences

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The brain–computer interface (BCI) provides direct communication between human brains and machines, including robots, drones and wheelchairs, without the involvement of peripheral systems. BCI based on electroencephalography (EEG) has been applied in many fields, including aiding people with physical disabilities, rehabilitation, education and entertainment. Among the different EEG-based BCI paradigms, steady-state visual evoked potential (SSVEP)-based BCIs are known for their lower training requirements, high classification accuracy and high information transfer rate (ITR). In this article, a filter bank complex spectrum convolutional neural network (FB-CCNN) was proposed, and it achieved leading classification accuracies of 94.85 ± 6.18% and 80.58 ± 14.43%, respectively, on two open SSVEP datasets. An optimization algorithm named artificial gradient descent (AGD) was also proposed to generate and optimize the hyperparameters of the FB-CCNN. AGD also revealed correlations between different hyperparameters and their corresponding performances. It was experimentally demonstrated that FB-CCNN performed better when the hyperparameters were fixed values rather than channel number-based. In conclusion, a deep learning model named FB-CCNN and a hyperparameter-optimizing algorithm named AGD were proposed and demonstrated to be effective in classifying SSVEP through experiments. The hyperparameter design process and analysis were carried out using AGD, and advice on choosing hyperparameters for deep learning models in classifying SSVEP was provided.

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