The precision and reliability of electroencephalogram (EEG) data are essential for the effective functioning of a brain-computer interface (BCI). As the number of BCI acquisition channels increases, more EEG information can be gathered. However, having too many channels will reduce the practicability of the BCI system, raise the likelihood of poor-quality channels, and lead to information misinterpretation. These issues pose challenges to the advancement of BCI systems. Determining the optimal configuration of BCI acquisition channels can minimize the number of channels utilized, but it is challenging to maintain the original operating system and accommodate individual variations in channel layout. To address these concerns, this study introduces the EEG-completion-informer (EC-informer), which is based on the Informer architecture known for its effectiveness in time-series problems. By providing input from four BCI acquisition channels, the EC-informer can generate several virtual acquisition channels to extract additional EEG information for analysis. This approach allows for the direct inheritance of the original model, significantly reducing researchers’ workload. Moreover, EC-informers demonstrate strong performance in damaged channel repair and poor channel identification. Using the Informer as a foundation, the study proposes the EC-informer, tailored to BCI requirements and demanding only a small number of training samples. This approach eliminates the need for extensive computing units to train an efficient, lightweight model while preserving comprehensive information about target channels. The study also confirms that the proposed model can be transferred to other operators with minimal loss, exhibiting robust applicability. The EC-informer’s features enable original BCI devices to adapt to a broader range of classification algorithms and relax the operational requirements of BCI devices, which could facilitate the promotion of the use of BCI devices in daily life.

In the process of high-speed penetration, penetrating ammunition is prone to problems such as penetration overload signal vibration and mixings and projectile attitude deflection. It is easy to misjudge if a fuze relies only on the overload data from the ground or the utilized program, and the actual penetration overload measured under actual launch conditions cannot be taken as the dynamic judgement basis. Therefore, a real-time penetration overload prediction method based on a deep neural network is proposed, which can predict overload values according to the projectile parameter settings, the real-time collection of overload information, and the calculation speed and assist the fuze in judging the target layer and projectile attitude. In this paper, we adopt a deep learning model with multiple time series inputs and modify the input coding mode so that the model can output a 48 us overload curve within 20 us, meeting the real-time signal processing requirements of the high-speed missile penetration process. The mean squared error between the predicted curve and the actual curve is 0.221 for the prediction of multilayer penetrating targets and 0.452 for the prediction of thick penetrating targets. A penetration overload prediction function can be realized.