Performance Enhancement of P300 Detection by Multiscale-CNN

The P300-based spelling system is one of the most popular brain-computer interface applications. Its success largely depends on performance, including the information transmission rate (ITR) and detection rate (i.e., accuracy). To achieve good performance, we proposed a multiscale convolutional neural network (MS-CNN) model that consists of seven layers. First, an upfront data set was used to train the MS-CNN, aiming to obtain a subject-unspecific model (universal model) for P300 detection. Second, this universal model was adapted by a portion of data derived from a subject to update the model to obtain a subject-specific model by incorporating a transfer learning technique. We applied the proposed model in the brain-computer interface (BCI) Controlled Robot Contest at the 2019 World Robot Conference, and our performance was the best among the teams in the contest. In the contest, ten healthy young subjects were randomly assigned by the contest committee to assess the model. Our model achieved the best P300 detection performance (higher accuracy with less repetition time). The ITR for the subject-unspecific case was 13.49 hits/min, while the ITR for the subject-specific case was 12.13 bits/min when the repetitions were fewer than six. It is believed that our method may pave a promising path for taking a further step toward efficient implementation of the P300-based spelling system.

Automated summary

The study introduced a multiscale convolutional neural network model, successfully applied to P300 detection and achieving the best performance in a robot contest, laying a promising foundation for the efficient implementation of P300-based spelling systems.