Image Segmentation-P300 Selector: A Brain–Computer Interface System for Target Selection  

作　　者：Hang Sun Changsheng Li He Zhang 

机构地区：[1]Mechanical Engineering,Nanjing University of Science and Technology,Nanjing,210018,China

出　　处：《Computers, Materials & Continua》2024年第5期2505-2522,共18页计算机、材料和连续体（英文）

摘　　要：Brain–computer interface (BCI) systems, such as the P300 speller, enable patients to express intentions withoutnecessitating extensive training.However, the complexity of operational instructions and the slow pace of characterspelling pose challenges for some patients. In this paper, an image segmentation P300 selector based on YOLOv7-mask and DeepSORT is proposed. The proposed system utilizes a camera to capture real-world objects forclassification and tracking. By applying predefined stimulation rules and object-specificmasks, the proposed systemtriggers stimuli associated with the objects displayed on the screen, inducing the generation of P300 signals in thepatient’s brain. Its video processing mechanism enables the system to identify the target the patient is focusing oneven if the object is partially obscured, overlapped, moving, or changing in number. The system alters the target’scolor display, thereby conveying the patient’s intentions to caregivers. The data analysis revealed that the selfrecognitionaccuracy of the subjects using this method was between 92% and 100%, and the cross-subject P300recognition precision was 81.9%–92.1%. This means that simple instructions such as “Do not worry, just focuson what you desire” effectively discerned the patient’s intentions using the Image Segmentation-P300 selector. Thisapproach provides cost-effective support and allows patients with communication difficulties to easily express theirneeds.

关 键 词：Brain-computer interface deep learning ELECTROENCEPHALOGRAM P300 YOLOv7-mask 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]