Electroencephalogram (EEG)-based brain–machine interface (BMI) has the potential to enhance rehabilitation training efficiency, but it still remains elusive regarding how to design BMI training for heterogeneous stroke patients with varied neural reorganization. Here, we hypothesize that tailoring BMI training according to different patterns of neural reorganization can contribute to a personalized rehabilitation trajectory. Thirteen stroke patients were recruited in a 2-week personalized BMI training experiment. Clinical and behavioral measurements, as well as cortical and muscular activities, were assessed before and after training. Following treatment, significant improvements were found in motor function assessment. Three types of brain activation patterns were identified during BMI tasks, namely, bilateral widespread activation, ipsilesional focusing activation, and contralesional recruitment activation. Patients with either ipsilesional dominance or contralesional dominance can achieve recovery through personalized BMI training. Results indicate that personalized BMI training tends to connect the potentially reorganized brain areas with event-contingent proprioceptive feedback. It can also be inferred that personalization plays an important role in establishing the sensorimotor loop in BMI training. With further understanding of neural rehabilitation mechanisms, personalized treatment strategy is a promising way to improve the rehabilitation efficacy and promote the clinical use of rehabilitation robots and other neurotechnologies.

In order to streamline and summarize the status quo of human–computer interaction (HCI) design research in minimally invasive surgery robots, and to inspire and promote in-depth design research in related fields, this study utilizes literature research methods, inductive summarizing methods, and comparative analysis methods to analyze and organize the usage scenarios, users, interaction content and form, and relevant design methods of minimally invasive surgery robots, with the purpose of arriving at a review. Through a summary method, this study will obtain outcomes such as design requirements, interaction information classification, and the advantages and disadvantages of different interaction forms, and then make predictions of future trends in this field. Research findings show that the HCI design in the relevant field display a highly intelligent, human-centered, and multimodal development trend through the application of cutting-edge technology, taking full account of work efficiency and user needs. However, meanwhile, there are problems such as the absence of guidance by a systematic user knowledge framework and incomplete design evaluation factors, which need to be supplemented and improved by researchers in related fields in the future.