P300 speller-based brain–computer interface (BCI) is an immediate correspondence between the human brain and computer that depends on the translation of mind reactions produced by the stimulus of a subject utilising the P300 speller. No muscle movements are required for this communication. As a P300 paradigm, a novel 2 × 3 matrix consisting of visual home appliances is proposed, which helps disabled people ease their lives by accessing mobile, light, fan, door, television, electric heater etc. In most of the current P300-based BCIs, 5–15 trials work better and the low information transfer rate (ITR) is a major issue in its adaptation in real-time. The objective of this Letter is to improve accuracy as well as an ITR for real-time home appliance control applications. To address this, the authors proposed a single trial weighted ensemble of compact convolution neural network and obtained an ITR of 46.45 bits per minute and an average target appliance accuracy of 93.22% for the BCI-based home environment system. The experimental findings confirmed the feasibility of the proposed method and thus can provide guidance for future use of the system for paralysed patients.

中文翻译：

---

基于脑机接口的单次试验P300检测家居环境应用

基于 P300 拼写器的脑机接口 (BCI) 是人脑和计算机之间的直接对应关系，它依赖于使用 P300 拼写器的受试者刺激产生的思维反应的翻译。这种交流不需要肌肉运动。作为 P300 范式，提出了一种由视觉家电组成的新型 2×3 矩阵，通过访问手机、灯、风扇、门、电视、电暖器等帮助残疾人减轻生活。 BCI，5-15 次试验效果更好，低信息传输率 (ITR) 是其实时适应的主要问题。这封信的目的是提高实时家电控制应用的准确性和 ITR。为了解决这个问题，作者提出了紧凑卷积神经网络的单次试验加权集成，并获得了每分钟 46.45 位的 ITR 和基于 BCI 的家庭环境系统的 93.22% 的平均目标设备准确率。实验结果证实了所提出方法的可行性，因此可以为未来将该系统用于瘫痪患者提供指导。