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.

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Brain–computer interface-based single trial P300 detection for home environment application

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