In the current societal landscape, the prevalence of disabilities and paralysis is steadily increasing. According to the World Health Organization (WHO) [1, 2], as of 2021, there are approximately 1.3 billion people with disabilities worldwide, with over 500,000 new cases of paralysis reported annually. Similarly, in Vietnam, the General Statistics Office reported that by the end of 2016, the country had approximately 6.2 million disabled individuals, accounting for 7.06% of the population aged 2 and above. Despite these numbers, the availability of assistive systems for individuals with disabilities and paralysis remains limited. In this study, we present the research and development of the Thinking Computer Control System (TCCS), a system of Artificial Intelligence (AI) that aims to assist individuals, particularly those with limited mobility, disabilities, and paralysis, in remotely controlling electronic devices through brainwave analysis. Specifically, we tackle the problem of “Brainwave-to-text conversion” to enable users to control devices using their thoughts. TCCS employs a combination of Long-Short Term Memory, Gated Recurrent Unit, and Dense Layers to create two personalized AI models that cater to individual user requirements. By utilizing the TCCS system, users can convert their brainwave signals into text according to their preferences, which are then transformed into executable commands, facilitating remote control of electronic devices through thoughts, with an accuracy of up to 91.7%. The training and testing datasets for the personalized AI models were collected from individual user using the Muse-2 brainwave measuring device. Overall, TCCS offers an optimized human-machine interaction (HMI) form, saving time, effort, and resources compared to existing global methods. Moreover, the low-cost brainwave measuring device employed by TCCS enables broader accessibility for diverse user populations.