The integration of brain-computer interfaces (BCIs) into the realm of smart wheelchair (SW) technology signifies a notable leap forward in enhancing the mobility and autonomy of individuals with physical disabilities. BCIs are a technology that enables direct communication between the brain and external devices. While BCIs systems offer remarkable opportunities for enhancing human-computer interaction and providing mobility solutions for individuals with disabilities, they also raise significant concerns regarding security, safety, and privacy that have not been thoroughly addressed by researchers on a large scale. Our research aims to enhance wheelchair control for individuals with physical disabilities by leveraging electroencephalography (EEG) signals for BCIs. We introduce a non-invasive BCI system that utilizes a neuro-signal acquisition headset to capture EEG signals. These signals are obtained from specific brain activities that individuals have been trained to produce, allowing for precise control of the wheelchair. EEG-based BCIs are instrumental in capturing the brain's electrical activity and translating these signals into actionable commands. The primary objective of our study is to demonstrate the system's capability to interpret EEG signals and decode specific thought patterns or mental commands issued by the user. By doing so, it aims to convert these into accurate control commands for the wheelchair. This process includes the recognition of navigational intentions, such as moving forward, backward, or executing turns, specifically tailored for wheelchair operation. Through this innovative approach, we aim to create a seamless interface between the user's cognitive intentions and the wheelchair's movements, enhancing autonomy and mobility for individuals with physical disabilities.

翻译：脑机接口（BCI）与智能轮椅（SW）技术的融合，在提升肢体残疾人士的行动能力与自主性方面标志着重大突破。BCI技术能够实现大脑与外部设备的直接通信。尽管BCI系统为增强人机交互、为残障人士提供行动解决方案创造了显著机遇，但其在安全性、可靠性与隐私保护方面引发的重大隐患尚未得到研究者的大规模深入探讨。本研究旨在通过利用脑电图（EEG）信号优化BCI系统，从而增强肢体残疾人士的轮椅控制能力。我们提出一种基于神经信号采集头戴式设备的非侵入式BCI系统来捕捉EEG信号。这些信号源自经训练产生的特定脑部活动，可实现对轮椅的精准控制。基于EEG的BCI技术能够有效捕捉大脑电信号，并将其转化为可执行指令。本研究的主要目标在于验证该系统解读EEG信号、解码用户特定思维模式或心理指令的能力，进而将这些认知信号转换为轮椅的精确控制指令。该过程包含对操作轮椅所需的导航意图识别，例如前移、后退及转向等动作。通过这一创新方法，我们致力于构建用户认知意图与轮椅运动之间的无缝接口，最终提升肢体残疾人士的自主性与行动能力。