Brain-computer interfaces (BCIs) allow direct communication between the brain and electronics without the need for speech or physical movement. Such interfaces can be particularly beneficial in applications requiring rapid response times, such as driving, where a vehicle's advanced driving assistance systems could benefit from immediate understanding of a driver's intentions. This study presents a novel method for predicting a driver's intention to steer using electroencephalography (EEG) signals through deep learning. A driving simulator created a controlled environment in which participants imagined controlling a vehicle during various driving scenarios, including left and right turns, as well as straight driving. A convolutional neural network (CNN) classified the detected EEG data with minimal pre-processing. Our model achieved an accuracy of 83.7% in distinguishing between the three steering intentions and demonstrated the ability of CNNs to process raw EEG data effectively. The classification accuracy was highest for right-turn segments, which suggests a potential spatial bias in brain activity. This study lays the foundation for more intuitive brain-to-vehicle communication systems.

翻译：脑机接口（BCI）无需言语或肢体动作即可实现大脑与电子设备之间的直接通信。此类接口在需要快速响应的应用场景中尤为有益，例如在驾驶过程中，车辆的先进驾驶辅助系统若能即时理解驾驶员意图，将显著提升性能。本研究提出一种基于深度学习、利用脑电图（EEG）信号预测驾驶员转向意图的新方法。通过驾驶模拟器构建受控环境，参与者在多种驾驶场景（包括左转、右转及直行）中想象操控车辆。采用卷积神经网络（CNN）对采集的EEG数据进行最小化预处理后分类。我们的模型在区分三种转向意图时达到83.7%的准确率，证明了CNN有效处理原始EEG数据的能力。右转片段的分类准确率最高，暗示大脑活动可能存在空间偏侧性。本研究为开发更直观的脑-车通信系统奠定了基础。