Eeg Artifact Removal For Improved Automated Lane Change Detection While Driving

In this study, we are interested in brain-machine interfaces that extract movement related cortical potentials (MRCP) from an electroencephalogram (EEG) recorded while driving and use this information to classify/detect when a driver intends to do left or right lane change. Collecting EEGs while driving, however, is a challenging task as it introduces numerous artifacts, including head movements (e.g., to look at side mirrors, etc), eye movements, and whole-body movements. Such artifacts can dominate the signal, thus hampering MRCP and lane change intent detection. Here, we explore three EEG artifact removal algorithms tailored for MRCP detection while driving, namely: headset accelerometer-based independent component analysis (acc-ICA), constrained ICA (cICA) and empirical mode decomposition (EMD). Next, we propose to use a recurrent neural network (RNN) reservoir classifier for lane change intent detection and compare results with a conventional support vector machine (SVM) based classifiers. Lastly, we explore the effect of EEG window analysis size on artifact removal and classification performance, thus gauging how close to real-time enhancement can be achieved and with what delay can lane change intent be detected. When looking at averaged trial performance, all system combinations achieved reliable accuracy. On the other hand, for single-trial accuracy, only acc-ICA and EMD methods performed well. Overall, average classification accuracy of 54.25% was achieved with SVMs, whereas accuracies of 82.88%, 82.76% and 82.69% were achieved with the RNN using acc-ICA, cICA and EMD artifact removal algorithms, respectively. Window sizes of 4 seconds prior to lane changes achieved the best result and only six EEG electrodes were required.