An Asynchronous and Non-Invasive Brain-Actuated Wheelchair

The possibility to act upon the surrounding environment without using our human nervous system’s efferent pathways enables a new interaction modality that can boost and speed up the human sensor-effector loop. In recent years, brain-computer interface (BCI) research is exploring many applications in different fields: communication, environmental control, robotics and mobility, and neuroprosthetics [1] [2] [3] [4] [5] [6] [7]. Our work in the MAIA project is focused on developing asynchronous and non-invasive BCI to control robots and wheelchairs [7] [8]. It means that the users control such devices spontaneously and at their own paced, by learning to voluntary control specific electroencephalogram (EEG) features measured from the scalp. To this end, the users learn how to voluntary modulate different oscillatory rhythms by execution of different mental tasks (motor and cognitive). To facilitate this learning process, machine learning techniques are utilized, both to find those subject-specific EEG features that maximize the separability between the patterns generated by executing the mental tasks [9], and to train classifiers that minimize the classification error rates of these subject-specific patterns [7]. Finally, to assist the control task, different levels of intelligence are implemented in the device jointly with shared control techniques between the two interacting agents, the BCI system and the intelligent device [10] [11]. One of the main challenges of a non-invasive BCI based on spontaneous brain activity is the non-stationary nature of the EEG signals. Shenoy and co-workers [12] describe two sources of non-stationarity, namely differences