EEG-Driven Detection of Motion Mismatch in Upper-Limb Movements

In recent years, research and development of human-assist robots equipped with brain-computer interfaces (BCIs) have accelerated due to the increasing number of stroke cases worldwide, which are a major contributor to permanent disabilities and loss of independence. BCIs can be an effective neurorehabilitation tool, as they assist the impaired sensorimotor loop by providing compensatory somatosensory feedback during motor attempts. The human-assist robot must support the user's movements based on their intended actions. However, determining whether the robot's assist motion is as intended by the user in real time remains challenging. This paper proposes a multi-stage method that utilizes time-frequency and statistical analyses for electroencephalography (EEG) signals to detect whether the arm movements are as intended. Furthermore, it employs a binary classifier with threshold optimization - guided by the highest classification accuracy - to determine the most effective threshold for distinguishing between intentional and unintentional movements. The effectiveness of the proposed method was evaluated through experiments involving multiple arm movements that combine both simple and complex tasks. A disturbance mechanism device was built and employed during these experiments to generate the sense of motion mismatch by applying disturbance forces. The experimental results demonstrate the effectiveness of the proposed method in detecting motion mismatch and distinguishing between intentional and unintentional movements. The proposed method is applicable to human-assist robots to assess whether the robot's assist movement matches the user's intended motion.