The acquisition of Human EEG Data during Self-Motion on a Stewart Platform

Purpose of the study: The human sense of movement and balance integrates vestibular information with visual and somatosensory information. Research into the neural correlates of vestibular processing has been hampered because the subject must remain in a fixed position, and consequently neither magnetic resonance imaging nor positron emitted tomography are suitable methods. Therefore, research on the neural correlates of linear self-motion in humans has typically used visual vection, which is self-motion induced by large-field visual motion stimulation during which the stationary subject perceives the moving visual surroundings as being stable and themselves as moving. This study investigated the feasibility of acquiring electroencephalography (EEG) data during self-motion in human subjects. Electroencephalography would appear to be a suitable candidate for recording neural activity during motion because modern EEG acquisition equipment is lightweight and portable. Furthermore, a Stewart motion platform provides an appropriate method of producing linear self-motion in a laboratory environment. There are, however, a number of potential drawbacks to using a Stewart motion platform in conjunction with EEG recording equipment. For example, noise could be introduced into the EEG signal from the motion of the actuators, the electrical noise of the platform power source, or from muscular activity of the subject as they compensate for the acceleration at the start and finish of the motion. If achievable, this would allow a new method for investigation of vestibular processing and multisensory interaction. Understanding the contribution of different sensory modalities to the human senses of balance and movement is a key task for aiding the elderly, as the processing of vestibular signals deteriorates over time, which can lead to postural instability and falls. Method: Six subjects performed a visual oddball task – designed to evoke a P3 event-related potential (ERP) – under four different motion conditions on a Stewart platform. The motion conditions – stationary, idle, slow and fast – were designed to ascertain if the Stewart platform produced electromagnetic noise which would mask EEG data. The P3 task was chosen as it is relatively simple to evoke and can be tested using various experimental paradigms and sensory modalities, making it a robust measure. The P3 task required the subject to respond when observing an infrequent visual stimulus. The visual stimuli were projected onto a large screen in the Stewart platform. EEG data were recorded using both a shielded system, the BrainAmp MRPlus, and separately using an unshielded Biosemi ActiveTwo system. Results: Reliable P3 ERPs were found to be present under all motion conditions. The correlation among conditions during the interval -100ms to 600ms of the ERPs was at least 0.93. There were no artifacts caused by interference from the Stewart platform from both the shielded and unshielded systems. The number of rejected epochs was similar across all conditions. Conclusion: The results of this study indicate that reliable EEG data can be obtained during self-motion on a Stewart platform, and that the task-independent vestibular input did not interfere with the visually-evoked P3 ERP. The Stewart platform did not introduce noise to the data. This finding is noteworthy for the ecological validity of further research into human motion