Using Actual and Imagined Walking Related Desynchronization Features in a BCI

Abstract

Recently, brain-computer interface (BCI) research has extended to investigate its possible use in motor rehabilitation. Most of these investigations have focused on the upper body. Only few studies consider gait because of the difficulty of recording EEG during gross movements. However, for stroke patients the rehabilitation of gait is of crucial importance. Therefore, this study investigates if a BCI can be based on walking related desynchronization features. Furthermore, the influence of complexity of the walking movements on the classification performance is investigated. Two BCI experiments were conducted in which healthy subjects performed a cued walking task, a more complex walking task (backward or adaptive walking), and imagination of the same tasks. EEG data during these tasks was classified into walking and no-walking. The results from both experiments show that despite the automaticity of walking and recording difficulties, brain signals related to walking could be classified rapidly and reliably. Classification performance was higher for actual walking movements than for imagined walking movements. There was no significant increase in classification performance for both the backward and adaptive walking tasks compared with the cued walking tasks. These results are promising for developing a BCI for the rehabilitation of gait.

Brain Functional Connectivity in Unconstrained Walking With and Without an Exoskeleton

“…These results demonstrated that the alpha and beta bands were equivalently involving in the walking in terms of inter-regional connectivity. The equivalent contribution to the walking was roughly in agreement with the observation that power spectral densities (PSD) of these two bands were concurrently changed relevant to walking [11], [28], [10] as well as the movements of upper limbs [45]. This might be due to that the alpha and beta bands are modulated to accomplish the largely overlapping part of the motor function.…”

“…Similar to the neural mechanism of contra-lateral control from the brain to upper limbs, lower limbs are controlled by contralateral brain hemispheres although the regions controlling respective lower limbs are closer to the midline of the brain [9]. According to the previous investigations, we have known that power spectral densities (PSD) extracted from the EEG recordings under different walking conditions were different [10], [11], [12], hemispherical asymmetry in the PSD was differential between the walking condition with an exoskeleton and the walking condition without an exoskeleton [13], the PSD was suppressed during walking compared to the upright standing [14], and the correlative relationship between brain activity and muscular contraction of the lower limb [15] and so forth. These differences among walking conditions have been utilized as features, based on which different walking conditions were successfully classified [10], [16], [17].…”

“…Therefore, we, in this study, designed a walking experiment in the unconstrained situation and attempted to explore different walking conditions with and without an exoskeleton from both individual connections and graph metrics for LOFC, HOFC, and associated HOFC (aHOFC). According to the published literature [16], [11], [28], [10], alpha band and beta band were dominantly involved in walking. Hence, our investigation was focused on these two individual bands as well as the incorporative frequency band of alpha and beta bands (i.e.…”

Brain-Machine Interfaces for Assistance and Rehabilitation of People with Reduced Mobility

“…Currently, multiple studies have been conducted focused on the development of this type of rehabilitation systems [18][19][20]. In this regard, brain-machine interfaces (BMIs) can lead to a significant improvement in rehabilitation systems for patients who have suffered a stroke, making them an active part of their own rehabilitation process and thus achieving not only a rehabilitation at muscle level but also an improvement at neurological level, since it is the patients themselves who generate the control commands of the system [21].…”

Analysis of Feature Extraction Algorithms Used in Brain-Computer Interfaces

“…(2) Event-Related Synchronizations or Desynchronizations (ERS/ERD). Studies have shown that the single side of the body movement or imaginary movement can generate ERD in contralateral brain and ERS in ipsilateral brain [3]. (3) Slow Cortical Potential (SCP).…”