Brain Computer Interfaces could be useful in rehabilitation of movement, perhaps also for gait. Until recently, research on movement related brain signals has not included measuring electroencephalography (EEG) during walking, because of the potential artifacts. We investigated if it is possible to measure the event Related Desynchronization (ERD) and event related spectral perturbations (ERSP) during walking. Six subjects walked on a treadmill with a slow speed, while EEG, electromyography (EMG) of the neck muscles and step cycle were measured. A Canonical Correlation Analysis (CCA) was used to remove EMG artifacts from the EEG signals. It was shown that this method correctly deleted EMG components. A strong ERD in the mu band and a somewhat less strong ERD in the beta band were found during walking compared to a baseline period. Furthermore, lateralized ERSPs were found, depending on the phase in the step cycle. It is concluded that this is a promising method to use in BCI research on walking. These results therefore pave the way for using brain signals related to walking in a BCI context.

In this study, a tactile speller was developed and compared with existing visual speller paradigms in terms of classification performance and elicited event-related potentials (ERPs). The fingertips of healthy participants were stimulated with short mechanical taps while electroencephalographic activity was measured. The letters of the alphabet were allocated to different fingers and subjects could select one of the fingers by silently counting the number of taps on that finger. The offline and online performance of the tactile speller was compared to the overt and covert attention visual matrix speller and the covert attention Hex-o-Spell speller. For the tactile speller, binary target versus non-target classification accuracy was 67% on average. Classification and decoding accuracies of the tactile speller were lower than the overt matrix speller, but higher than the covert matrix speller, and similar to Hex-o-Spell. The average maximum information transfer rate of the tactile speller was 7.8 bits min(-1) (1.51 char min(-1)), with the best subject reaching a bit-rate of 27 bits min(-1) (5.22 char min(-1)). An increased amplitude of the P300 ERP component was found in response to attended stimuli versus unattended stimuli in all speller types. In addition, the tactile and overt matrix spellers also used the N2 component for discriminating between targets and non-targets. Overall, this study shows that it is possible to use a tactile speller for communication. The tactile speller provides a useful alternative to the visual speller, especially for people whose eye gaze is impaired.

Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.