Sensorimotor cortical activity during human gait : forward versus backward walking

In previous studies, cortical activity was found as related to the gait cycle, and these modulations seem to vary according to the level of automaticity of the movement. In this study, we aimed to distinguish whether these modulations were due to sensor or to motor processing using EEG, and whether or not they can be used to distinguish forward versus backward walking. Four conditions were compared in a within subjects experiment: forward walking, backward walking, imaginary forward walking and imaginary backward walking. Results shown that EEG measurements are coupled with the gait cycle, however, differences between sensor and motor processing could not be distinguished. Further analysis should be done in order to remove movement artifacts and to answer the question of interest. Therefore, two ICA approaches were proposed. Finally, observed modulations are promising features for a BCI for walking, especially in combination with tasks which maintain the user engaged. Such BCIs could help in the rehabilitation of stroke and spinal cord injury patients. Introduction Motor impairment and functional disability are the major consequences of Spinal Cord Injury (SCI) and stroke. SCI patients suffer from damage in the spinal cord, which obstructs the communication between the brain and several parts of the body, depending on the level of the injury. On the other hand, a stroke, which is also known as CVA (Cerebrovascular Accident), is related to a distortion of the capacity of the brain to process neural information after a failure of blood supply. Even though both problems present consequences related to motor impairments, the brain functionality in patients with SCI is assumed to be intact and capable of driving limb movements (Cramer, Orr, Cohen, & Lacourse, 2007; Enzinger et al., 2008), while in a stroke, the motor impairment is caused by brain damage (de Vries & Mulder, 2007). Therefore, rehabilitation strategies for both impairments follow different paths. Rehabilitation after a stroke is mainly based on therapy and practice. In this case, functional recovery is attributed to processes of reorganization and substitution in the damaged brain. For SCI, rehabilitation could follow two approaches. Firstly, repair of the damaged spinal cord and the use of cortical information to drive muscles, bypassing the area of damage. Secondly, Brain-Computer Interfaces, or BCI, allow the use of neurophysiological signals of the brain to control external devices (Birbaumer & Cohen, 2007), and could therefore be used to achieve the goal of bypassing damaged areas in the spinal cord and drive prosthesis or even the patients’ muscles. Even if the damage which caused the impairment is not in the brain itself, some abnormalities have been described in the brain motor functions of SCI patients, including reduced activation, abnormal activation patterns and higher thresholds and latency for motor evoked potentials (Cramer et al., 2007; Enzinger et al., 2008). These abnormalities suggest that indeed the use or disuse of a brain functionality can influence cortical representations. In other words, reorganization of neural structures takes place as result of either depravation or increase of sensory input due to movements. Hence, constant training is required in order to maintain the brain’s motor system functionalities in good shape. Unfortunately, for an injured patient it is not always possible to practice the required movements, either because they cannot move or pain is involved with the movement. There is some evidence that imaginary movements might be helpful by providing means of practice when no actual movement can be performed (Mulder, 2007). Motor imagery refers to the cognitive image of performing a movement, without executing the actual movement. In fact, motor imagery can be though as a tool which allows subjects to manipulate their movement representations, as internal models of actions (de Lange, Hagoort, & Toni, 2005). Imaginary movements are embodied and they can activate sensory-motor areas in the brain (Mulder, 2007). Indeed, research over the neural topography of movement representations has used imaginary movements to investigate how the brain deals with immediate action independently of movement execution: first, it combines somatosensory and visuomotor information (i.e. feedback) in the posterior parietal cortex; second, it generates an actual motor plan in the dorsal premotor cortex, and finally, it deals with movement execution in the motor cortex (de Lange et al., 2005). Motor imagery training could be enhanced by providing the patient with feedback of the movement. There are several types of feedback, including proprioceptive and visual. In stroke physiotherapy, the patient is helped by a therapist to move the paralyzed limbs; therefore enhancing proprioceptive feedback. Another example from therapy are devices such as the Lokomat System (Hocoma Inc.), which have been designed to help injured patients to simulate the movement of walking with slightly better results than traditional therapy (Mayr et al., 2007). The simulation is achieved by using a treadmill with weight support and motorized boots. Visual feedback can also be provided by the Lokomat Pro. This system shows visual cues of walking on a screen with a virtual environment, where the patient is supposed to walk and complete some tasks. This augmented feedback enhances the patient active participation in the task. Therefore, a BCI using imaginary movements could help in rehabilitation, not only by providing the means of bypassing spinal cord injuries, but also to help both, SCI and stroke, patients to maintain and recover their brain functionalities by giving direct feedback of the imagined movements. Despite the potential of BCI and its possible benefits in rehabilitation, there are few studies that have explored its feasibility. One example is the study by Prasad, Herman, Coyle, McDonough, & Crosbie, (2010), where a game-based neurofeedback BCI was used to help movement recovery in stroke patients. The lack of proof is mainly due to the difficulty to identify “signatures” in the brain signal. Signatures are the association of a brain signal with a mental state that uniquely caused it (van Gerven et al., 2009). EEG potentials are one example of neurophysiological signals that can be used to identify patterns useful to build a BCI. For motor imagery purposes, the mu-rhythm and beta-band are of special interest. Mu-rhythm refers to an oscillation of about 8-14 Hz in the EEG signal which cannot only be seen with movement, but also if one imagines a movement (Pfurtscheller, Stancák, & Neuper, 1996; Prasad et al., 2010). Central mu rhythms coincide with the alpha band (10Hz). An event-related desynchronization (ERD) in the mu-rhythm frequencies in humans has been linked to somatosensory stimulation or movement. Evidence for this has been found especially for hand movements. However, mu-band ERDs for foot movements is more difficult to detect because the foot area is located within the mesial wall in the interhemispheric fisure. Beta band is also generated in the somatosensory cortex, but differently to mu rhythms, it is easier to detect, and time courses of recovery after desynchronization is faster in beta than in mu rhythms (Pfurtscheller et al., 1996). The present research focuses on walking. The ultimate goal would be to provide feedback about imaginary walking movements, using a BCI. This could be applied in rehabilitation to improve the process of learning to walk again. Therefore, the identification of the correlation between patterns of movements during the human gait cycle and EEG potentials is crucial to develop such brain-computer interfaces. A recent study by Gwin, Gramann, Makeig, & Ferris (2011), showed cortical involvement in EEG signals during human gait; they found intra-stride changes of activity in different brain areas, including the anterior cingulate, prefrontal cortex, posterior parietal cortex, and sensorimotor cortex. Peaks in the beta and alpha bands spectral power were found in the heel-strike and approximately in the middle of the double support phases, respectively. The synchronizations between the gait cycle and the spectral power of the beta band were confirmed by a previous study (Peters, 2011). In this study, participants had to walk at different speeds, and also included stepping in place. It was shown that the intensity of activity is greater in heel-strike and toe-off phases of the gain cycle, and also that uncommon walking activity such as stepping in place and walking fast involves stronger modulations of cortical activity. This might suggest that the cortical activity depends on experience and familiarity with the movements (Peters, 2011). From these results, the question arises on whether the found differences in cortical activity are caused by differences in the motor activity or changes in the sensory perceptions due to different movements. Indeed, motor and sensor processing in the brain are highly interrelated, and sensory input can affect motor representations. Some research has suggested that action observation (sensory input) automatically triggers action stimulation and by this, action observation facilitates action execution (Mulder, 2007). It may also happen that the feedback connections between both sensory and motor components are so strong that they are indistinguishable and re-defined upon each other in a complementation loop (Duch, Bekkering, & Neggers, n.d.). A typical example is the discovery of mirror neurons, which fire not only while performing a movement, but also when someone else is seen executing a movement (Mulder, 2007; Rizzolatti & Craighero, 2004). Therefore, it is important to consider the looping interaction between sensory feedback and motor control, in order to distinguish one from another. Specifically for human gait, it has also been stated that the control of movement includes both