About the cortical origin of the low-delta and high-gamma rhythms observed in EEG signals during treadmill walking.

This paper presents a spectral and time-frequency analysis of EEG signals recorded on seven healthy subjects walking on a treadmill at three different speeds. An accelerometer was placed on the head of the subjects in order to record the shocks undergone by the EEG electrodes during walking. Our results indicate that up to 15 harmonics of the fundamental stepping frequency may pollute EEG signals, depending on the walking speed and also on the electrode location.

Performance of the Emotiv Epoc headset for P300-based applications.

Background: For two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people.

Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al.

A subjective assessment of a P300 BCI system for lower-limb rehabilitation purposes.

Recent research has shown that a P300 system can be used while walking without requiring any specific gait-related artifact removal techniques. Also, standard EEG-based Brain-Computer Interfaces (BCI) have not been really assessed for lower limb rehabilitation/prosthesis. Therefore, this paper gives a first baseline estimation (for future BCI comparisons) of the subjective and objective performances of a four-state P300 BCI plus a non-control state for lower-limb rehabilitation purposes.

Towards effective non-invasive brain-computer interfaces dedicated to gait rehabilitation systems.

In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury.

An active foot lifter orthosis based on a PCPG algorithm.

Central pattern generators (CPGs) are known to play an important role in the generation of rhythmic movements in gait, both in animals and humans. The comprehension of their underlying mechanism has led to the development of an important family of algorithms at the basis of autonomous walking robots. Recently, it has been shown that human gait could be modeled using a subclass of those algorithms, namely a Programmable Central Pattern Generator (PCPG).