Pseudo-Online BMI Based on EEG to Detect the Appearance of Sudden Obstacles during Walking

The aim of this paper is to describe new methods for detecting the appearance of unexpected obstacles during normal gait from EEG signals, improving the accuracy and reducing the false positive rate obtained in previous studies. This way, an exoskeleton for rehabilitation or assistance of people with motor limitations commanded by a Brain-Machine Interface (BMI) could be stopped in case that an obstacle suddenly appears during walking. The EEG data of nine healthy subjects were collected during their normal gait while an obstacle appearance was simulated by the projection of a laser line in a random pattern. Different approaches were considered for selecting the parameters of the BMI: subsets of electrodes, time windows and classifier probabilities, which were based on a linear discriminant analysis (LDA). The pseudo-online results of the BMI for detecting the appearance of obstacles, with an average percentage of 63.9% of accuracy and 2.6 false positives per minute, showed a significant improvement over previous studiesThis research has been carried out in the framework of the project Walk—Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (RTI2018-096677-B-I00), funded by the Spanish Ministry of Science, Innovation and Universities, the Spanish State Agency of Research, and the European Union through the European Regional Development Fund

EEG model stability and online decoding of attentional demand during gait using gamma band features

Rehabilitation therapies are evolving oriented to improve their performances in terms of functional recovery. To achieve such recovery, the patients’ involvement is an important factor that correlates with the plastic properties of the brain. By evaluating electroencephalographic signals, it is possible to modify, in real time, the parameters of the rehabilitation according to the patients’ cognitive state. In this paper, an online brain–machine interface to measure the attention level during gait is presented. The system is based on the measurement of selective attention mechanisms manifested as power synchronization and desynchronization in the gamma band. A Linear Discriminant Analysis classifier is used to provide an attention index between 0 and 1 in real time. Robust techniques for artifact rejection and signal standardization are used in order to deal with the problems associated to the measurement of cortical signals during walking. The final interface is validated with 4 incomplete Spinal Cord Injury patients and 4 healthy participants. The system shows an average success rate of 68.1% in the classification of 3 attention levels and a stable behavior of these results during timeThis research has been funded by the Commission of the European Union under the BioMot project – Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT- 2013-10-611695)and by the Spanish Ministry of Science, Innovation and Universities, the Spanish State Agency of Research, and the Commission of the European Union through the European Regional Development Fund. under the Walk project - Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (Grant Agreement number RTI2018-096677-B-I00)