Machine Learning Algorithms for exoskeleton control using Brain Computer Interfaces
A Brain-Computer Interface (BCI) system allows the brain to communicate with an external device. A BCI consists of signal acquisition, feature extraction, feature translation, and device output. BCI systems have been a great help in the medical field for assistance to patients suffering from neuromotor disorders, spinal cord injuries and trauma to the nervous systems, with applications including wheelchairs and exoskeletons. Brain activity may be recorded through invasive, semi-invasive or noninvasive systems, detecting electrical or optical signals from the brain related to brain activity. Once the raw signals are collected from these systems, it is important to analyze and translate these signals to control an interfacing device in real-time. Hence, a robust framework is a requirement for any BCI system. Machine learning/ Deep Learning algorithms are now used for the processing of these signals and later translating them into action. The algorithms for BCI control need to be trained and tested. Advancement in the BCI systems has developed from moving a pointer cursor to operating a wheelchair using the brain commands. Various neuronal potentials may be used to control such commands, for instance, SSVEP, P300 and motor imagery signals. This study aims to evaluate the EEG signals from a non-invasive cap and translating these for a lower limb exoskeleton allowing dynamic balance without the use of crutches while walking. One of the major challenges faced in this field is the time investment for training purposes. The research will apply qualitative and quantitative analysis to evaluate the present state-of-art in BCI systems to operate exoskeletons.The goal is to design a BCI framework having minimal training and evaluation of various neuronal potentials that would decrease the training time for exoskeleton addressing the following possible research questions: Q1. Which neuronal potentials have the highest accuracy, efficiency and require less training? Q2. Do hybrid systems perform better as compared to traditional systems for applications like exoskeletons? Q3. Which algorithms hold higher accuracy and could be operated in real-time to minimize the training. Following on from these questions a framework will be generated for the advancement in locomotion for exoskeletons.